7 WDI
7.1 Reviews and Previews
- We have used
tidyverseandgapminderalready. - If you have not installed
WDI, install it. -
mapsandreadxlare bundled intidyversebut need to be attached bylibrary.
7.1.1 Gapminder Package Data
df <- gapminder
df
#> # A tibble: 1,704 × 6
#> country continent year lifeExp pop gdpPercap
#> <fct> <fct> <int> <dbl> <int> <dbl>
#> 1 Afghanistan Asia 1952 28.8 8425333 779.
#> 2 Afghanistan Asia 1957 30.3 9240934 821.
#> 3 Afghanistan Asia 1962 32.0 10267083 853.
#> 4 Afghanistan Asia 1967 34.0 11537966 836.
#> 5 Afghanistan Asia 1972 36.1 13079460 740.
#> 6 Afghanistan Asia 1977 38.4 14880372 786.
#> 7 Afghanistan Asia 1982 39.9 12881816 978.
#> 8 Afghanistan Asia 1987 40.8 13867957 852.
#> 9 Afghanistan Asia 1992 41.7 16317921 649.
#> 10 Afghanistan Asia 1997 41.8 22227415 635.
#> # ℹ 1,694 more rows
7.1.2 gdpPercap of ASEAN countries
asean <- c("Brunei", "Cambodia", "Laos", "Myanmar",
"Philippines", "Indonesia", "Malaysia", "Singapore")
df %>% filter(country %in% asean) %>%
ggplot(aes(x = year, y = gdpPercap, col = country)) + geom_line()
df %>% filter(country %in% asean) %>%
ggplot(aes(x = gdpPercap, y = lifeExp, col = country)) + geom_point()
df %>% filter(country %in% asean) %>%
ggplot(aes(x = gdpPercap, y = lifeExp, col = country)) +
geom_point() + coord_trans(x = "log10", y = "identity")
\(\log_{10}{100}\) = 2, \(\log_{10}{1000}\) = 3, \(\log_{10}{10000}\) = 4
df2007 <- df %>% filter(country %in% asean, year == 2007)
df %>% filter(country %in% asean) %>%
ggplot(aes(x = gdpPercap, y = lifeExp, col = country))+
geom_line() + geom_label(data = df2007, aes(label = country), position = position_jitter()) + geom_point() +
coord_trans(x = "log10", y = "identity") +
theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1), legend.position = "none") +
labs(title = "Life Expectancy vs GDP Per Capita of ASEAN Countries",
subtitle = "Data: gapminder package", x = "GDP per Capita", y = "Life Expectancy")
The gapminder is a handy package to look at a worldview using three indicators, i.e., life expectancy, population, and GDP per capita. However, some countries are missing, and the data source is unclear.
7.1.3 World Bank: World Development Indicators (WDI)
- SP.DYN.LE00.IN: Life expectancy at birth, total (years)
- NY.GDP.PCAP.KD: GDP per capita (constant 2015 US$)
- SP.POP.TOTL: Population, total
df_wdi <- WDI(
country = "all",
indicator = c(lifeExp = "SP.DYN.LE00.IN", pop = "SP.POP.TOTL", gdpPercap = "NY.GDP.PCAP.KD")
)
df_wdi
#> # A tibble: 16,492 × 7
#> country iso2c iso3c year lifeExp pop gdpPercap
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Afghanistan AF AFG 1960 32.5 8622466 NA
#> 2 Afghanistan AF AFG 1961 33.1 8790140 NA
#> 3 Afghanistan AF AFG 1962 33.5 8969047 NA
#> 4 Afghanistan AF AFG 1963 34.0 9157465 NA
#> 5 Afghanistan AF AFG 1964 34.5 9355514 NA
#> 6 Afghanistan AF AFG 1965 35.0 9565147 NA
#> 7 Afghanistan AF AFG 1966 35.5 9783147 NA
#> 8 Afghanistan AF AFG 1967 35.9 10010030 NA
#> 9 Afghanistan AF AFG 1968 36.4 10247780 NA
#> 10 Afghanistan AF AFG 1969 36.9 10494489 NA
#> # ℹ 16,482 more rows
df_wdi_extra <- WDI(
country = "all",
indicator = c(lifeExp = "SP.DYN.LE00.IN", pop = "SP.POP.TOTL", gdpPercap = "NY.GDP.PCAP.KD"),
extra = TRUE
)
df_wdi_extra
#> # A tibble: 16,492 × 15
#> country iso2c iso3c year status lastupdated lifeExp
#> <chr> <chr> <chr> <dbl> <lgl> <date> <dbl>
#> 1 Afghanistan AF AFG 1993 NA 2022-12-22 51.5
#> 2 Afghanistan AF AFG 1997 NA 2022-12-22 53.6
#> 3 Afghanistan AF AFG 1994 NA 2022-12-22 51.5
#> 4 Afghanistan AF AFG 1995 NA 2022-12-22 52.5
#> 5 Afghanistan AF AFG 2001 NA 2022-12-22 55.8
#> 6 Afghanistan AF AFG 1998 NA 2022-12-22 52.9
#> 7 Afghanistan AF AFG 1999 NA 2022-12-22 54.8
#> 8 Afghanistan AF AFG 2007 NA 2022-12-22 59.1
#> 9 Afghanistan AF AFG 2008 NA 2022-12-22 59.9
#> 10 Afghanistan AF AFG 1980 NA 2022-12-22 39.6
#> # ℹ 16,482 more rows
#> # ℹ 8 more variables: pop <dbl>, gdpPercap <dbl>,
#> # region <chr>, capital <chr>, longitude <dbl>,
#> # latitude <dbl>, income <chr>, lending <chr>7.2 Exploratory Data Analysis
7.2.1 What is EDA (Posit Primers: Visualise Data)
EDA is an iterative cycle that helps you understand what your data says. When you do EDA, you:
Generate questions about your data
Search for answers by visualising, transforming, and/or modeling your data
Use what you learn to refine your questions and/or generate new questions
EDA is an important part of any data analysis. You can use EDA to make discoveries about the world; or you can use EDA to ensure the quality of your data, asking questions about whether the data meets your standards or not.
7.3 Open and Public Data, World Bank
7.3.1 Open Government Data Toolkit: Open Data Defined
The term Open Data has a very precise meaning. Data or content is open if anyone is free to use, re-use or redistribute it, subject at most to measures that preserve provenance and openness.
- The data must be legally open, which means they must be placed in the public domain or under liberal terms of use with minimal restrictions.
- The data must be technically open, which means they must be published in electronic formats that are machine readable and non-proprietary, so that anyone can access and use the data using common, freely available software tools. Data must also be publicly available and accessible on a public server, without password or firewall restrictions. To make Open Data easier to find, most organizations create and manage Open Data catalogs.
7.4 World Bank: WDI - World Development Indicaters
- World Bank: https://www.worldbank.org
-
Who we are:
- To end extreme poverty: By reducing the share of the global population that lives in extreme poverty to 3 percent by 2030.
- To promote shared prosperity: By increasing the incomes of the poorest 40 percent of people in every country.
- World Bank Open Data: https://data.worldbank.org
- Data Bank, World Development Indicators, etc.
-
World Development Indicators (WDI) : the World Bank’s premier compilation of cross-country comparable data on development; 1400 time series indicators
- Themes: Poverty and Inequality, People, Environment, Economy, States and Markets, Global Links
- Open Data & DataBank: Explore data, Query database
- Bulk Download: Excel, CSV
- API Documentation
7.5 R Package WDI
- WDI: World Development Indicators and Other World Bank Data
- Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators (‘WDI’), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.
- Version: 2.7.4
- Materials: README - usage
- NEWS - version history
- Published: 2021-04-06
- README: https://cran.r-project.org/web/packages/WDI/readme/README.html
- Reference manual: WDI.pdf
7.6 Function WDI
- Usage
WDI(country = "all",
indicator = "NY.GDP.PCAP.KD",
start = 1960,
end = 2020,
extra = FALSE,
cache = NULL)-
Arguments See Help!
- country: Vector of countries (ISO-2 character codes, e.g. “BR”, “US”, “CA”, or “all”)
- indicator: If you supply a named vector, the indicators will be automatically renamed:
c('women_private_sector' = 'BI.PWK.PRVS.FE.ZS')
7.7 Function WDIsearch
WDIsearch(string = "NY.GDP.PCAP.KD",
field = "indicator", cache = NULL)
#> indicator name
#> 11431 NY.GDP.PCAP.KD GDP per capita (constant 2015 US$)
#> 11432 NY.GDP.PCAP.KD.ZG GDP per capita growth (annual %)
WDIsearch(string = "population",
field = "name", short=FALSE, cache = NULL)
#> indicator
#> 25 1.1_ACCESS.ELECTRICITY.TOT
#> 40 1.2_ACCESS.ELECTRICITY.RURAL
#> 41 1.3_ACCESS.ELECTRICITY.URBAN
#> 165 2.1_ACCESS.CFT.TOT
#> 199 3.11.01.01.popcen
#> 1173 allsa.cov_pop
#> 1176 allsi.cov_pop
#> 1179 allsp.cov_pop
#> 1197 BAR.NOED.1519.FE.ZS
#> 1198 BAR.NOED.1519.ZS
#> 1199 BAR.NOED.15UP.FE.ZS
#> 1200 BAR.NOED.15UP.ZS
#> 1201 BAR.NOED.2024.FE.ZS
#> 1202 BAR.NOED.2024.ZS
#> 1203 BAR.NOED.2529.FE.ZS
#> 1204 BAR.NOED.2529.ZS
#> 1205 BAR.NOED.25UP.FE.ZS
#> 1206 BAR.NOED.25UP.ZS
#> 1207 BAR.NOED.3034.FE.ZS
#> 1208 BAR.NOED.3034.ZS
#> 1209 BAR.NOED.3539.FE.ZS
#> 1210 BAR.NOED.3539.ZS
#> 1211 BAR.NOED.4044.FE.ZS
#> 1212 BAR.NOED.4044.ZS
#> 1213 BAR.NOED.4549.FE.ZS
#> 1214 BAR.NOED.4549.ZS
#> 1215 BAR.NOED.5054.FE.ZS
#> 1216 BAR.NOED.5054.ZS
#> 1217 BAR.NOED.5559.FE.ZS
#> 1218 BAR.NOED.5559.ZS
#> 1219 BAR.NOED.6064.FE.ZS
#> 1220 BAR.NOED.6064.ZS
#> 1221 BAR.NOED.6569.FE.ZS
#> 1222 BAR.NOED.6569.ZS
#> 1223 BAR.NOED.7074.FE.ZS
#> 1224 BAR.NOED.7074.ZS
#> 1225 BAR.NOED.75UP.FE.ZS
#> 1226 BAR.NOED.75UP.ZS
#> 1227 BAR.POP.1519
#> 1228 BAR.POP.1519.FE
#> 1229 BAR.POP.15UP
#> 1230 BAR.POP.15UP.FE
#> 1231 BAR.POP.2024
#> 1232 BAR.POP.2024.FE
#> 1233 BAR.POP.2529
#> 1234 BAR.POP.2529.FE
#> 1235 BAR.POP.25UP
#> 1236 BAR.POP.25UP.FE
#> 1237 BAR.POP.3034
#> 1238 BAR.POP.3034.FE
#> 1239 BAR.POP.3539
#> 1240 BAR.POP.3539.FE
#> 1241 BAR.POP.4044
#> 1242 BAR.POP.4044.FE
#> 1243 BAR.POP.4549
#> 1244 BAR.POP.4549.FE
#> 1245 BAR.POP.5054
#> 1246 BAR.POP.5054.FE
#> 1247 BAR.POP.5559
#> 1248 BAR.POP.5559.FE
#> 1249 BAR.POP.6064
#> 1250 BAR.POP.6064.FE
#> 1251 BAR.POP.6569
#> 1252 BAR.POP.6569.FE
#> 1253 BAR.POP.7074
#> 1254 BAR.POP.7074.FE
#> 1255 BAR.POP.75UP
#> 1256 BAR.POP.75UP.FE
#> 1257 BAR.PRM.CMPT.1519.FE.ZS
#> 1258 BAR.PRM.CMPT.1519.ZS
#> 1259 BAR.PRM.CMPT.15UP.FE.ZS
#> 1260 BAR.PRM.CMPT.15UP.ZS
#> 1261 BAR.PRM.CMPT.2024.FE.ZS
#> 1262 BAR.PRM.CMPT.2024.ZS
#> 1263 BAR.PRM.CMPT.2529.FE.ZS
#> 1264 BAR.PRM.CMPT.2529.ZS
#> 1265 BAR.PRM.CMPT.25UP.FE.ZS
#> 1266 BAR.PRM.CMPT.25UP.ZS
#> 1267 BAR.PRM.CMPT.3034.FE.ZS
#> 1268 BAR.PRM.CMPT.3034.ZS
#> 1269 BAR.PRM.CMPT.3539.FE.ZS
#> 1270 BAR.PRM.CMPT.3539.ZS
#> 1271 BAR.PRM.CMPT.4044.FE.ZS
#> 1272 BAR.PRM.CMPT.4044.ZS
#> 1273 BAR.PRM.CMPT.4549.FE.ZS
#> 1274 BAR.PRM.CMPT.4549.ZS
#> 1275 BAR.PRM.CMPT.5054.FE.ZS
#> 1276 BAR.PRM.CMPT.5054.ZS
#> 1277 BAR.PRM.CMPT.5559.FE.ZS
#> 1278 BAR.PRM.CMPT.5559.ZS
#> 1279 BAR.PRM.CMPT.6064.FE.ZS
#> 1280 BAR.PRM.CMPT.6064.ZS
#> 1281 BAR.PRM.CMPT.6569.FE.ZS
#> 1282 BAR.PRM.CMPT.6569.ZS
#> 1283 BAR.PRM.CMPT.7074.FE.ZS
#> 1284 BAR.PRM.CMPT.7074.ZS
#> 1285 BAR.PRM.CMPT.75UP.FE.ZS
#> 1286 BAR.PRM.CMPT.75UP.ZS
#> 1287 BAR.PRM.ICMP.1519.FE.ZS
#> 1288 BAR.PRM.ICMP.1519.ZS
#> 1289 BAR.PRM.ICMP.15UP.FE.ZS
#> 1290 BAR.PRM.ICMP.15UP.ZS
#> 1291 BAR.PRM.ICMP.2024.FE.ZS
#> 1292 BAR.PRM.ICMP.2024.ZS
#> 1293 BAR.PRM.ICMP.2529.FE.ZS
#> 1294 BAR.PRM.ICMP.2529.ZS
#> 1295 BAR.PRM.ICMP.25UP.FE.ZS
#> 1296 BAR.PRM.ICMP.25UP.ZS
#> 1297 BAR.PRM.ICMP.3034.FE.ZS
#> 1298 BAR.PRM.ICMP.3034.ZS
#> 1299 BAR.PRM.ICMP.3539.FE.ZS
#> 1300 BAR.PRM.ICMP.3539.ZS
#> 1301 BAR.PRM.ICMP.4044.FE.ZS
#> 1302 BAR.PRM.ICMP.4044.ZS
#> 1303 BAR.PRM.ICMP.4549.FE.ZS
#> 1304 BAR.PRM.ICMP.4549.ZS
#> 1305 BAR.PRM.ICMP.5054.FE.ZS
#> 1306 BAR.PRM.ICMP.5054.ZS
#> 1307 BAR.PRM.ICMP.5559.FE.ZS
#> 1308 BAR.PRM.ICMP.5559.ZS
#> 1309 BAR.PRM.ICMP.6064.FE.ZS
#> 1310 BAR.PRM.ICMP.6064.ZS
#> 1311 BAR.PRM.ICMP.6569.FE.ZS
#> 1312 BAR.PRM.ICMP.6569.ZS
#> 1313 BAR.PRM.ICMP.7074.FE.ZS
#> 1314 BAR.PRM.ICMP.7074.ZS
#> 1315 BAR.PRM.ICMP.75UP.FE.ZS
#> 1316 BAR.PRM.ICMP.75UP.ZS
#> 1377 BAR.SEC.CMPT.1519.FE.ZS
#> 1378 BAR.SEC.CMPT.1519.ZS
#> 1379 BAR.SEC.CMPT.15UP.FE.ZS
#> 1380 BAR.SEC.CMPT.15UP.ZS
#> 1381 BAR.SEC.CMPT.2024.FE.ZS
#> 1382 BAR.SEC.CMPT.2024.ZS
#> 1383 BAR.SEC.CMPT.2529.FE.ZS
#> 1384 BAR.SEC.CMPT.2529.ZS
#> 1385 BAR.SEC.CMPT.25UP.FE.ZS
#> 1386 BAR.SEC.CMPT.25UP.ZS
#> 1387 BAR.SEC.CMPT.3034.FE.ZS
#> 1388 BAR.SEC.CMPT.3034.ZS
#> 1389 BAR.SEC.CMPT.3539.FE.ZS
#> 1390 BAR.SEC.CMPT.3539.ZS
#> 1391 BAR.SEC.CMPT.4044.FE.ZS
#> 1392 BAR.SEC.CMPT.4044.ZS
#> 1393 BAR.SEC.CMPT.4549.FE.ZS
#> 1394 BAR.SEC.CMPT.4549.ZS
#> 1395 BAR.SEC.CMPT.5054.FE.ZS
#> 1396 BAR.SEC.CMPT.5054.ZS
#> 1397 BAR.SEC.CMPT.5559.FE.ZS
#> 1398 BAR.SEC.CMPT.5559.ZS
#> 1399 BAR.SEC.CMPT.6064.FE.ZS
#> 1400 BAR.SEC.CMPT.6064.ZS
#> 1401 BAR.SEC.CMPT.6569.FE.ZS
#> 1402 BAR.SEC.CMPT.6569.ZS
#> 1403 BAR.SEC.CMPT.7074.FE.ZS
#> 1404 BAR.SEC.CMPT.7074.ZS
#> 1405 BAR.SEC.CMPT.75UP.FE.ZS
#> 1406 BAR.SEC.CMPT.75UP.ZS
#> 1407 BAR.SEC.ICMP.1519.FE.ZS
#> 1408 BAR.SEC.ICMP.1519.ZS
#> 1409 BAR.SEC.ICMP.15UP.FE.ZS
#> 1410 BAR.SEC.ICMP.15UP.ZS
#> 1411 BAR.SEC.ICMP.2024.FE.ZS
#> 1412 BAR.SEC.ICMP.2024.ZS
#> 1413 BAR.SEC.ICMP.2529.FE.ZS
#> 1414 BAR.SEC.ICMP.2529.ZS
#> 1415 BAR.SEC.ICMP.25UP.FE.ZS
#> 1416 BAR.SEC.ICMP.25UP.ZS
#> 1417 BAR.SEC.ICMP.3034.FE.ZS
#> 1418 BAR.SEC.ICMP.3034.ZS
#> 1419 BAR.SEC.ICMP.3539.FE.ZS
#> 1420 BAR.SEC.ICMP.3539.ZS
#> 1421 BAR.SEC.ICMP.4044.FE.ZS
#> 1422 BAR.SEC.ICMP.4044.ZS
#> 1423 BAR.SEC.ICMP.4549.FE.ZS
#> 1424 BAR.SEC.ICMP.4549.ZS
#> 1425 BAR.SEC.ICMP.5054.FE.ZS
#> 1426 BAR.SEC.ICMP.5054.ZS
#> 1427 BAR.SEC.ICMP.5559.FE.ZS
#> 1428 BAR.SEC.ICMP.5559.ZS
#> 1429 BAR.SEC.ICMP.6064.FE.ZS
#> 1430 BAR.SEC.ICMP.6064.ZS
#> 1431 BAR.SEC.ICMP.6569.FE.ZS
#> 1432 BAR.SEC.ICMP.6569.ZS
#> 1433 BAR.SEC.ICMP.7074.FE.ZS
#> 1434 BAR.SEC.ICMP.7074.ZS
#> 1435 BAR.SEC.ICMP.75UP.FE.ZS
#> 1436 BAR.SEC.ICMP.75UP.ZS
#> 1467 BAR.TER.CMPT.1519.FE.ZS
#> 1468 BAR.TER.CMPT.1519.ZS
#> 1469 BAR.TER.CMPT.15UP.FE.ZS
#> 1470 BAR.TER.CMPT.15UP.ZS
#> 1471 BAR.TER.CMPT.2024.FE.ZS
#> 1472 BAR.TER.CMPT.2024.ZS
#> 1473 BAR.TER.CMPT.2529.FE.ZS
#> 1474 BAR.TER.CMPT.2529.ZS
#> 1475 BAR.TER.CMPT.25UP.FE.ZS
#> 1476 BAR.TER.CMPT.25UP.ZS
#> 1477 BAR.TER.CMPT.3034.FE.ZS
#> 1478 BAR.TER.CMPT.3034.ZS
#> 1479 BAR.TER.CMPT.3539.FE.ZS
#> 1480 BAR.TER.CMPT.3539.ZS
#> 1481 BAR.TER.CMPT.4044.FE.ZS
#> 1482 BAR.TER.CMPT.4044.ZS
#> 1483 BAR.TER.CMPT.4549.FE.ZS
#> 1484 BAR.TER.CMPT.4549.ZS
#> 1485 BAR.TER.CMPT.5054.FE.ZS
#> 1486 BAR.TER.CMPT.5054.ZS
#> 1487 BAR.TER.CMPT.5559.FE.ZS
#> 1488 BAR.TER.CMPT.5559.ZS
#> 1489 BAR.TER.CMPT.6064.FE.ZS
#> 1490 BAR.TER.CMPT.6064.ZS
#> 1491 BAR.TER.CMPT.6569.FE.ZS
#> 1492 BAR.TER.CMPT.6569.ZS
#> 1493 BAR.TER.CMPT.7074.FE.ZS
#> 1494 BAR.TER.CMPT.7074.ZS
#> 1495 BAR.TER.CMPT.75UP.FE.ZS
#> 1496 BAR.TER.CMPT.75UP.ZS
#> 1497 BAR.TER.ICMP.1519.FE.ZS
#> 1498 BAR.TER.ICMP.1519.ZS
#> 1499 BAR.TER.ICMP.15UP.FE.ZS
#> 1500 BAR.TER.ICMP.15UP.ZS
#> 1501 BAR.TER.ICMP.2024.FE.ZS
#> 1502 BAR.TER.ICMP.2024.ZS
#> 1503 BAR.TER.ICMP.2529.FE.ZS
#> 1504 BAR.TER.ICMP.2529.ZS
#> 1505 BAR.TER.ICMP.25UP.FE.ZS
#> 1506 BAR.TER.ICMP.25UP.ZS
#> 1507 BAR.TER.ICMP.3034.FE.ZS
#> 1508 BAR.TER.ICMP.3034.ZS
#> 1509 BAR.TER.ICMP.3539.FE.ZS
#> 1510 BAR.TER.ICMP.3539.ZS
#> 1511 BAR.TER.ICMP.4044.FE.ZS
#> 1512 BAR.TER.ICMP.4044.ZS
#> 1513 BAR.TER.ICMP.4549.FE.ZS
#> 1514 BAR.TER.ICMP.4549.ZS
#> 1515 BAR.TER.ICMP.5054.FE.ZS
#> 1516 BAR.TER.ICMP.5054.ZS
#> 1517 BAR.TER.ICMP.5559.FE.ZS
#> 1518 BAR.TER.ICMP.5559.ZS
#> 1519 BAR.TER.ICMP.6064.FE.ZS
#> 1520 BAR.TER.ICMP.6064.ZS
#> 1521 BAR.TER.ICMP.6569.FE.ZS
#> 1522 BAR.TER.ICMP.6569.ZS
#> 1523 BAR.TER.ICMP.7074.FE.ZS
#> 1524 BAR.TER.ICMP.7074.ZS
#> 1525 BAR.TER.ICMP.75UP.FE.ZS
#> 1526 BAR.TER.ICMP.75UP.ZS
#> 1916 C1.2
#> 2024 CC.ADPO.MAEX.AA
#> 2025 CC.ADPO.MAEX.BB
#> 2026 CC.ADPO.MIEX.AA
#> 2027 CC.ADPO.MIEX.BB
#> 2029 CC.AVPB.PTPI.AI
#> 2030 CC.AVPB.PTPI.AR
#> 2031 CC.AVPB.PTPI.DI
#> 2032 CC.AVPB.PTPI.FP
#> 2033 CC.AVPB.PTPI.HE
#> 2034 CC.AVPB.PTPI.LP
#> 2035 CC.AVPB.TPOP.AG
#> 2036 CC.AVPB.TPOP.AI
#> 2037 CC.AVPB.TPOP.DI
#> 2038 CC.AVPB.TPOP.HE
#> 2039 CC.AVPB.TPOP.TE
#> 2040 CC.CHIC.BTFP.AG
#> 2041 CC.CHIC.BTFP.AI
#> 2042 CC.CHIC.BTFP.DI
#> 2043 CC.CHIC.BTFP.HE
#> 2044 CC.CHIC.BTFP.TE
#> 2045 CC.CHIC.CFPI.AG
#> 2046 CC.CHIC.CFPI.AI
#> 2047 CC.CHIC.CFPI.DI
#> 2048 CC.CHIC.CFPI.FP
#> 2049 CC.CHIC.CFPI.HE
#> 2050 CC.CHIC.CFPI.LP
#> 2236 CC.FLD.BELW.ZS
#> 2237 CC.FLD.TOTL.ZS
#> 2272 CC.GHG.MEMG.PO
#> 2358 CC.SE.CAT2.ZS
#> 2359 CC.SE.CAT3.ZS
#> 2361 CC.SH.AIRP.AIR
#> 2362 CC.SH.AIRP.AMB
#> 2363 CC.SP.COV.ZS
#> 2411 CoCA_headcount
#> 2423 CoHD_headcount
#> 2439 CoNA_headcount
#> 5429 DT.ODA.DACD.POP.CD
#> 5966 EG.CFT.ACCS.RU.ZS
#> 5967 EG.CFT.ACCS.UR.ZS
#> 5968 EG.CFT.ACCS.ZS
#> 5971 EG.ELC.ACCS.RU.ZS
#> 5972 EG.ELC.ACCS.UR.ZS
#> 5973 EG.ELC.ACCS.ZS
#> 5999 EG.NSF.ACCS.RU.ZS
#> 6000 EG.NSF.ACCS.UR.ZS
#> 6001 EG.NSF.ACCS.ZS
#> 6013 EN.AGR.EMPL
#> 6014 EN.AGR.EMPL.FE
#> 6015 EN.AGR.EMPL.IN
#> 6016 EN.AGR.EMPL.MA
#> 6064 EN.ATM.PM25.MC.T1.ZS
#> 6065 EN.ATM.PM25.MC.T2.ZS
#> 6066 EN.ATM.PM25.MC.T3.ZS
#> 6067 EN.ATM.PM25.MC.ZS
#> 6075 EN.CLC.MDAT.ZS
#> 6103 EN.NAGR.EMPL.IN
#> 6104 EN.POP.DNST
#> 6105 EN.POP.EL5M.RU.ZS
#> 6106 EN.POP.EL5M.UR.ZS
#> 6107 EN.POP.EL5M.ZS
#> 6108 EN.POP.SLUM.UR.ZS
#> 6113 EN.RUR.DNST
#> 6114 EN.RUR.DNST.TOTL
#> 6120 EN.URB.LCTY
#> 6121 EN.URB.LCTY.UR.ZS
#> 6122 EN.URB.MCTY
#> 6123 EN.URB.MCTY.TL.ZS
#> 7474 FX.OWN.TOTL.40.ZS
#> 7475 FX.OWN.TOTL.60.ZS
#> 7476 FX.OWN.TOTL.FE.ZS
#> 7477 FX.OWN.TOTL.MA.ZS
#> 7478 FX.OWN.TOTL.OL.ZS
#> 7479 FX.OWN.TOTL.PL.ZS
#> 7480 FX.OWN.TOTL.SO.ZS
#> 7481 FX.OWN.TOTL.YG.ZS
#> 7482 FX.OWN.TOTL.ZS
#> 7945 HF.CON.AIDS.FE.ZS
#> 7946 HF.CON.AIDS.FE.ZS.Q1
#> 7947 HF.CON.AIDS.FE.ZS.Q2
#> 7948 HF.CON.AIDS.FE.ZS.Q3
#> 7949 HF.CON.AIDS.FE.ZS.Q4
#> 7950 HF.CON.AIDS.FE.ZS.Q5
#> 7951 HF.DYN.AIDS.ZS
#> 7952 HF.DYN.AIDS.ZS.Q1
#> 7953 HF.DYN.AIDS.ZS.Q2
#> 7954 HF.DYN.AIDS.ZS.Q3
#> 7955 HF.DYN.AIDS.ZS.Q4
#> 7956 HF.DYN.AIDS.ZS.Q5
#> 7993 HF.MLR.NETS.ZS
#> 7994 HF.MLR.NETS.ZS.Q1
#> 7995 HF.MLR.NETS.ZS.Q2
#> 7996 HF.MLR.NETS.ZS.Q3
#> 7997 HF.MLR.NETS.ZS.Q4
#> 7998 HF.MLR.NETS.ZS.Q5
#> 8011 HF.STA.BLSG.ZS
#> 8012 HF.STA.BLSG.ZS.Q1
#> 8013 HF.STA.BLSG.ZS.Q2
#> 8014 HF.STA.BLSG.ZS.Q3
#> 8015 HF.STA.BLSG.ZS.Q4
#> 8016 HF.STA.BLSG.ZS.Q5
#> 8041 HF.STA.BP18.ZS
#> 8042 HF.STA.BP18.ZS.Q1
#> 8043 HF.STA.BP18.ZS.Q2
#> 8044 HF.STA.BP18.ZS.Q3
#> 8045 HF.STA.BP18.ZS.Q4
#> 8046 HF.STA.BP18.ZS.Q5
#> 8047 HF.STA.BPDI
#> 8048 HF.STA.BPDI.Q1
#> 8049 HF.STA.BPDI.Q2
#> 8050 HF.STA.BPDI.Q3
#> 8051 HF.STA.BPDI.Q4
#> 8052 HF.STA.BPDI.Q5
#> 8053 HF.STA.BPHT.ZS
#> 8054 HF.STA.BPHT.ZS.Q1
#> 8055 HF.STA.BPHT.ZS.Q2
#> 8056 HF.STA.BPHT.ZS.Q3
#> 8057 HF.STA.BPHT.ZS.Q4
#> 8058 HF.STA.BPHT.ZS.Q5
#> 8059 HF.STA.BPSY
#> 8060 HF.STA.BPSY.Q1
#> 8061 HF.STA.BPSY.Q2
#> 8062 HF.STA.BPSY.Q3
#> 8063 HF.STA.BPSY.Q4
#> 8064 HF.STA.BPSY.Q5
#> 8065 HF.STA.BPTR.ZS
#> 8066 HF.STA.BPTR.ZS.Q1
#> 8067 HF.STA.BPTR.ZS.Q2
#> 8068 HF.STA.BPTR.ZS.Q3
#> 8069 HF.STA.BPTR.ZS.Q4
#> 8070 HF.STA.BPTR.ZS.Q5
#> 8077 HF.STA.CHOL
#> 8078 HF.STA.CHOL.ZS
#> 8079 HF.STA.CHOM.ZS
#> 8080 HF.STA.CHOM.ZS.Q1
#> 8081 HF.STA.CHOM.ZS.Q2
#> 8082 HF.STA.CHOM.ZS.Q3
#> 8083 HF.STA.CHOM.ZS.Q4
#> 8084 HF.STA.CHOM.ZS.Q5
#> 8085 HF.STA.DIAB.ZS
#> 8086 HF.STA.DIAB.ZS.Q1
#> 8087 HF.STA.DIAB.ZS.Q2
#> 8088 HF.STA.DIAB.ZS.Q3
#> 8089 HF.STA.DIAB.ZS.Q4
#> 8090 HF.STA.DIAB.ZS.Q5
#> 8091 HF.STA.GLUC
#> 8092 HF.STA.GLYC.ZS
#> 8117 HF.STA.INPT.ZS
#> 8118 HF.STA.INPT.ZS.Q1
#> 8119 HF.STA.INPT.ZS.Q2
#> 8120 HF.STA.INPT.ZS.Q3
#> 8121 HF.STA.INPT.ZS.Q4
#> 8122 HF.STA.INPT.ZS.Q5
#> 8135 HF.STA.OB15.FE.ZS
#> 8136 HF.STA.OB15.FE.ZS.Q1
#> 8137 HF.STA.OB15.FE.ZS.Q2
#> 8138 HF.STA.OB15.FE.ZS.Q3
#> 8139 HF.STA.OB15.FE.ZS.Q4
#> 8140 HF.STA.OB15.FE.ZS.Q5
#> 8141 HF.STA.OB18.FE.ZS
#> 8142 HF.STA.OB18.FE.ZS.Q1
#> 8143 HF.STA.OB18.FE.ZS.Q2
#> 8144 HF.STA.OB18.FE.ZS.Q3
#> 8145 HF.STA.OB18.FE.ZS.Q4
#> 8146 HF.STA.OB18.FE.ZS.Q5
#> 8147 HF.STA.OB18.MA.ZS
#> 8148 HF.STA.OB18.MA.ZS.Q1
#> 8149 HF.STA.OB18.MA.ZS.Q2
#> 8150 HF.STA.OB18.MA.ZS.Q3
#> 8151 HF.STA.OB18.MA.ZS.Q4
#> 8152 HF.STA.OB18.MA.ZS.Q5
#> 8153 HF.STA.OB18.ZS
#> 8154 HF.STA.OB18.ZS.Q1
#> 8155 HF.STA.OB18.ZS.Q2
#> 8156 HF.STA.OB18.ZS.Q3
#> 8157 HF.STA.OB18.ZS.Q4
#> 8158 HF.STA.OB18.ZS.Q5
#> 8165 HF.STA.OW15.FE.ZS
#> 8166 HF.STA.OW15.FE.ZS.Q1
#> 8167 HF.STA.OW15.FE.ZS.Q2
#> 8168 HF.STA.OW15.FE.ZS.Q3
#> 8169 HF.STA.OW15.FE.ZS.Q4
#> 8170 HF.STA.OW15.FE.ZS.Q5
#> 8171 HF.STA.OW18.FE.ZS
#> 8172 HF.STA.OW18.FE.ZS.Q1
#> 8173 HF.STA.OW18.FE.ZS.Q2
#> 8174 HF.STA.OW18.FE.ZS.Q3
#> 8175 HF.STA.OW18.FE.ZS.Q4
#> 8176 HF.STA.OW18.FE.ZS.Q5
#> 8177 HF.STA.OW18.MA.ZS
#> 8178 HF.STA.OW18.MA.ZS.Q1
#> 8179 HF.STA.OW18.MA.ZS.Q2
#> 8180 HF.STA.OW18.MA.ZS.Q3
#> 8181 HF.STA.OW18.MA.ZS.Q4
#> 8182 HF.STA.OW18.MA.ZS.Q5
#> 8183 HF.STA.OW18.ZS
#> 8184 HF.STA.OW18.ZS.Q1
#> 8185 HF.STA.OW18.ZS.Q2
#> 8186 HF.STA.OW18.ZS.Q3
#> 8187 HF.STA.OW18.ZS.Q4
#> 8188 HF.STA.OW18.ZS.Q5
#> 8195 HF.UHC.CONS.ZS
#> 8196 HF.UHC.CONS.ZS.Q1
#> 8197 HF.UHC.CONS.ZS.Q2
#> 8198 HF.UHC.CONS.ZS.Q3
#> 8199 HF.UHC.CONS.ZS.Q4
#> 8200 HF.UHC.CONS.ZS.Q5
#> 8202 HF.UHC.NOP1.ZS
#> 8203 HF.UHC.NOP1.ZS.Q1
#> 8204 HF.UHC.NOP1.ZS.Q2
#> 8205 HF.UHC.NOP1.ZS.Q3
#> 8206 HF.UHC.NOP1.ZS.Q4
#> 8207 HF.UHC.NOP1.ZS.Q5
#> 8209 HF.UHC.NOP2.ZS
#> 8210 HF.UHC.NOP2.ZS.Q1
#> 8211 HF.UHC.NOP2.ZS.Q2
#> 8212 HF.UHC.NOP2.ZS.Q3
#> 8213 HF.UHC.NOP2.ZS.Q4
#> 8214 HF.UHC.NOP2.ZS.Q5
#> 8216 HF.UHC.NOP3.ZS
#> 8217 HF.UHC.NOP3.ZS.Q1
#> 8218 HF.UHC.NOP3.ZS.Q2
#> 8219 HF.UHC.NOP3.ZS.Q3
#> 8220 HF.UHC.NOP3.ZS.Q4
#> 8221 HF.UHC.NOP3.ZS.Q5
#> 8223 HF.UHC.NOP4.ZS
#> 8224 HF.UHC.NOP4.ZS.Q1
#> 8225 HF.UHC.NOP4.ZS.Q2
#> 8226 HF.UHC.NOP4.ZS.Q3
#> 8227 HF.UHC.NOP4.ZS.Q4
#> 8228 HF.UHC.NOP4.ZS.Q5
#> 8229 HF.UHC.NOPX.ZS
#> 8230 HF.UHC.NOPX.ZS.Q1
#> 8231 HF.UHC.NOPX.ZS.Q2
#> 8232 HF.UHC.NOPX.ZS.Q3
#> 8233 HF.UHC.NOPX.ZS.Q4
#> 8234 HF.UHC.NOPX.ZS.Q5
#> 8242 HF.UHC.OOPC.10.ZS
#> 8243 HF.UHC.OOPC.10.ZS.Q1
#> 8244 HF.UHC.OOPC.10.ZS.Q2
#> 8245 HF.UHC.OOPC.10.ZS.Q3
#> 8246 HF.UHC.OOPC.10.ZS.Q4
#> 8247 HF.UHC.OOPC.10.ZS.Q5
#> 8248 HF.UHC.OOPC.25.ZS
#> 8249 HF.UHC.OOPC.25.ZS.Q1
#> 8250 HF.UHC.OOPC.25.ZS.Q2
#> 8251 HF.UHC.OOPC.25.ZS.Q3
#> 8252 HF.UHC.OOPC.25.ZS.Q4
#> 8253 HF.UHC.OOPC.25.ZS.Q5
#> 8708 IN.EC.POP.GRWTHRAT
#> 8709 IN.EC.POP.GRWTHRAT.RURL
#> 8710 IN.EC.POP.GRWTHRAT.URBN
#> 8711 IN.EC.POP.RURL
#> 8712 IN.EC.POP.RURL.PCT
#> 8713 IN.EC.POP.TOTL
#> 8714 IN.EC.POP.URBN.PCT
#> 8775 IN.FIN.POP.PERBANK
#> 8779 IN.HLTH.DOCS.PER100K
#> 8781 IN.HLTH.GOVHOSPTL.BEDS.PER100K
#> 8783 IN.HLTH.GOVHOSPTL.PER100K
#> 8863 IN.POV.SLUM.POP.FEMALE.NUM
#> 8864 IN.POV.SLUM.POP.MALE.NUM
#> 8865 IN.POV.SLUM.POP.TOTL.NUM
#> 8880 IN.TRANSPORT.RURLRD.DENSIT
#> 8882 IN.TRANSPORT.URBNRD.DENSIT
#> 8972 IT.CEL.COVR.ZS
#> 9027 IT.MOB.COV.ZS
#> 9052 IT.NET.USER.ZS
#> 9176 JI.EMP.AGRI.FE.ZS
#> 9177 JI.EMP.AGRI.HE.ZS
#> 9178 JI.EMP.AGRI.LE.ZS
#> 9179 JI.EMP.AGRI.MA.ZS
#> 9180 JI.EMP.AGRI.OL.ZS
#> 9181 JI.EMP.AGRI.RU.ZS
#> 9182 JI.EMP.AGRI.UR.ZS
#> 9183 JI.EMP.AGRI.YG.ZS
#> 9184 JI.EMP.AGRI.ZS
#> 9185 JI.EMP.ARFC.FE.ZS
#> 9186 JI.EMP.ARFC.HE.ZS
#> 9187 JI.EMP.ARFC.LE.ZS
#> 9188 JI.EMP.ARFC.MA.ZS
#> 9189 JI.EMP.ARFC.OL.ZS
#> 9190 JI.EMP.ARFC.RU.ZS
#> 9191 JI.EMP.ARFC.UR.ZS
#> 9192 JI.EMP.ARFC.YG.ZS
#> 9193 JI.EMP.ARFC.ZS
#> 9194 JI.EMP.CLRK.FE.ZS
#> 9195 JI.EMP.CLRK.HE.ZS
#> 9196 JI.EMP.CLRK.LE.ZS
#> 9197 JI.EMP.CLRK.MA.ZS
#> 9198 JI.EMP.CLRK.OL.ZS
#> 9199 JI.EMP.CLRK.RU.ZS
#> 9200 JI.EMP.CLRK.UR.ZS
#> 9201 JI.EMP.CLRK.YG.ZS
#> 9202 JI.EMP.CLRK.ZS
#> 9203 JI.EMP.CNST.FE.ZS
#> 9204 JI.EMP.CNST.HE.ZS
#> 9205 JI.EMP.CNST.LE.ZS
#> 9206 JI.EMP.CNST.MA.ZS
#> 9207 JI.EMP.CNST.OL.ZS
#> 9208 JI.EMP.CNST.RU.ZS
#> 9209 JI.EMP.CNST.UR.ZS
#> 9210 JI.EMP.CNST.YG.ZS
#> 9211 JI.EMP.CNST.ZS
#> 9212 JI.EMP.COME.FE.ZS
#> 9213 JI.EMP.COME.HE.ZS
#> 9214 JI.EMP.COME.LE.ZS
#> 9215 JI.EMP.COME.MA.ZS
#> 9216 JI.EMP.COME.OL.ZS
#> 9217 JI.EMP.COME.RU.ZS
#> 9218 JI.EMP.COME.UR.ZS
#> 9219 JI.EMP.COME.YG.ZS
#> 9220 JI.EMP.COME.ZS
#> 9221 JI.EMP.CONT.FE.ZS
#> 9222 JI.EMP.CONT.HE.ZS
#> 9223 JI.EMP.CONT.LE.ZS
#> 9224 JI.EMP.CONT.MA.ZS
#> 9225 JI.EMP.CONT.OL.ZS
#> 9226 JI.EMP.CONT.RU.ZS
#> 9227 JI.EMP.CONT.UR.ZS
#> 9228 JI.EMP.CONT.YG.ZS
#> 9229 JI.EMP.CONT.ZS
#> 9230 JI.EMP.CRFT.FE.ZS
#> 9231 JI.EMP.CRFT.HE.ZS
#> 9232 JI.EMP.CRFT.LE.ZS
#> 9233 JI.EMP.CRFT.MA.ZS
#> 9234 JI.EMP.CRFT.OL.ZS
#> 9235 JI.EMP.CRFT.RU.ZS
#> 9236 JI.EMP.CRFT.UR.ZS
#> 9237 JI.EMP.CRFT.YG.ZS
#> 9238 JI.EMP.CRFT.ZS
#> 9239 JI.EMP.ELEC.FE.ZS
#> 9240 JI.EMP.ELEC.HE.ZS
#> 9241 JI.EMP.ELEC.LE.ZS
#> 9242 JI.EMP.ELEC.MA.ZS
#> 9243 JI.EMP.ELEC.OL.ZS
#> 9244 JI.EMP.ELEC.RU.ZS
#> 9245 JI.EMP.ELEC.UR.ZS
#> 9246 JI.EMP.ELEC.YG.ZS
#> 9247 JI.EMP.ELEC.ZS
#> 9248 JI.EMP.ELEM.FE.ZS
#> 9249 JI.EMP.ELEM.HE.ZS
#> 9250 JI.EMP.ELEM.LE.ZS
#> 9251 JI.EMP.ELEM.MA.ZS
#> 9252 JI.EMP.ELEM.OL.ZS
#> 9253 JI.EMP.ELEM.RU.ZS
#> 9254 JI.EMP.ELEM.UR.ZS
#> 9255 JI.EMP.ELEM.YG.ZS
#> 9256 JI.EMP.ELEM.ZS
#> 9257 JI.EMP.FABU.FE.ZS
#> 9258 JI.EMP.FABU.HE.ZS
#> 9259 JI.EMP.FABU.LE.ZS
#> 9260 JI.EMP.FABU.MA.ZS
#> 9261 JI.EMP.FABU.OL.ZS
#> 9262 JI.EMP.FABU.RU.ZS
#> 9263 JI.EMP.FABU.UR.ZS
#> 9264 JI.EMP.FABU.YG.ZS
#> 9265 JI.EMP.FABU.ZS
#> 9266 JI.EMP.HINS.FE.ZS
#> 9267 JI.EMP.HINS.HE.ZS
#> 9268 JI.EMP.HINS.LE.ZS
#> 9269 JI.EMP.HINS.MA.ZS
#> 9270 JI.EMP.HINS.OL.ZS
#> 9271 JI.EMP.HINS.RU.ZS
#> 9272 JI.EMP.HINS.UR.ZS
#> 9273 JI.EMP.HINS.YG.ZS
#> 9274 JI.EMP.HINS.ZS
#> 9275 JI.EMP.IFRM.FE.ZS
#> 9276 JI.EMP.IFRM.HE.ZS
#> 9277 JI.EMP.IFRM.LE.ZS
#> 9278 JI.EMP.IFRM.MA.ZS
#> 9279 JI.EMP.IFRM.OL.ZS
#> 9280 JI.EMP.IFRM.RU.ZS
#> 9281 JI.EMP.IFRM.UR.ZS
#> 9282 JI.EMP.IFRM.YG.ZS
#> 9283 JI.EMP.IFRM.ZS
#> 9284 JI.EMP.INDU.FE.ZS
#> 9285 JI.EMP.INDU.HE.ZS
#> 9286 JI.EMP.INDU.LE.ZS
#> 9287 JI.EMP.INDU.MA.ZS
#> 9288 JI.EMP.INDU.OL.ZS
#> 9289 JI.EMP.INDU.RU.ZS
#> 9290 JI.EMP.INDU.UR.ZS
#> 9291 JI.EMP.INDU.YG.ZS
#> 9292 JI.EMP.INDU.ZS
#> 9293 JI.EMP.MACH.FE.ZS
#> 9294 JI.EMP.MACH.HE.ZS
#> 9295 JI.EMP.MACH.LE.ZS
#> 9296 JI.EMP.MACH.MA.ZS
#> 9297 JI.EMP.MACH.OL.ZS
#> 9298 JI.EMP.MACH.RU.ZS
#> 9299 JI.EMP.MACH.UR.ZS
#> 9300 JI.EMP.MACH.YG.ZS
#> 9301 JI.EMP.MACH.ZS
#> 9302 JI.EMP.MANF.FE.ZS
#> 9303 JI.EMP.MANF.HE.ZS
#> 9304 JI.EMP.MANF.LE.ZS
#> 9305 JI.EMP.MANF.MA.ZS
#> 9306 JI.EMP.MANF.OL.ZS
#> 9307 JI.EMP.MANF.RU.ZS
#> 9308 JI.EMP.MANF.UR.ZS
#> 9309 JI.EMP.MANF.YG.ZS
#> 9310 JI.EMP.MANF.ZS
#> 9311 JI.EMP.MINQ.FE.ZS
#> 9312 JI.EMP.MINQ.HE.ZS
#> 9313 JI.EMP.MINQ.LE.ZS
#> 9314 JI.EMP.MINQ.MA.ZS
#> 9315 JI.EMP.MINQ.OL.ZS
#> 9316 JI.EMP.MINQ.RU.ZS
#> 9317 JI.EMP.MINQ.UR.ZS
#> 9318 JI.EMP.MINQ.YG.ZS
#> 9319 JI.EMP.MINQ.ZS
#> 9320 JI.EMP.MPYR.FE.ZS
#> 9321 JI.EMP.MPYR.HE.ZS
#> 9322 JI.EMP.MPYR.LE.ZS
#> 9323 JI.EMP.MPYR.MA.ZS
#> 9324 JI.EMP.MPYR.NA.FE.ZS
#> 9325 JI.EMP.MPYR.NA.HE.ZS
#> 9326 JI.EMP.MPYR.NA.LE.ZS
#> 9327 JI.EMP.MPYR.NA.MA.ZS
#> 9328 JI.EMP.MPYR.NA.OL.ZS
#> 9329 JI.EMP.MPYR.NA.RU.ZS
#> 9330 JI.EMP.MPYR.NA.UR.ZS
#> 9331 JI.EMP.MPYR.NA.YG.ZS
#> 9332 JI.EMP.MPYR.NA.ZS
#> 9333 JI.EMP.MPYR.OL.ZS
#> 9334 JI.EMP.MPYR.RU.ZS
#> 9335 JI.EMP.MPYR.UR.ZS
#> 9336 JI.EMP.MPYR.YG.ZS
#> 9337 JI.EMP.MPYR.ZS
#> 9338 JI.EMP.NAGR.FE.HE.ZS
#> 9339 JI.EMP.NAGR.FE.LE.ZS
#> 9340 JI.EMP.NAGR.FE.OL.ZS
#> 9341 JI.EMP.NAGR.FE.RU.ZS
#> 9342 JI.EMP.NAGR.FE.UR.ZS
#> 9343 JI.EMP.NAGR.FE.YG.ZS
#> 9344 JI.EMP.NAGR.FE.ZS
#> 9352 JI.EMP.OSRV.FE.ZS
#> 9353 JI.EMP.OSRV.HE.ZS
#> 9354 JI.EMP.OSRV.LE.ZS
#> 9355 JI.EMP.OSRV.MA.ZS
#> 9356 JI.EMP.OSRV.OL.ZS
#> 9357 JI.EMP.OSRV.RU.ZS
#> 9358 JI.EMP.OSRV.UR.ZS
#> 9359 JI.EMP.OSRV.YG.ZS
#> 9360 JI.EMP.OSRV.ZS
#> 9361 JI.EMP.PADM.FE.ZS
#> 9362 JI.EMP.PADM.HE.ZS
#> 9363 JI.EMP.PADM.LE.ZS
#> 9364 JI.EMP.PADM.MA.ZS
#> 9365 JI.EMP.PADM.OL.ZS
#> 9366 JI.EMP.PADM.RU.ZS
#> 9367 JI.EMP.PADM.UR.ZS
#> 9368 JI.EMP.PADM.YG.ZS
#> 9369 JI.EMP.PADM.ZS
#> 9370 JI.EMP.PROF.FE.ZS
#> 9371 JI.EMP.PROF.HE.ZS
#> 9372 JI.EMP.PROF.LE.ZS
#> 9373 JI.EMP.PROF.MA.ZS
#> 9374 JI.EMP.PROF.OL.ZS
#> 9375 JI.EMP.PROF.RU.ZS
#> 9376 JI.EMP.PROF.UR.ZS
#> 9377 JI.EMP.PROF.YG.ZS
#> 9378 JI.EMP.PROF.ZS
#> 9379 JI.EMP.PUBS.FE.ZS
#> 9380 JI.EMP.PUBS.HE.ZS
#> 9381 JI.EMP.PUBS.LE.ZS
#> 9382 JI.EMP.PUBS.MA.ZS
#> 9383 JI.EMP.PUBS.OL.ZS
#> 9384 JI.EMP.PUBS.RU.ZS
#> 9385 JI.EMP.PUBS.UR.ZS
#> 9386 JI.EMP.PUBS.YG.ZS
#> 9387 JI.EMP.PUBS.ZS
#> 9388 JI.EMP.SELF.FE.ZS
#> 9389 JI.EMP.SELF.HE.ZS
#> 9390 JI.EMP.SELF.LE.ZS
#> 9391 JI.EMP.SELF.MA.ZS
#> 9392 JI.EMP.SELF.NA.FE.ZS
#> 9393 JI.EMP.SELF.NA.HE.ZS
#> 9394 JI.EMP.SELF.NA.LE.ZS
#> 9395 JI.EMP.SELF.NA.MA.ZS
#> 9396 JI.EMP.SELF.NA.OL.ZS
#> 9397 JI.EMP.SELF.NA.RU.ZS
#> 9398 JI.EMP.SELF.NA.UR.ZS
#> 9399 JI.EMP.SELF.NA.YG.ZS
#> 9400 JI.EMP.SELF.NA.ZS
#> 9401 JI.EMP.SELF.OL.ZS
#> 9402 JI.EMP.SELF.RU.ZS
#> 9403 JI.EMP.SELF.UR.ZS
#> 9404 JI.EMP.SELF.YG.ZS
#> 9405 JI.EMP.SELF.ZS
#> 9406 JI.EMP.SEOF.FE.ZS
#> 9407 JI.EMP.SEOF.HE.ZS
#> 9408 JI.EMP.SEOF.LE.ZS
#> 9409 JI.EMP.SEOF.MA.ZS
#> 9410 JI.EMP.SEOF.OL.ZS
#> 9411 JI.EMP.SEOF.RU.ZS
#> 9412 JI.EMP.SEOF.UR.ZS
#> 9413 JI.EMP.SEOF.YG.ZS
#> 9414 JI.EMP.SEOF.ZS
#> 9415 JI.EMP.SERV.FE.ZS
#> 9416 JI.EMP.SERV.HE.ZS
#> 9417 JI.EMP.SERV.LE.ZS
#> 9418 JI.EMP.SERV.MA.ZS
#> 9419 JI.EMP.SERV.OL.ZS
#> 9420 JI.EMP.SERV.RU.ZS
#> 9421 JI.EMP.SERV.UR.ZS
#> 9422 JI.EMP.SERV.YG.ZS
#> 9423 JI.EMP.SERV.ZS
#> 9424 JI.EMP.SKAG.FE.ZS
#> 9425 JI.EMP.SKAG.HE.ZS
#> 9426 JI.EMP.SKAG.LE.ZS
#> 9427 JI.EMP.SKAG.MA.ZS
#> 9428 JI.EMP.SKAG.OL.ZS
#> 9429 JI.EMP.SKAG.RU.ZS
#> 9430 JI.EMP.SKAG.UR.ZS
#> 9431 JI.EMP.SKAG.YG.ZS
#> 9432 JI.EMP.SKAG.ZS
#> 9433 JI.EMP.SSEC.FE.ZS
#> 9434 JI.EMP.SSEC.HE.ZS
#> 9435 JI.EMP.SSEC.LE.ZS
#> 9436 JI.EMP.SSEC.MA.ZS
#> 9437 JI.EMP.SSEC.OL.ZS
#> 9438 JI.EMP.SSEC.RU.ZS
#> 9439 JI.EMP.SSEC.UR.ZS
#> 9440 JI.EMP.SSEC.YG.ZS
#> 9441 JI.EMP.SSEC.ZS
#> 9442 JI.EMP.SVMK.FE.ZS
#> 9443 JI.EMP.SVMK.HE.ZS
#> 9444 JI.EMP.SVMK.LE.ZS
#> 9445 JI.EMP.SVMK.MA.ZS
#> 9446 JI.EMP.SVMK.OL.ZS
#> 9447 JI.EMP.SVMK.RU.ZS
#> 9448 JI.EMP.SVMK.UR.ZS
#> 9449 JI.EMP.SVMK.YG.ZS
#> 9450 JI.EMP.SVMK.ZS
#> 9451 JI.EMP.TECH.FE.ZS
#> 9452 JI.EMP.TECH.HE.ZS
#> 9453 JI.EMP.TECH.LE.ZS
#> 9454 JI.EMP.TECH.MA.ZS
#> 9455 JI.EMP.TECH.OL.ZS
#> 9456 JI.EMP.TECH.RU.ZS
#> 9457 JI.EMP.TECH.UR.ZS
#> 9458 JI.EMP.TECH.YG.ZS
#> 9459 JI.EMP.TECH.ZS
#> 9460 JI.EMP.TOTL.SP.FE.ZS
#> 9461 JI.EMP.TOTL.SP.HE.ZS
#> 9462 JI.EMP.TOTL.SP.LE.ZS
#> 9463 JI.EMP.TOTL.SP.MA.ZS
#> 9464 JI.EMP.TOTL.SP.OL.ZS
#> 9465 JI.EMP.TOTL.SP.RU.ZS
#> 9466 JI.EMP.TOTL.SP.UR.ZS
#> 9467 JI.EMP.TOTL.SP.YG.ZS
#> 9468 JI.EMP.TOTL.SP.ZS
#> 9469 JI.EMP.TRCM.FE.ZS
#> 9470 JI.EMP.TRCM.HE.ZS
#> 9471 JI.EMP.TRCM.LE.ZS
#> 9472 JI.EMP.TRCM.MA.ZS
#> 9473 JI.EMP.TRCM.OL.ZS
#> 9474 JI.EMP.TRCM.RU.ZS
#> 9475 JI.EMP.TRCM.UR.ZS
#> 9476 JI.EMP.TRCM.YG.ZS
#> 9477 JI.EMP.TRCM.ZS
#> 9478 JI.EMP.UNPD.FE.ZS
#> 9479 JI.EMP.UNPD.HE.ZS
#> 9480 JI.EMP.UNPD.LE.ZS
#> 9481 JI.EMP.UNPD.MA.ZS
#> 9482 JI.EMP.UNPD.NA.FE.ZS
#> 9483 JI.EMP.UNPD.NA.HE.ZS
#> 9484 JI.EMP.UNPD.NA.LE.ZS
#> 9485 JI.EMP.UNPD.NA.MA.ZS
#> 9486 JI.EMP.UNPD.NA.OL.ZS
#> 9487 JI.EMP.UNPD.NA.RU.ZS
#> 9488 JI.EMP.UNPD.NA.UR.ZS
#> 9489 JI.EMP.UNPD.NA.YG.ZS
#> 9490 JI.EMP.UNPD.NA.ZS
#> 9491 JI.EMP.UNPD.OL.ZS
#> 9492 JI.EMP.UNPD.RU.ZS
#> 9493 JI.EMP.UNPD.UR.ZS
#> 9494 JI.EMP.UNPD.YG.ZS
#> 9495 JI.EMP.UNPD.ZS
#> 9496 JI.EMP.UPSE.FE.ZS
#> 9497 JI.EMP.UPSE.HE.ZS
#> 9498 JI.EMP.UPSE.LE.ZS
#> 9499 JI.EMP.UPSE.MA.ZS
#> 9500 JI.EMP.UPSE.OL.ZS
#> 9501 JI.EMP.UPSE.RU.ZS
#> 9502 JI.EMP.UPSE.UR.ZS
#> 9503 JI.EMP.UPSE.YG.ZS
#> 9504 JI.EMP.UPSE.ZS
#> 9512 JI.EMP.WAGE.FE.ZS
#> 9513 JI.EMP.WAGE.HE.ZS
#> 9514 JI.EMP.WAGE.LE.ZS
#> 9515 JI.EMP.WAGE.MA.ZS
#> 9516 JI.EMP.WAGE.NA.FE.ZS
#> 9517 JI.EMP.WAGE.NA.HE.ZS
#> 9518 JI.EMP.WAGE.NA.LE.ZS
#> 9519 JI.EMP.WAGE.NA.MA.ZS
#> 9520 JI.EMP.WAGE.NA.OL.ZS
#> 9521 JI.EMP.WAGE.NA.RU.ZS
#> 9522 JI.EMP.WAGE.NA.UR.ZS
#> 9523 JI.EMP.WAGE.NA.YG.ZS
#> 9524 JI.EMP.WAGE.NA.ZS
#> 9525 JI.EMP.WAGE.OL.ZS
#> 9526 JI.EMP.WAGE.RU.ZS
#> 9527 JI.EMP.WAGE.UR.ZS
#> 9528 JI.EMP.WAGE.YG.ZS
#> 9529 JI.EMP.WAGE.ZS
#> 9530 JI.ENR.0616.FE.ZS
#> 9531 JI.ENR.0616.HE.ZS
#> 9532 JI.ENR.0616.LE.ZS
#> 9533 JI.ENR.0616.MA.ZS
#> 9534 JI.ENR.0616.RU.ZS
#> 9535 JI.ENR.0616.UR.ZS
#> 9536 JI.ENR.0616.YG.ZS
#> 9537 JI.ENR.0616.ZS
#> 9565 JI.JOB.MLTP.FE.ZS
#> 9566 JI.JOB.MLTP.HE.ZS
#> 9567 JI.JOB.MLTP.LE.ZS
#> 9568 JI.JOB.MLTP.MA.ZS
#> 9569 JI.JOB.MLTP.OL.ZS
#> 9570 JI.JOB.MLTP.RU.ZS
#> 9571 JI.JOB.MLTP.UR.ZS
#> 9572 JI.JOB.MLTP.YG.ZS
#> 9573 JI.JOB.MLTP.ZS
#> 9574 JI.POP.0014.FE.ZS
#> 9575 JI.POP.0014.HE.ZS
#> 9576 JI.POP.0014.LE.ZS
#> 9577 JI.POP.0014.MA.ZS
#> 9578 JI.POP.0014.RU.ZS
#> 9579 JI.POP.0014.UR.ZS
#> 9580 JI.POP.0014.ZS
#> 9581 JI.POP.1524.FE.ZS
#> 9582 JI.POP.1524.HE.ZS
#> 9583 JI.POP.1524.LE.ZS
#> 9584 JI.POP.1524.MA.ZS
#> 9585 JI.POP.1524.RU.ZS
#> 9586 JI.POP.1524.UR.ZS
#> 9587 JI.POP.1524.ZS
#> 9588 JI.POP.1564.FE.ZS
#> 9589 JI.POP.1564.HE.ZS
#> 9590 JI.POP.1564.LE.ZS
#> 9591 JI.POP.1564.MA.ZS
#> 9592 JI.POP.1564.RU.ZS
#> 9593 JI.POP.1564.UR.ZS
#> 9594 JI.POP.1564.ZS
#> 9595 JI.POP.2564.FE.ZS
#> 9596 JI.POP.2564.HE.ZS
#> 9597 JI.POP.2564.LE.ZS
#> 9598 JI.POP.2564.MA.ZS
#> 9599 JI.POP.2564.RU.ZS
#> 9600 JI.POP.2564.UR.ZS
#> 9601 JI.POP.2564.ZS
#> 9602 JI.POP.65UP.FE.ZS
#> 9603 JI.POP.65UP.HE.ZS
#> 9604 JI.POP.65UP.LE.ZS
#> 9605 JI.POP.65UP.MA.ZS
#> 9606 JI.POP.65UP.RU.ZS
#> 9607 JI.POP.65UP.UR.ZS
#> 9608 JI.POP.65UP.ZS
#> 9612 JI.POP.NEDU.FE.ZS
#> 9613 JI.POP.NEDU.LE.ZS
#> 9614 JI.POP.NEDU.MA.ZS
#> 9615 JI.POP.NEDU.OL.ZS
#> 9616 JI.POP.NEDU.RU.ZS
#> 9617 JI.POP.NEDU.UR.ZS
#> 9618 JI.POP.NEDU.YG.ZS
#> 9619 JI.POP.NEDU.ZS
#> 9620 JI.POP.PRIM.FE.ZS
#> 9621 JI.POP.PRIM.LE.ZS
#> 9622 JI.POP.PRIM.MA.ZS
#> 9623 JI.POP.PRIM.OL.ZS
#> 9624 JI.POP.PRIM.RU.ZS
#> 9625 JI.POP.PRIM.UR.ZS
#> 9626 JI.POP.PRIM.YG.ZS
#> 9627 JI.POP.PRIM.ZS
#> 9628 JI.POP.SECO.FE.ZS
#> 9629 JI.POP.SECO.HE.ZS
#> 9630 JI.POP.SECO.MA.ZS
#> 9631 JI.POP.SECO.OL.ZS
#> 9632 JI.POP.SECO.PO.FE.ZS
#> 9633 JI.POP.SECO.PO.HE.ZS
#> 9634 JI.POP.SECO.PO.MA.ZS
#> 9635 JI.POP.SECO.PO.OL.ZS
#> 9636 JI.POP.SECO.PO.RU.ZS
#> 9637 JI.POP.SECO.PO.UR.ZS
#> 9638 JI.POP.SECO.PO.YG.ZS
#> 9639 JI.POP.SECO.PO.ZS
#> 9640 JI.POP.SECO.RU.ZS
#> 9641 JI.POP.SECO.UR.ZS
#> 9642 JI.POP.SECO.YG.ZS
#> 9643 JI.POP.SECO.ZS
#> 9644 JI.POP.TOTL
#> 9645 JI.POP.TOTL.FE
#> 9646 JI.POP.TOTL.HE
#> 9647 JI.POP.TOTL.LE
#> 9648 JI.POP.TOTL.MA
#> 9649 JI.POP.TOTL.OL
#> 9650 JI.POP.TOTL.RU
#> 9651 JI.POP.TOTL.UR
#> 9652 JI.POP.TOTL.YG
#> 9653 JI.POP.URBN.FE.ZS
#> 9654 JI.POP.URBN.HE.ZS
#> 9655 JI.POP.URBN.LE.ZS
#> 9656 JI.POP.URBN.MA.ZS
#> 9657 JI.POP.URBN.OL.ZS
#> 9658 JI.POP.URBN.YG.ZS
#> 9659 JI.POP.URBN.ZS
#> 9696 JI.TLF.35BL.TM.FE.ZS
#> 9697 JI.TLF.35BL.TM.HE.ZS
#> 9698 JI.TLF.35BL.TM.LE.ZS
#> 9699 JI.TLF.35BL.TM.MA.ZS
#> 9700 JI.TLF.35BL.TM.OL.ZS
#> 9701 JI.TLF.35BL.TM.RU.ZS
#> 9702 JI.TLF.35BL.TM.UR.ZS
#> 9703 JI.TLF.35BL.TM.YG.ZS
#> 9704 JI.TLF.35BL.TM.ZS
#> 9705 JI.TLF.48UP.TM.FE.ZS
#> 9706 JI.TLF.48UP.TM.HE.ZS
#> 9707 JI.TLF.48UP.TM.LE.ZS
#> 9708 JI.TLF.48UP.TM.MA.ZS
#> 9709 JI.TLF.48UP.TM.OL.ZS
#> 9710 JI.TLF.48UP.TM.RU.ZS
#> 9711 JI.TLF.48UP.TM.UR.ZS
#> 9712 JI.TLF.48UP.TM.YG.ZS
#> 9713 JI.TLF.48UP.TM.ZS
#> 9748 JI.UEM.NEET.FE.ZS
#> 9749 JI.UEM.NEET.HE.ZS
#> 9750 JI.UEM.NEET.LE.ZS
#> 9751 JI.UEM.NEET.MA.ZS
#> 9752 JI.UEM.NEET.RU.ZS
#> 9753 JI.UEM.NEET.UR.ZS
#> 9754 JI.UEM.NEET.ZS
#> 9826 lm_ub.cov_pop
#> 11715 per_allsp.cov_pop_tot
#> 11993 per_lm_alllm.cov_pop_tot
#> 11997 per_lm_alllm.cov_q1_tot
#> 12001 per_lm_alllm.cov_q2_tot
#> 12005 per_lm_alllm.cov_q3_tot
#> 12009 per_lm_alllm.cov_q4_tot
#> 12013 per_lm_alllm.cov_q5_tot
#> 12165 per_lmonl.overlap_ep_preT_tot
#> 12166 per_lmonl.overlap_ep_tot
#> 12167 per_lmonl.overlap_pop_preT_tot
#> 12168 per_lmonl.overlap_pop_rur
#> 12169 per_lmonl.overlap_pop_tot
#> 12170 per_lmonl.overlap_pop_urb
#> 12171 per_lmonl.overlap_q1_preT_tot
#> 12172 per_lmonl.overlap_q1_rur
#> 12173 per_lmonl.overlap_q1_tot
#> 12174 per_lmonl.overlap_q1_urb
#> 12175 per_nprog.overlap_ep_preT_tot
#> 12176 per_nprog.overlap_ep_tot
#> 12177 per_nprog.overlap_pop_preT_tot
#> 12178 per_nprog.overlap_pop_rur
#> 12179 per_nprog.overlap_pop_tot
#> 12180 per_nprog.overlap_pop_urb
#> 12181 per_nprog.overlap_q1_preT_tot
#> 12182 per_nprog.overlap_q1_rur
#> 12183 per_nprog.overlap_q1_tot
#> 12184 per_nprog.overlap_q1_urb
#> 12185 per_numprog1_ep_preT_tot
#> 12186 per_numprog1_ep_tot
#> 12187 per_numprog1_pop_preT_tot
#> 12188 per_numprog1_pop_rur
#> 12189 per_numprog1_pop_tot
#> 12190 per_numprog1_pop_urb
#> 12191 per_numprog1_q1_preT_tot
#> 12192 per_numprog1_q1_rur
#> 12193 per_numprog1_q1_tot
#> 12194 per_numprog1_q1_urb
#> 12195 per_numprog2_ep_preT_tot
#> 12196 per_numprog2_ep_tot
#> 12197 per_numprog2_pop_preT_tot
#> 12198 per_numprog2_pop_rur
#> 12199 per_numprog2_pop_tot
#> 12200 per_numprog2_pop_urb
#> 12201 per_numprog2_q1_preT_tot
#> 12202 per_numprog2_q1_rur
#> 12203 per_numprog2_q1_tot
#> 12204 per_numprog2_q1_urb
#> 12205 per_numprog3_ep_preT_tot
#> 12206 per_numprog3_ep_tot
#> 12207 per_numprog3_pop_preT_tot
#> 12208 per_numprog3_pop_rur
#> 12209 per_numprog3_pop_tot
#> 12210 per_numprog3_pop_urb
#> 12211 per_numprog3_q1_preT_tot
#> 12212 per_numprog3_q1_rur
#> 12213 per_numprog3_q1_tot
#> 12214 per_numprog3_q1_urb
#> 12215 per_numprog4_ep_preT_tot
#> 12216 per_numprog4_ep_tot
#> 12217 per_numprog4_pop_preT_tot
#> 12218 per_numprog4_pop_rur
#> 12219 per_numprog4_pop_tot
#> 12220 per_numprog4_pop_urb
#> 12221 per_numprog4_q1_preT_tot
#> 12222 per_numprog4_q1_rur
#> 12223 per_numprog4_q1_tot
#> 12224 per_numprog4_q1_urb
#> 12748 per_sa_allsa.cov_pop_tot
#> 12752 per_sa_allsa.cov_q1_tot
#> 12756 per_sa_allsa.cov_q2_tot
#> 12760 per_sa_allsa.cov_q3_tot
#> 12764 per_sa_allsa.cov_q4_tot
#> 12768 per_sa_allsa.cov_q5_tot
#> 13893 per_saonl.overlap_ep_preT_tot
#> 13894 per_saonl.overlap_ep_tot
#> 13895 per_saonl.overlap_pop_preT_tot
#> 13896 per_saonl.overlap_pop_rur
#> 13897 per_saonl.overlap_pop_tot
#> 13898 per_saonl.overlap_pop_urb
#> 13899 per_saonl.overlap_q1_preT_tot
#> 13900 per_saonl.overlap_q1_rur
#> 13901 per_saonl.overlap_q1_tot
#> 13902 per_saonl.overlap_q1_urb
#> 13903 per_saoth.overlap_ep_preT_tot
#> 13904 per_saoth.overlap_ep_tot
#> 13905 per_saoth.overlap_pop_preT_tot
#> 13906 per_saoth.overlap_pop_rur
#> 13907 per_saoth.overlap_pop_tot
#> 13908 per_saoth.overlap_pop_urb
#> 13909 per_saoth.overlap_q1_preT_tot
#> 13910 per_saoth.overlap_q1_rur
#> 13911 per_saoth.overlap_q1_tot
#> 13912 per_saoth.overlap_q1_urb
#> 14019 per_si_allsi.cov_pop_tot
#> 14023 per_si_allsi.cov_q1_tot
#> 14027 per_si_allsi.cov_q2_tot
#> 14031 per_si_allsi.cov_q3_tot
#> 14035 per_si_allsi.cov_q4_tot
#> 14039 per_si_allsi.cov_q5_tot
#> 14330 per_silm.overlap_ep_preT_tot
#> 14331 per_silm.overlap_ep_tot
#> 14332 per_silm.overlap_pop_preT_tot
#> 14333 per_silm.overlap_pop_rur
#> 14334 per_silm.overlap_pop_tot
#> 14335 per_silm.overlap_pop_urb
#> 14336 per_silm.overlap_q1_preT_tot
#> 14337 per_silm.overlap_q1_rur
#> 14338 per_silm.overlap_q1_tot
#> 14339 per_silm.overlap_q1_urb
#> 14340 per_sionl.overlap_ep_preT_tot
#> 14341 per_sionl.overlap_ep_tot
#> 14342 per_sionl.overlap_pop_preT_tot
#> 14343 per_sionl.overlap_pop_rur
#> 14344 per_sionl.overlap_pop_tot
#> 14345 per_sionl.overlap_pop_urb
#> 14346 per_sionl.overlap_q1_preT_tot
#> 14347 per_sionl.overlap_q1_rur
#> 14348 per_sionl.overlap_q1_tot
#> 14349 per_sionl.overlap_q1_urb
#> 14446 PRJ.ATT.1519.1.FE
#> 14447 PRJ.ATT.1519.1.MA
#> 14448 PRJ.ATT.1519.1.MF
#> 14449 PRJ.ATT.1519.2.FE
#> 14450 PRJ.ATT.1519.2.MA
#> 14451 PRJ.ATT.1519.2.MF
#> 14452 PRJ.ATT.1519.3.FE
#> 14453 PRJ.ATT.1519.3.MA
#> 14454 PRJ.ATT.1519.3.MF
#> 14455 PRJ.ATT.1519.4.FE
#> 14456 PRJ.ATT.1519.4.MA
#> 14457 PRJ.ATT.1519.4.MF
#> 14458 PRJ.ATT.1519.NED.FE
#> 14459 PRJ.ATT.1519.NED.MA
#> 14460 PRJ.ATT.1519.NED.MF
#> 14461 PRJ.ATT.1519.S1.FE
#> 14462 PRJ.ATT.1519.S1.MA
#> 14463 PRJ.ATT.1519.S1.MF
#> 14464 PRJ.ATT.15UP.1.FE
#> 14465 PRJ.ATT.15UP.1.MA
#> 14466 PRJ.ATT.15UP.1.MF
#> 14467 PRJ.ATT.15UP.2.FE
#> 14468 PRJ.ATT.15UP.2.MA
#> 14469 PRJ.ATT.15UP.2.MF
#> 14470 PRJ.ATT.15UP.3.FE
#> 14471 PRJ.ATT.15UP.3.MA
#> 14472 PRJ.ATT.15UP.3.MF
#> 14473 PRJ.ATT.15UP.4.FE
#> 14474 PRJ.ATT.15UP.4.MA
#> 14475 PRJ.ATT.15UP.4.MF
#> 14476 PRJ.ATT.15UP.NED.FE
#> 14477 PRJ.ATT.15UP.NED.MA
#> 14478 PRJ.ATT.15UP.NED.MF
#> 14479 PRJ.ATT.15UP.S1.FE
#> 14480 PRJ.ATT.15UP.S1.MA
#> 14481 PRJ.ATT.15UP.S1.MF
#> 14482 PRJ.ATT.2024.1.FE
#> 14483 PRJ.ATT.2024.1.MA
#> 14484 PRJ.ATT.2024.1.MF
#> 14485 PRJ.ATT.2024.2.FE
#> 14486 PRJ.ATT.2024.2.MA
#> 14487 PRJ.ATT.2024.2.MF
#> 14488 PRJ.ATT.2024.3.FE
#> 14489 PRJ.ATT.2024.3.MA
#> 14490 PRJ.ATT.2024.3.MF
#> 14491 PRJ.ATT.2024.4.FE
#> 14492 PRJ.ATT.2024.4.MA
#> 14493 PRJ.ATT.2024.4.MF
#> 14494 PRJ.ATT.2024.NED.FE
#> 14495 PRJ.ATT.2024.NED.MA
#> 14496 PRJ.ATT.2024.NED.MF
#> 14497 PRJ.ATT.2024.S1.FE
#> 14498 PRJ.ATT.2024.S1.MA
#> 14499 PRJ.ATT.2024.S1.MF
#> 14500 PRJ.ATT.2039.1.FE
#> 14501 PRJ.ATT.2039.1.MA
#> 14502 PRJ.ATT.2039.1.MF
#> 14503 PRJ.ATT.2039.2.FE
#> 14504 PRJ.ATT.2039.2.MA
#> 14505 PRJ.ATT.2039.2.MF
#> 14506 PRJ.ATT.2039.3.FE
#> 14507 PRJ.ATT.2039.3.MA
#> 14508 PRJ.ATT.2039.3.MF
#> 14509 PRJ.ATT.2039.4.FE
#> 14510 PRJ.ATT.2039.4.MA
#> 14511 PRJ.ATT.2039.4.MF
#> 14512 PRJ.ATT.2039.NED.FE
#> 14513 PRJ.ATT.2039.NED.MA
#> 14514 PRJ.ATT.2039.NED.MF
#> 14515 PRJ.ATT.2039.S1.FE
#> 14516 PRJ.ATT.2039.S1.MA
#> 14517 PRJ.ATT.2039.S1.MF
#> 14518 PRJ.ATT.2064.1.FE
#> 14519 PRJ.ATT.2064.1.MA
#> 14520 PRJ.ATT.2064.1.MF
#> 14521 PRJ.ATT.2064.2.FE
#> 14522 PRJ.ATT.2064.2.MA
#> 14523 PRJ.ATT.2064.2.MF
#> 14524 PRJ.ATT.2064.3.FE
#> 14525 PRJ.ATT.2064.3.MA
#> 14526 PRJ.ATT.2064.3.MF
#> 14527 PRJ.ATT.2064.4.FE
#> 14528 PRJ.ATT.2064.4.MA
#> 14529 PRJ.ATT.2064.4.MF
#> 14530 PRJ.ATT.2064.NED.FE
#> 14531 PRJ.ATT.2064.NED.MA
#> 14532 PRJ.ATT.2064.NED.MF
#> 14533 PRJ.ATT.2064.S1.FE
#> 14534 PRJ.ATT.2064.S1.MA
#> 14535 PRJ.ATT.2064.S1.MF
#> 14536 PRJ.ATT.2529.1.FE
#> 14537 PRJ.ATT.2529.1.MA
#> 14538 PRJ.ATT.2529.1.MF
#> 14539 PRJ.ATT.2529.2.FE
#> 14540 PRJ.ATT.2529.2.MA
#> 14541 PRJ.ATT.2529.2.MF
#> 14542 PRJ.ATT.2529.3.FE
#> 14543 PRJ.ATT.2529.3.MA
#> 14544 PRJ.ATT.2529.3.MF
#> 14545 PRJ.ATT.2529.4.FE
#> 14546 PRJ.ATT.2529.4.MA
#> 14547 PRJ.ATT.2529.4.MF
#> 14548 PRJ.ATT.2529.NED.FE
#> 14549 PRJ.ATT.2529.NED.MA
#> 14550 PRJ.ATT.2529.NED.MF
#> 14551 PRJ.ATT.2529.S1.FE
#> 14552 PRJ.ATT.2529.S1.MA
#> 14553 PRJ.ATT.2529.S1.MF
#> 14554 PRJ.ATT.25UP.1.FE
#> 14555 PRJ.ATT.25UP.1.MA
#> 14556 PRJ.ATT.25UP.1.MF
#> 14557 PRJ.ATT.25UP.2.FE
#> 14558 PRJ.ATT.25UP.2.MA
#> 14559 PRJ.ATT.25UP.2.MF
#> 14560 PRJ.ATT.25UP.3.FE
#> 14561 PRJ.ATT.25UP.3.MA
#> 14562 PRJ.ATT.25UP.3.MF
#> 14563 PRJ.ATT.25UP.4.FE
#> 14564 PRJ.ATT.25UP.4.MA
#> 14565 PRJ.ATT.25UP.4.MF
#> 14566 PRJ.ATT.25UP.NED.FE
#> 14567 PRJ.ATT.25UP.NED.MA
#> 14568 PRJ.ATT.25UP.NED.MF
#> 14569 PRJ.ATT.25UP.S1.FE
#> 14570 PRJ.ATT.25UP.S1.MA
#> 14571 PRJ.ATT.25UP.S1.MF
#> 14572 PRJ.ATT.4064.1.FE
#> 14573 PRJ.ATT.4064.1.MA
#> 14574 PRJ.ATT.4064.1.MF
#> 14575 PRJ.ATT.4064.2.FE
#> 14576 PRJ.ATT.4064.2.MA
#> 14577 PRJ.ATT.4064.2.MF
#> 14578 PRJ.ATT.4064.3.FE
#> 14579 PRJ.ATT.4064.3.MA
#> 14580 PRJ.ATT.4064.3.MF
#> 14581 PRJ.ATT.4064.4.FE
#> 14582 PRJ.ATT.4064.4.MA
#> 14583 PRJ.ATT.4064.4.MF
#> 14584 PRJ.ATT.4064.NED.FE
#> 14585 PRJ.ATT.4064.NED.MA
#> 14586 PRJ.ATT.4064.NED.MF
#> 14587 PRJ.ATT.4064.S1.FE
#> 14588 PRJ.ATT.4064.S1.MA
#> 14589 PRJ.ATT.4064.S1.MF
#> 14590 PRJ.ATT.60UP.1.FE
#> 14591 PRJ.ATT.60UP.1.MA
#> 14592 PRJ.ATT.60UP.1.MF
#> 14593 PRJ.ATT.60UP.2.FE
#> 14594 PRJ.ATT.60UP.2.MA
#> 14595 PRJ.ATT.60UP.2.MF
#> 14596 PRJ.ATT.60UP.3.FE
#> 14597 PRJ.ATT.60UP.3.MA
#> 14598 PRJ.ATT.60UP.3.MF
#> 14599 PRJ.ATT.60UP.4.FE
#> 14600 PRJ.ATT.60UP.4.MA
#> 14601 PRJ.ATT.60UP.4.MF
#> 14602 PRJ.ATT.60UP.NED.FE
#> 14603 PRJ.ATT.60UP.NED.MA
#> 14604 PRJ.ATT.60UP.NED.MF
#> 14605 PRJ.ATT.60UP.S1.FE
#> 14606 PRJ.ATT.60UP.S1.MA
#> 14607 PRJ.ATT.60UP.S1.MF
#> 14608 PRJ.ATT.80UP.1.FE
#> 14609 PRJ.ATT.80UP.1.MA
#> 14610 PRJ.ATT.80UP.1.MF
#> 14611 PRJ.ATT.80UP.2.FE
#> 14612 PRJ.ATT.80UP.2.MA
#> 14613 PRJ.ATT.80UP.2.MF
#> 14614 PRJ.ATT.80UP.3.FE
#> 14615 PRJ.ATT.80UP.3.MA
#> 14616 PRJ.ATT.80UP.3.MF
#> 14617 PRJ.ATT.80UP.4.FE
#> 14618 PRJ.ATT.80UP.4.MA
#> 14619 PRJ.ATT.80UP.4.MF
#> 14620 PRJ.ATT.80UP.NED.FE
#> 14621 PRJ.ATT.80UP.NED.MA
#> 14622 PRJ.ATT.80UP.NED.MF
#> 14623 PRJ.ATT.80UP.S1.FE
#> 14624 PRJ.ATT.80UP.S1.MA
#> 14625 PRJ.ATT.80UP.S1.MF
#> 14626 PRJ.ATT.ALL.1.FE
#> 14627 PRJ.ATT.ALL.1.MA
#> 14628 PRJ.ATT.ALL.1.MF
#> 14629 PRJ.ATT.ALL.2.FE
#> 14630 PRJ.ATT.ALL.2.MA
#> 14631 PRJ.ATT.ALL.2.MF
#> 14632 PRJ.ATT.ALL.3.FE
#> 14633 PRJ.ATT.ALL.3.MA
#> 14634 PRJ.ATT.ALL.3.MF
#> 14635 PRJ.ATT.ALL.4.FE
#> 14636 PRJ.ATT.ALL.4.MA
#> 14637 PRJ.ATT.ALL.4.MF
#> 14638 PRJ.ATT.ALL.NED.FE
#> 14639 PRJ.ATT.ALL.NED.MA
#> 14640 PRJ.ATT.ALL.NED.MF
#> 14641 PRJ.ATT.ALL.S1.FE
#> 14642 PRJ.ATT.ALL.S1.MA
#> 14643 PRJ.ATT.ALL.S1.MF
#> 14682 PRJ.POP.1519.1.FE
#> 14683 PRJ.POP.1519.1.MA
#> 14684 PRJ.POP.1519.1.MF
#> 14685 PRJ.POP.1519.2.FE
#> 14686 PRJ.POP.1519.2.MA
#> 14687 PRJ.POP.1519.2.MF
#> 14688 PRJ.POP.1519.3.FE
#> 14689 PRJ.POP.1519.3.MA
#> 14690 PRJ.POP.1519.3.MF
#> 14691 PRJ.POP.1519.4.FE
#> 14692 PRJ.POP.1519.4.MA
#> 14693 PRJ.POP.1519.4.MF
#> 14694 PRJ.POP.1519.NED.FE
#> 14695 PRJ.POP.1519.NED.MA
#> 14696 PRJ.POP.1519.NED.MF
#> 14697 PRJ.POP.1519.S1.FE
#> 14698 PRJ.POP.1519.S1.MA
#> 14699 PRJ.POP.1519.S1.MF
#> 14700 PRJ.POP.2024.1.FE
#> 14701 PRJ.POP.2024.1.MA
#> 14702 PRJ.POP.2024.1.MF
#> 14703 PRJ.POP.2024.2.FE
#> 14704 PRJ.POP.2024.2.MA
#> 14705 PRJ.POP.2024.2.MF
#> 14706 PRJ.POP.2024.3.FE
#> 14707 PRJ.POP.2024.3.MA
#> 14708 PRJ.POP.2024.3.MF
#> 14709 PRJ.POP.2024.4.FE
#> 14710 PRJ.POP.2024.4.MA
#> 14711 PRJ.POP.2024.4.MF
#> 14712 PRJ.POP.2024.NED.FE
#> 14713 PRJ.POP.2024.NED.MA
#> 14714 PRJ.POP.2024.NED.MF
#> 14715 PRJ.POP.2024.S1.FE
#> 14716 PRJ.POP.2024.S1.MA
#> 14717 PRJ.POP.2024.S1.MF
#> 14718 PRJ.POP.2529.1.FE
#> 14719 PRJ.POP.2529.1.MA
#> 14720 PRJ.POP.2529.1.MF
#> 14721 PRJ.POP.2529.2.FE
#> 14722 PRJ.POP.2529.2.MA
#> 14723 PRJ.POP.2529.2.MF
#> 14724 PRJ.POP.2529.3.FE
#> 14725 PRJ.POP.2529.3.MA
#> 14726 PRJ.POP.2529.3.MF
#> 14727 PRJ.POP.2529.4.FE
#> 14728 PRJ.POP.2529.4.MA
#> 14729 PRJ.POP.2529.4.MF
#> 14730 PRJ.POP.2529.NED.FE
#> 14731 PRJ.POP.2529.NED.MA
#> 14732 PRJ.POP.2529.NED.MF
#> 14733 PRJ.POP.2529.S1.FE
#> 14734 PRJ.POP.2529.S1.MA
#> 14735 PRJ.POP.2529.S1.MF
#> 14736 PRJ.POP.ALL.1.FE
#> 14737 PRJ.POP.ALL.1.MA
#> 14738 PRJ.POP.ALL.1.MF
#> 14739 PRJ.POP.ALL.2.FE
#> 14740 PRJ.POP.ALL.2.MA
#> 14741 PRJ.POP.ALL.2.MF
#> 14742 PRJ.POP.ALL.3.FE
#> 14743 PRJ.POP.ALL.3.MA
#> 14744 PRJ.POP.ALL.3.MF
#> 14745 PRJ.POP.ALL.4.FE
#> 14746 PRJ.POP.ALL.4.MA
#> 14747 PRJ.POP.ALL.4.MF
#> 14748 PRJ.POP.ALL.NED.FE
#> 14749 PRJ.POP.ALL.NED.MA
#> 14750 PRJ.POP.ALL.NED.MF
#> 14751 PRJ.POP.ALL.S1.FE
#> 14752 PRJ.POP.ALL.S1.MA
#> 14753 PRJ.POP.ALL.S1.MF
#> 14841 RAW.D4.1.1.POPU.CENSUS
#> 15186 SE.LITR.15UP.ZS
#> 15278 SE.PRM.CUAT.FE.ZS
#> 15279 SE.PRM.CUAT.MA.ZS
#> 15280 SE.PRM.CUAT.ZS
#> 15501 SE.PRM.NINT.FE.ZS
#> 15502 SE.PRM.NINT.MA.ZS
#> 15503 SE.PRM.NINT.ZS
#> 15761 SE.SEC.CUAT.LO.FE.ZS
#> 15762 SE.SEC.CUAT.LO.MA.ZS
#> 15763 SE.SEC.CUAT.LO.ZS
#> 15764 SE.SEC.CUAT.PO.FE.ZS
#> 15765 SE.SEC.CUAT.PO.MA.ZS
#> 15766 SE.SEC.CUAT.PO.ZS
#> 15767 SE.SEC.CUAT.UP.FE.ZS
#> 15768 SE.SEC.CUAT.UP.MA.ZS
#> 15769 SE.SEC.CUAT.UP.ZS
#> 15841 SE.TER.CUAT.BA.FE.ZS
#> 15842 SE.TER.CUAT.BA.MA.ZS
#> 15843 SE.TER.CUAT.BA.ZS
#> 15844 SE.TER.CUAT.DO.FE.ZS
#> 15845 SE.TER.CUAT.DO.MA.ZS
#> 15846 SE.TER.CUAT.DO.ZS
#> 15847 SE.TER.CUAT.MS.FE.ZS
#> 15848 SE.TER.CUAT.MS.MA.ZS
#> 15849 SE.TER.CUAT.MS.ZS
#> 15850 SE.TER.CUAT.ST.FE.ZS
#> 15851 SE.TER.CUAT.ST.MA.ZS
#> 15852 SE.TER.CUAT.ST.ZS
#> 16251 SH.ADM.INPT
#> 16258 SH.CON.1524.FE.ZS
#> 16259 SH.CON.1524.MA.ZS
#> 16269 SH.DTH.COMM.0004.FE.ZS
#> 16270 SH.DTH.COMM.0004.MA.ZS
#> 16271 SH.DTH.COMM.0004.ZS
#> 16272 SH.DTH.COMM.0514.FE.ZS
#> 16273 SH.DTH.COMM.0514.MA.ZS
#> 16274 SH.DTH.COMM.0514.ZS
#> 16275 SH.DTH.COMM.1559.FE.ZS
#> 16276 SH.DTH.COMM.1559.MA.ZS
#> 16277 SH.DTH.COMM.1559.ZS
#> 16278 SH.DTH.COMM.60UP.FE.ZS
#> 16279 SH.DTH.COMM.60UP.MA.ZS
#> 16280 SH.DTH.COMM.60UP.ZS
#> 16281 SH.DTH.COMM.FE.ZS
#> 16282 SH.DTH.COMM.MA.ZS
#> 16287 SH.DTH.INJR.0004.FE.ZS
#> 16288 SH.DTH.INJR.0004.MA.ZS
#> 16289 SH.DTH.INJR.0004.ZS
#> 16290 SH.DTH.INJR.0514.FE.ZS
#> 16291 SH.DTH.INJR.0514.MA.ZS
#> 16292 SH.DTH.INJR.0514.ZS
#> 16293 SH.DTH.INJR.1559.FE.ZS
#> 16294 SH.DTH.INJR.1559.MA.ZS
#> 16295 SH.DTH.INJR.1559.ZS
#> 16296 SH.DTH.INJR.60UP.FE.ZS
#> 16297 SH.DTH.INJR.60UP.MA.ZS
#> 16298 SH.DTH.INJR.60UP.ZS
#> 16299 SH.DTH.INJR.FE.ZS
#> 16300 SH.DTH.INJR.MA.ZS
#> 16305 SH.DTH.NCOM.0004.FE.ZS
#> 16306 SH.DTH.NCOM.0004.MA.ZS
#> 16307 SH.DTH.NCOM.0004.ZS
#> 16308 SH.DTH.NCOM.0514.FE.ZS
#> 16309 SH.DTH.NCOM.0514.MA.ZS
#> 16310 SH.DTH.NCOM.0514.ZS
#> 16311 SH.DTH.NCOM.1559.FE.ZS
#> 16312 SH.DTH.NCOM.1559.MA.ZS
#> 16313 SH.DTH.NCOM.1559.ZS
#> 16314 SH.DTH.NCOM.60UP.FE.ZS
#> 16315 SH.DTH.NCOM.60UP.MA.ZS
#> 16316 SH.DTH.NCOM.60UP.ZS
#> 16317 SH.DTH.NCOM.FE.ZS
#> 16318 SH.DTH.NCOM.MA.ZS
#> 16330 SH.DYN.AIDS.FE.ZS
#> 16333 SH.DYN.AIDS.ZS
#> 16421 SH.H2O.BASW.Q1.ZS
#> 16422 SH.H2O.BASW.Q2.ZS
#> 16423 SH.H2O.BASW.Q3.ZS
#> 16424 SH.H2O.BASW.Q4.ZS
#> 16425 SH.H2O.BASW.Q5.ZS
#> 16426 SH.H2O.BASW.RU.Q1.ZS
#> 16427 SH.H2O.BASW.RU.Q2.ZS
#> 16428 SH.H2O.BASW.RU.Q3.ZS
#> 16429 SH.H2O.BASW.RU.Q4.ZS
#> 16430 SH.H2O.BASW.RU.Q5.ZS
#> 16431 SH.H2O.BASW.RU.ZS
#> 16432 SH.H2O.BASW.UR.Q1.ZS
#> 16433 SH.H2O.BASW.UR.Q2.ZS
#> 16434 SH.H2O.BASW.UR.Q3.ZS
#> 16435 SH.H2O.BASW.UR.Q4.ZS
#> 16436 SH.H2O.BASW.UR.Q5.ZS
#> 16437 SH.H2O.BASW.UR.ZS
#> 16438 SH.H2O.BASW.ZS
#> 16439 SH.H2O.SAFE.RU.ZS
#> 16440 SH.H2O.SAFE.UR.ZS
#> 16441 SH.H2O.SAFE.ZS
#> 16442 SH.H2O.SMDW.RU.ZS
#> 16443 SH.H2O.SMDW.UR.ZS
#> 16444 SH.H2O.SMDW.ZS
#> 16462 SH.HIV.INCD.50.P3
#> 16463 SH.HIV.INCD.FE.P3
#> 16464 SH.HIV.INCD.MA.P3
#> 16466 SH.HIV.INCD.TL.P3
#> 16468 SH.HIV.INCD.YG.FE.P3
#> 16469 SH.HIV.INCD.YG.MA.P3
#> 16470 SH.HIV.INCD.YG.P3
#> 16471 SH.HIV.INCD.ZS
#> 16505 SH.IMM.CHLD.ZS
#> 16537 SH.MED.NURS.ZS
#> 16539 SH.MED.SAOP.P5
#> 16544 SH.MLR.INCD.P3
#> 16567 SH.MLR.NETS.ZS
#> 16618 SH.SGR.PROC.P5
#> 16619 SH.STA.ACCH.ZS
#> 16620 SH.STA.ACSN
#> 16621 SH.STA.ACSN.RU
#> 16622 SH.STA.ACSN.UR
#> 16623 SH.STA.AIRP.FE.P5
#> 16624 SH.STA.AIRP.MA.P5
#> 16625 SH.STA.AIRP.P5
#> 16655 SH.STA.BASS.Q1.ZS
#> 16656 SH.STA.BASS.Q2.ZS
#> 16657 SH.STA.BASS.Q3.ZS
#> 16658 SH.STA.BASS.Q4.ZS
#> 16659 SH.STA.BASS.Q5.ZS
#> 16660 SH.STA.BASS.RU.Q1.ZS
#> 16661 SH.STA.BASS.RU.Q2.ZS
#> 16662 SH.STA.BASS.RU.Q3.ZS
#> 16663 SH.STA.BASS.RU.Q4.ZS
#> 16664 SH.STA.BASS.RU.Q5.ZS
#> 16665 SH.STA.BASS.RU.ZS
#> 16666 SH.STA.BASS.UR.Q1.ZS
#> 16667 SH.STA.BASS.UR.Q2.ZS
#> 16668 SH.STA.BASS.UR.Q3.ZS
#> 16669 SH.STA.BASS.UR.Q4.ZS
#> 16670 SH.STA.BASS.UR.Q5.ZS
#> 16671 SH.STA.BASS.UR.ZS
#> 16672 SH.STA.BASS.ZS
#> 16696 SH.STA.DIAB.ZS
#> 16714 SH.STA.HYGN.Q1.ZS
#> 16715 SH.STA.HYGN.Q2.ZS
#> 16716 SH.STA.HYGN.Q3.ZS
#> 16717 SH.STA.HYGN.Q4.ZS
#> 16718 SH.STA.HYGN.Q5.ZS
#> 16719 SH.STA.HYGN.RU.Q1.ZS
#> 16720 SH.STA.HYGN.RU.Q2.ZS
#> 16721 SH.STA.HYGN.RU.Q3.ZS
#> 16722 SH.STA.HYGN.RU.Q4.ZS
#> 16723 SH.STA.HYGN.RU.Q5.ZS
#> 16724 SH.STA.HYGN.RU.ZS
#> 16725 SH.STA.HYGN.UR.Q1.ZS
#> 16726 SH.STA.HYGN.UR.Q2.ZS
#> 16727 SH.STA.HYGN.UR.Q3.ZS
#> 16728 SH.STA.HYGN.UR.Q4.ZS
#> 16729 SH.STA.HYGN.UR.Q5.ZS
#> 16730 SH.STA.HYGN.UR.ZS
#> 16731 SH.STA.HYGN.ZS
#> 16754 SH.STA.OB18.FE.ZS
#> 16755 SH.STA.OB18.MA.ZS
#> 16756 SH.STA.ODFC.Q1.ZS
#> 16757 SH.STA.ODFC.Q2.ZS
#> 16758 SH.STA.ODFC.Q3.ZS
#> 16759 SH.STA.ODFC.Q4.ZS
#> 16760 SH.STA.ODFC.Q5.ZS
#> 16761 SH.STA.ODFC.RU.Q1.ZS
#> 16762 SH.STA.ODFC.RU.Q2.ZS
#> 16763 SH.STA.ODFC.RU.Q3.ZS
#> 16764 SH.STA.ODFC.RU.Q4.ZS
#> 16765 SH.STA.ODFC.RU.Q5.ZS
#> 16766 SH.STA.ODFC.RU.ZS
#> 16767 SH.STA.ODFC.UR.Q1.ZS
#> 16768 SH.STA.ODFC.UR.Q2.ZS
#> 16769 SH.STA.ODFC.UR.Q3.ZS
#> 16770 SH.STA.ODFC.UR.Q4.ZS
#> 16771 SH.STA.ODFC.UR.Q5.ZS
#> 16772 SH.STA.ODFC.UR.ZS
#> 16773 SH.STA.ODFC.ZS
#> 16799 SH.STA.POIS.P5
#> 16800 SH.STA.POIS.P5.FE
#> 16801 SH.STA.POIS.P5.MA
#> 16802 SH.STA.SMSS.RU.ZS
#> 16803 SH.STA.SMSS.UR.ZS
#> 16804 SH.STA.SMSS.ZS
#> 16819 SH.STA.SUIC.FE.P5
#> 16820 SH.STA.SUIC.MA.P5
#> 16821 SH.STA.SUIC.P5
#> 16822 SH.STA.TRAF.FE.P5
#> 16823 SH.STA.TRAF.MA.P5
#> 16824 SH.STA.TRAF.P5
#> 16825 SH.STA.WASH.FE.P5
#> 16826 SH.STA.WASH.MA.P5
#> 16827 SH.STA.WASH.P5
#> 16851 SH.TBS.MORT.HG
#> 16852 SH.TBS.MORT.LW
#> 16853 SH.TBS.PREV
#> 16854 SH.TBS.PREV.HG
#> 16855 SH.TBS.PREV.LW
#> 16857 SH.UHC.CONS.ZS
#> 16859 SH.UHC.FBP1.ZS
#> 16861 SH.UHC.FBP2.ZS
#> 16863 SH.UHC.FBPR.ZS
#> 16867 SH.UHC.NOP1.ZS
#> 16871 SH.UHC.NOP2.ZS
#> 16873 SH.UHC.NOPR.ZS
#> 16875 SH.UHC.OOPC.10.ZS
#> 16877 SH.UHC.OOPC.25.ZS
#> 16930 SI.POV.2DAY
#> 16931 SI.POV.ATTM.MI
#> 16933 SI.POV.DDAY
#> 16934 SI.POV.DDAY.14
#> 16935 SI.POV.DDAY.1564
#> 16936 SI.POV.DDAY.16.PL
#> 16937 SI.POV.DDAY.16.PR
#> 16938 SI.POV.DDAY.16.SG
#> 16939 SI.POV.DDAY.16.ST
#> 16940 SI.POV.DDAY.65
#> 16942 SI.POV.DDAY.FE
#> 16943 SI.POV.DDAY.FS
#> 16944 SI.POV.DDAY.MA
#> 16945 SI.POV.DDAY.MI
#> 16946 SI.POV.DDAY.RU
#> 16947 SI.POV.DDAY.SG
#> 16948 SI.POV.DDAY.SH
#> 16949 SI.POV.DDAY.TH
#> 16950 SI.POV.DDAY.UR
#> 16951 SI.POV.ELEC.MI
#> 16952 SI.POV.ENRL.MI
#> 16959 SI.POV.HCRT.MI
#> 16960 SI.POV.LMIC
#> 16961 SI.POV.LMIC.FS
#> 16964 SI.POV.LMIC.SG
#> 16965 SI.POV.LMIC.TH
#> 16966 SI.POV.MDIM
#> 16967 SI.POV.MDIM.17
#> 16968 SI.POV.MDIM.17.XQ
#> 16969 SI.POV.MDIM.FE
#> 16972 SI.POV.MDIM.MA
#> 16976 SI.POV.NAHC
#> 16977 SI.POV.NAHC.NC
#> 16979 SI.POV.NAPR.ZS
#> 16985 SI.POV.RUHC
#> 16986 SI.POV.SANI.MI
#> 16987 SI.POV.UMIC
#> 16988 SI.POV.UMIC.FS
#> 16991 SI.POV.UMIC.SG
#> 16992 SI.POV.UMIC.TH
#> 16994 SI.POV.URHC
#> 16995 SI.POV.WATR.MI
#> 17001 SI.SPR.BL50.ZS
#> 17002 SI.SPR.PC40
#> 17004 SI.SPR.PC40.ZG
#> 17005 SI.SPR.PCAP
#> 17006 SI.SPR.PCAP.05
#> 17007 SI.SPR.PCAP.ZG
#> 17023 SL.EMP.1524.SP.FE.NE.ZS
#> 17024 SL.EMP.1524.SP.FE.ZS
#> 17025 SL.EMP.1524.SP.MA.NE.ZS
#> 17026 SL.EMP.1524.SP.MA.ZS
#> 17027 SL.EMP.1524.SP.NE.ZS
#> 17028 SL.EMP.1524.SP.ZS
#> 17050 SL.EMP.TOTL.SP.FE.NE.ZS
#> 17051 SL.EMP.TOTL.SP.FE.ZS
#> 17052 SL.EMP.TOTL.SP.MA.NE.ZS
#> 17053 SL.EMP.TOTL.SP.MA.ZS
#> 17054 SL.EMP.TOTL.SP.NE.ZS
#> 17055 SL.EMP.TOTL.SP.ZS
#> 17127 SL.TLF.ACTI.FE.ZS
#> 17128 SL.TLF.ACTI.MA.ZS
#> 17129 SL.TLF.ACTI.ZS
#> 17130 SL.TLF.ADVN.FE.ZS
#> 17131 SL.TLF.ADVN.MA.ZS
#> 17132 SL.TLF.ADVN.ZS
#> 17133 SL.TLF.BASC.FE.ZS
#> 17134 SL.TLF.BASC.MA.ZS
#> 17135 SL.TLF.BASC.ZS
#> 17136 SL.TLF.CACT.2534.FE.ZS
#> 17137 SL.TLF.CACT.2534.MA.ZS
#> 17138 SL.TLF.CACT.2534.ZS
#> 17139 SL.TLF.CACT.2554.FE.ZS
#> 17140 SL.TLF.CACT.2554.MA.ZS
#> 17141 SL.TLF.CACT.2554.ZS
#> 17142 SL.TLF.CACT.3554.FE.ZS
#> 17143 SL.TLF.CACT.3554.MA.ZS
#> 17144 SL.TLF.CACT.3554.ZS
#> 17145 SL.TLF.CACT.5564.FE.ZS
#> 17146 SL.TLF.CACT.5564.MA.ZS
#> 17147 SL.TLF.CACT.5564.ZS
#> 17148 SL.TLF.CACT.65UP.FE.ZS
#> 17149 SL.TLF.CACT.65UP.MA.ZS
#> 17150 SL.TLF.CACT.65UP.ZS
#> 17151 SL.TLF.CACT.FE.NE.ZS
#> 17152 SL.TLF.CACT.FE.ZS
#> 17155 SL.TLF.CACT.MA.NE.ZS
#> 17156 SL.TLF.CACT.MA.ZS
#> 17157 SL.TLF.CACT.NE.ZS
#> 17158 SL.TLF.CACT.ZS
#> 17161 SL.TLF.INTM.FE.ZS
#> 17162 SL.TLF.INTM.MA.ZS
#> 17163 SL.TLF.INTM.ZS
#> 17204 SL.UEM.NEET.FE.ZS
#> 17205 SL.UEM.NEET.MA.ZS
#> 17206 SL.UEM.NEET.ZS
#> 17226 SM.EMI.TERT.ZS
#> 17228 SM.POP.FRGN
#> 17229 SM.POP.FRGN.ZS
#> 17231 SM.POP.IFRN
#> 17233 SM.POP.REFG
#> 17234 SM.POP.REFG.OR
#> 17236 SM.POP.TOTL.ZS
#> 17240 SN.ITK.DEFC.POP
#> 17241 SN.ITK.DEFC.ZS
#> 17244 SN.ITK.MSFI.ZS
#> 17246 SN.ITK.SVFI.ZS
#> 17267 SP.BRT.CRUD.ZT
#> 17274 SP.DYN.CBRT
#> 17342 SP.POP.0004.FE
#> 17343 SP.POP.0004.FE.5Y
#> 17344 SP.POP.0004.MA
#> 17345 SP.POP.0004.MA.5Y
#> 17346 SP.POP.0014.FE.IN
#> 17347 SP.POP.0014.FE.ZS
#> 17348 SP.POP.0014.MA.IN
#> 17349 SP.POP.0014.MA.ZS
#> 17350 SP.POP.0014.TO
#> 17351 SP.POP.0014.TO.ZS
#> 17352 SP.POP.0024.TO.ZS
#> 17353 SP.POP.0305.FE.UN
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#> 17355 SP.POP.0305.TO.UN
#> 17356 SP.POP.0406.FE.UN
#> 17357 SP.POP.0406.MA.UN
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#> 17359 SP.POP.0509.FE
#> 17360 SP.POP.0509.FE.5Y
#> 17361 SP.POP.0509.FE.UN
#> 17362 SP.POP.0509.MA
#> 17363 SP.POP.0509.MA.5Y
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#> 17372 SP.POP.0609.FE.UN
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#> 17381 SP.POP.0612.FE.UN
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#> 17384 SP.POP.0709.FE.UN
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#> 17396 SP.POP.0713.FE.UN
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#> 17398 SP.POP.0713.TO.UN
#> 17399 SP.POP.1014.FE
#> 17400 SP.POP.1014.FE.5Y
#> 17401 SP.POP.1014.FE.UN
#> 17402 SP.POP.1014.MA
#> 17403 SP.POP.1014.MA.5Y
#> 17404 SP.POP.1014.MA.UN
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#> 17406 SP.POP.1015.FE.UN
#> 17407 SP.POP.1015.MA.UN
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#> 17409 SP.POP.1016.FE.UN
#> 17410 SP.POP.1016.MA.UN
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#> 17412 SP.POP.1017.FE.UN
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#> 17418 SP.POP.1115.FE.UN
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#> 17430 SP.POP.1215.FE.UN
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#> 17445 SP.POP.1317.FE.UN
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#> 17460 SP.POP.1519.FE
#> 17461 SP.POP.1519.FE.5Y
#> 17462 SP.POP.1519.MA
#> 17463 SP.POP.1519.MA.5Y
#> 17464 SP.POP.1524.FE.UN
#> 17465 SP.POP.1524.MA.UN
#> 17466 SP.POP.1524.TO.UN
#> 17467 SP.POP.1564.FE.IN
#> 17468 SP.POP.1564.FE.ZS
#> 17469 SP.POP.1564.IN
#> 17470 SP.POP.1564.IN.ZS
#> 17471 SP.POP.1564.MA.IN
#> 17472 SP.POP.1564.MA.ZS
#> 17473 SP.POP.1564.TO
#> 17474 SP.POP.1564.TO.ZS
#> 17475 SP.POP.2024.FE
#> 17476 SP.POP.2024.FE.5Y
#> 17477 SP.POP.2024.MA
#> 17478 SP.POP.2024.MA.5Y
#> 17479 SP.POP.2529.FE
#> 17480 SP.POP.2529.FE.5Y
#> 17481 SP.POP.2529.MA
#> 17482 SP.POP.2529.MA.5Y
#> 17483 SP.POP.3034.FE
#> 17484 SP.POP.3034.FE.5Y
#> 17485 SP.POP.3034.MA
#> 17486 SP.POP.3034.MA.5Y
#> 17487 SP.POP.3539.FE
#> 17488 SP.POP.3539.FE.5Y
#> 17489 SP.POP.3539.MA
#> 17490 SP.POP.3539.MA.5Y
#> 17491 SP.POP.4044.FE
#> 17492 SP.POP.4044.FE.5Y
#> 17493 SP.POP.4044.MA
#> 17494 SP.POP.4044.MA.5Y
#> 17495 SP.POP.4549.FE
#> 17496 SP.POP.4549.FE.5Y
#> 17497 SP.POP.4549.MA
#> 17498 SP.POP.4549.MA.5Y
#> 17499 SP.POP.5054.FE
#> 17500 SP.POP.5054.FE.5Y
#> 17501 SP.POP.5054.MA
#> 17502 SP.POP.5054.MA.5Y
#> 17503 SP.POP.5559.FE
#> 17504 SP.POP.5559.FE.5Y
#> 17505 SP.POP.5559.MA
#> 17506 SP.POP.5559.MA.5Y
#> 17507 SP.POP.6064.FE
#> 17508 SP.POP.6064.FE.5Y
#> 17509 SP.POP.6064.MA
#> 17510 SP.POP.6064.MA.5Y
#> 17511 SP.POP.6569.FE
#> 17512 SP.POP.6569.FE.5Y
#> 17513 SP.POP.6569.MA
#> 17514 SP.POP.6569.MA.5Y
#> 17515 SP.POP.65UP.FE.IN
#> 17516 SP.POP.65UP.FE.ZS
#> 17517 SP.POP.65UP.MA.IN
#> 17518 SP.POP.65UP.MA.ZS
#> 17520 SP.POP.65UP.TO
#> 17521 SP.POP.65UP.TO.ZS
#> 17522 SP.POP.7074.FE
#> 17523 SP.POP.7074.FE.5Y
#> 17524 SP.POP.7074.MA
#> 17525 SP.POP.7074.MA.5Y
#> 17526 SP.POP.7579.FE
#> 17527 SP.POP.7579.FE.5Y
#> 17528 SP.POP.7579.MA
#> 17529 SP.POP.7579.MA.5Y
#> 17530 SP.POP.80UP.FE
#> 17531 SP.POP.80UP.FE.5Y
#> 17532 SP.POP.80UP.MA
#> 17533 SP.POP.80UP.MA.5Y
#> 17534 SP.POP.AG00.FE.IN
#> 17535 SP.POP.AG00.FE.UN
#> 17536 SP.POP.AG00.MA.IN
#> 17537 SP.POP.AG00.MA.UN
#> 17538 SP.POP.AG00.TO.UN
#> 17539 SP.POP.AG01.FE.IN
#> 17540 SP.POP.AG01.FE.UN
#> 17541 SP.POP.AG01.MA.IN
#> 17542 SP.POP.AG01.MA.UN
#> 17543 SP.POP.AG01.TO.UN
#> 17544 SP.POP.AG02.FE.IN
#> 17545 SP.POP.AG02.FE.UN
#> 17546 SP.POP.AG02.MA.IN
#> 17547 SP.POP.AG02.MA.UN
#> 17548 SP.POP.AG02.TO.UN
#> 17549 SP.POP.AG03.FE.IN
#> 17550 SP.POP.AG03.FE.UN
#> 17551 SP.POP.AG03.MA.IN
#> 17552 SP.POP.AG03.MA.UN
#> 17553 SP.POP.AG03.TO.UN
#> 17554 SP.POP.AG04.FE.IN
#> 17555 SP.POP.AG04.FE.UN
#> 17556 SP.POP.AG04.MA.IN
#> 17557 SP.POP.AG04.MA.UN
#> 17558 SP.POP.AG04.TO.UN
#> 17559 SP.POP.AG05.FE.IN
#> 17560 SP.POP.AG05.FE.UN
#> 17561 SP.POP.AG05.MA.IN
#> 17562 SP.POP.AG05.MA.UN
#> 17563 SP.POP.AG05.TO.UN
#> 17564 SP.POP.AG06.FE.IN
#> 17565 SP.POP.AG06.FE.UN
#> 17566 SP.POP.AG06.MA.IN
#> 17567 SP.POP.AG06.MA.UN
#> 17568 SP.POP.AG06.TO.UN
#> 17569 SP.POP.AG07.FE.IN
#> 17570 SP.POP.AG07.FE.UN
#> 17571 SP.POP.AG07.MA.IN
#> 17572 SP.POP.AG07.MA.UN
#> 17573 SP.POP.AG07.TO.UN
#> 17574 SP.POP.AG08.FE.IN
#> 17575 SP.POP.AG08.FE.UN
#> 17576 SP.POP.AG08.MA.IN
#> 17577 SP.POP.AG08.MA.UN
#> 17578 SP.POP.AG08.TO.UN
#> 17579 SP.POP.AG09.FE.IN
#> 17580 SP.POP.AG09.FE.UN
#> 17581 SP.POP.AG09.MA.IN
#> 17582 SP.POP.AG09.MA.UN
#> 17583 SP.POP.AG09.TO.UN
#> 17584 SP.POP.AG10.FE.IN
#> 17585 SP.POP.AG10.FE.UN
#> 17586 SP.POP.AG10.MA.IN
#> 17587 SP.POP.AG10.MA.UN
#> 17588 SP.POP.AG10.TO.UN
#> 17589 SP.POP.AG11.FE.IN
#> 17590 SP.POP.AG11.FE.UN
#> 17591 SP.POP.AG11.MA.IN
#> 17592 SP.POP.AG11.MA.UN
#> 17593 SP.POP.AG11.TO.UN
#> 17594 SP.POP.AG12.FE.IN
#> 17595 SP.POP.AG12.FE.UN
#> 17596 SP.POP.AG12.MA.IN
#> 17597 SP.POP.AG12.MA.UN
#> 17598 SP.POP.AG12.TO.UN
#> 17599 SP.POP.AG13.FE.IN
#> 17600 SP.POP.AG13.FE.UN
#> 17601 SP.POP.AG13.MA.IN
#> 17602 SP.POP.AG13.MA.UN
#> 17603 SP.POP.AG13.TO.UN
#> 17604 SP.POP.AG14.FE.IN
#> 17605 SP.POP.AG14.FE.UN
#> 17606 SP.POP.AG14.MA.IN
#> 17607 SP.POP.AG14.MA.UN
#> 17608 SP.POP.AG14.TO.UN
#> 17609 SP.POP.AG15.FE.IN
#> 17610 SP.POP.AG15.FE.UN
#> 17611 SP.POP.AG15.MA.IN
#> 17612 SP.POP.AG15.MA.UN
#> 17613 SP.POP.AG15.TO.UN
#> 17614 SP.POP.AG16.FE.IN
#> 17615 SP.POP.AG16.FE.UN
#> 17616 SP.POP.AG16.MA.IN
#> 17617 SP.POP.AG16.MA.UN
#> 17618 SP.POP.AG16.TO.UN
#> 17619 SP.POP.AG17.FE.IN
#> 17620 SP.POP.AG17.FE.UN
#> 17621 SP.POP.AG17.MA.IN
#> 17622 SP.POP.AG17.MA.UN
#> 17623 SP.POP.AG17.TO.UN
#> 17624 SP.POP.AG18.FE.IN
#> 17625 SP.POP.AG18.FE.UN
#> 17626 SP.POP.AG18.MA.IN
#> 17627 SP.POP.AG18.MA.UN
#> 17628 SP.POP.AG18.TO.UN
#> 17629 SP.POP.AG19.FE.IN
#> 17630 SP.POP.AG19.FE.UN
#> 17631 SP.POP.AG19.MA.IN
#> 17632 SP.POP.AG19.MA.UN
#> 17633 SP.POP.AG19.TO.UN
#> 17634 SP.POP.AG20.FE.IN
#> 17635 SP.POP.AG20.FE.UN
#> 17636 SP.POP.AG20.MA.IN
#> 17637 SP.POP.AG20.MA.UN
#> 17638 SP.POP.AG20.TO.UN
#> 17639 SP.POP.AG21.FE.IN
#> 17640 SP.POP.AG21.FE.UN
#> 17641 SP.POP.AG21.MA.IN
#> 17642 SP.POP.AG21.MA.UN
#> 17643 SP.POP.AG21.TO.UN
#> 17644 SP.POP.AG22.FE.IN
#> 17645 SP.POP.AG22.FE.UN
#> 17646 SP.POP.AG22.MA.IN
#> 17647 SP.POP.AG22.MA.UN
#> 17648 SP.POP.AG22.TO.UN
#> 17649 SP.POP.AG23.FE.IN
#> 17650 SP.POP.AG23.FE.UN
#> 17651 SP.POP.AG23.MA.IN
#> 17652 SP.POP.AG23.MA.UN
#> 17653 SP.POP.AG23.TO.UN
#> 17654 SP.POP.AG24.FE.IN
#> 17655 SP.POP.AG24.FE.UN
#> 17656 SP.POP.AG24.MA.IN
#> 17657 SP.POP.AG24.MA.UN
#> 17658 SP.POP.AG24.TO.UN
#> 17659 SP.POP.AG25.FE.IN
#> 17660 SP.POP.AG25.FE.UN
#> 17661 SP.POP.AG25.MA.IN
#> 17662 SP.POP.AG25.MA.UN
#> 17663 SP.POP.AG25.TO.UN
#> 17665 SP.POP.DPND
#> 17666 SP.POP.DPND.OL
#> 17667 SP.POP.DPND.YG
#> 17668 SP.POP.GROW
#> 17669 SP.POP.LAND.ZS
#> 17674 SP.POP.TOTL
#> 17675 SP.POP.TOTL.FE.IN
#> 17676 SP.POP.TOTL.FE.ZS
#> 17677 SP.POP.TOTL.ICP
#> 17678 SP.POP.TOTL.ICP.ZS
#> 17679 SP.POP.TOTL.MA.IN
#> 17680 SP.POP.TOTL.MA.ZS
#> 17681 SP.POP.TOTL.ZS
#> 17682 SP.PRE.TOTL.FE.IN
#> 17683 SP.PRE.TOTL.IN
#> 17684 SP.PRE.TOTL.MA.IN
#> 17685 SP.PRM.GRAD.FE
#> 17686 SP.PRM.GRAD.MA
#> 17687 SP.PRM.GRAD.TO
#> 17688 SP.PRM.TOTL.FE.IN
#> 17689 SP.PRM.TOTL.IN
#> 17690 SP.PRM.TOTL.MA.IN
#> 17702 SP.RUR.TOTL
#> 17703 SP.RUR.TOTL.FE.ZS
#> 17704 SP.RUR.TOTL.MA.ZS
#> 17705 SP.RUR.TOTL.ZG
#> 17706 SP.RUR.TOTL.ZS
#> 17707 SP.SEC.LTOT.FE.IN
#> 17708 SP.SEC.LTOT.IN
#> 17709 SP.SEC.LTOT.MA.IN
#> 17710 SP.SEC.TOTL.FE.IN
#> 17711 SP.SEC.TOTL.IN
#> 17712 SP.SEC.TOTL.MA.IN
#> 17713 SP.SEC.UTOT.FE.IN
#> 17714 SP.SEC.UTOT.IN
#> 17715 SP.SEC.UTOT.MA.IN
#> 17716 SP.TER.TOTL.FE.IN
#> 17717 SP.TER.TOTL.IN
#> 17718 SP.TER.TOTL.MA.IN
#> 17719 SP.URB.GROW
#> 17720 SP.URB.LCTY
#> 17721 SP.URB.LCTY.UR.ZS
#> 17722 SP.URB.MCTY
#> 17723 SP.URB.MCTY.UR.ZS
#> 17724 SP.URB.TOTL
#> 17725 SP.URB.TOTL.FE.ZS
#> 17726 SP.URB.TOTL.IN.ZS
#> 17727 SP.URB.TOTL.MA.ZS
#> 17728 SP.URB.TOTL.ZS
#> 17771 SPI.D4.1.1.POPU
#> 18453 UIS.EA.1.AG25T99
#> 18454 UIS.EA.1.AG25T99.F
#> 18455 UIS.EA.1.AG25T99.M
#> 18456 UIS.EA.1T6.AG25T99
#> 18457 UIS.EA.1T6.AG25T99.F
#> 18458 UIS.EA.1T6.AG25T99.M
#> 18459 UIS.EA.1T8.AG25T99.GPIA
#> 18460 UIS.EA.2.AG25T99
#> 18461 UIS.EA.2.AG25T99.F
#> 18462 UIS.EA.2.AG25T99.M
#> 18463 UIS.EA.2T6.AG25T99
#> 18464 UIS.EA.2T6.AG25T99.F
#> 18465 UIS.EA.2T6.AG25T99.M
#> 18466 UIS.EA.2T8.AG25T99.GPIA
#> 18467 UIS.EA.3.AG25T99
#> 18468 UIS.EA.3.AG25T99.F
#> 18469 UIS.EA.3.AG25T99.M
#> 18470 UIS.EA.3T6.AG25T99
#> 18471 UIS.EA.3T6.AG25T99.F
#> 18472 UIS.EA.3T6.AG25T99.M
#> 18473 UIS.EA.3T8.AG25T99.GPIA
#> 18474 UIS.EA.4.AG25T99
#> 18475 UIS.EA.4.AG25T99.F
#> 18476 UIS.EA.4.AG25T99.M
#> 18477 UIS.EA.4T6.AG25T99
#> 18478 UIS.EA.4T6.AG25T99.F
#> 18479 UIS.EA.4T6.AG25T99.M
#> 18480 UIS.EA.4T8.AG25T99.GPIA
#> 18481 UIS.EA.5.AG25T99
#> 18482 UIS.EA.5.AG25T99.F
#> 18483 UIS.EA.5.AG25T99.M
#> 18484 UIS.EA.5T8.AG25T99
#> 18485 UIS.EA.5T8.AG25T99.F
#> 18486 UIS.EA.5T8.AG25T99.GPIA
#> 18487 UIS.EA.5T8.AG25T99.M
#> 18488 UIS.EA.6.AG25T99
#> 18489 UIS.EA.6.AG25T99.F
#> 18490 UIS.EA.6.AG25T99.M
#> 18491 UIS.EA.6T8.AG25T99
#> 18492 UIS.EA.6T8.AG25T99.F
#> 18493 UIS.EA.6T8.AG25T99.GPIA
#> 18494 UIS.EA.6T8.AG25T99.M
#> 18495 UIS.EA.7.AG25T99
#> 18496 UIS.EA.7.AG25T99.F
#> 18497 UIS.EA.7.AG25T99.M
#> 18498 UIS.EA.7T8.AG25T99
#> 18499 UIS.EA.7T8.AG25T99.F
#> 18500 UIS.EA.7T8.AG25T99.GPIA
#> 18501 UIS.EA.7T8.AG25T99.M
#> 18502 UIS.EA.8.AG25T99
#> 18503 UIS.EA.8.AG25T99.F
#> 18504 UIS.EA.8.AG25T99.GPIA
#> 18505 UIS.EA.8.AG25T99.M
#> 18506 UIS.EA.MEAN.1T6.AG25T99
#> 18507 UIS.EA.MEAN.1T6.AG25T99.F
#> 18508 UIS.EA.MEAN.1T6.AG25T99.M
#> 18509 UIS.EA.NS.AG25T99
#> 18510 UIS.EA.NS.AG25T99.F
#> 18511 UIS.EA.NS.AG25T99.M
#> 18512 UIS.EA.S1.AG25T99
#> 18513 UIS.EA.S1.AG25T99.F
#> 18514 UIS.EA.S1.AG25T99.M
#> 18515 UIS.EA.S1T8.AG25T99
#> 18516 UIS.EA.S1T8.AG25T99.F
#> 18517 UIS.EA.S1T8.AG25T99.GPIA
#> 18518 UIS.EA.S1T8.AG25T99.M
#> 18519 UIS.EA.UK.AG25T99
#> 18520 UIS.EA.UK.AG25T99.F
#> 18521 UIS.EA.UK.AG25T99.M
#> 18776 UIS.ILLPOP.AG25T64
#> 18777 UIS.ILLPOP.AG25T64.F
#> 18778 UIS.ILLPOP.AG25T64.M
#> 18779 UIS.ILLPOPF.AG25T64
#> 18780 UIS.LP.AG15T24
#> 18781 UIS.LP.AG15T24.F
#> 18782 UIS.LP.AG15T24.M
#> 18783 UIS.LP.AG15T99
#> 18784 UIS.LP.AG15T99.F
#> 18785 UIS.LP.AG15T99.M
#> 18786 UIS.LP.AG65
#> 18787 UIS.LP.AG65.F
#> 18788 UIS.LP.AG65.M
#> 18789 UIS.LPP.AG15T24
#> 18790 UIS.LPP.AG15T99
#> 18791 UIS.LPP.AG65
#> 18792 UIS.LR.AG15T24.F.LPIA
#> 18793 UIS.LR.AG15T24.GPIA
#> 18794 UIS.LR.AG15T24.LPIA
#> 18795 UIS.LR.AG15T24.M.LPIA
#> 18796 UIS.LR.AG15T24.RUR
#> 18797 UIS.LR.AG15T24.RUR.F
#> 18798 UIS.LR.AG15T24.RUR.GPIA
#> 18799 UIS.LR.AG15T24.RUR.M
#> 18800 UIS.LR.AG15T24.URB
#> 18801 UIS.LR.AG15T24.URB.F
#> 18802 UIS.LR.AG15T24.URB.GPIA
#> 18803 UIS.LR.AG15T24.URB.M
#> 18804 UIS.LR.AG15T99.F.LPIA
#> 18805 UIS.LR.AG15T99.GPIA
#> 18806 UIS.LR.AG15T99.LPIA
#> 18807 UIS.LR.AG15T99.M.LPIA
#> 18808 UIS.LR.AG15T99.RUR
#> 18809 UIS.LR.AG15T99.RUR.F
#> 18810 UIS.LR.AG15T99.RUR.GPIA
#> 18811 UIS.LR.AG15T99.RUR.M
#> 18812 UIS.LR.AG15T99.URB
#> 18813 UIS.LR.AG15T99.URB.F
#> 18814 UIS.LR.AG15T99.URB.GPIA
#> 18815 UIS.LR.AG15T99.URB.M
#> 18816 UIS.LR.AG25T64
#> 18817 UIS.LR.AG25T64.F
#> 18818 UIS.LR.AG25T64.F.LPIA
#> 18819 UIS.LR.AG25T64.GPIA
#> 18820 UIS.LR.AG25T64.LPIA
#> 18821 UIS.LR.AG25T64.M
#> 18822 UIS.LR.AG25T64.M.LPIA
#> 18823 UIS.LR.AG25T64.RUR
#> 18824 UIS.LR.AG25T64.RUR.F
#> 18825 UIS.LR.AG25T64.RUR.GPIA
#> 18826 UIS.LR.AG25T64.RUR.M
#> 18827 UIS.LR.AG25T64.URB
#> 18828 UIS.LR.AG25T64.URB.F
#> 18829 UIS.LR.AG25T64.URB.GPIA
#> 18830 UIS.LR.AG25T64.URB.M
#> 18831 UIS.LR.AG65
#> 18832 UIS.LR.AG65.F
#> 18833 UIS.LR.AG65.M
#> 18834 UIS.LR.AG65T99.F.LPIA
#> 18835 UIS.LR.AG65T99.GPIA
#> 18836 UIS.LR.AG65T99.LPIA
#> 18837 UIS.LR.AG65T99.M.LPIA
#> 18838 UIS.LR.AG65T99.RUR
#> 18839 UIS.LR.AG65T99.RUR.F
#> 18840 UIS.LR.AG65T99.RUR.GPIA
#> 18841 UIS.LR.AG65T99.RUR.M
#> 18842 UIS.LR.AG65T99.URB
#> 18843 UIS.LR.AG65T99.URB.F
#> 18844 UIS.LR.AG65T99.URB.GPIA
#> 18845 UIS.LR.AG65T99.URB.M
#> 19358 UIS.PLILLITP
#> 19359 UIS.PLILLITP.F
#> 19360 UIS.PLILLITP.M
#> 19876 UIS.SAP.0
#> 19877 UIS.SAP.0.F
#> 19878 UIS.SAP.0.M
#> 19879 UIS.SAP.01
#> 19880 UIS.SAP.01.F
#> 19881 UIS.SAP.01.M
#> 19882 UIS.SAP.1.AGM1
#> 19883 UIS.SAP.1.AGM1.F
#> 19884 UIS.SAP.1.AGM1.M
#> 19885 UIS.SAP.1.G1
#> 19886 UIS.SAP.1.G1.F
#> 19887 UIS.SAP.1.G1.M
#> 19888 UIS.SAP.23.GPV.G1
#> 19889 UIS.SAP.23.GPV.G1.F
#> 19890 UIS.SAP.23.GPV.G1.M
#> 19891 UIS.SAP.4
#> 19892 UIS.SAP.4.F
#> 19893 UIS.SAP.4.M
#> 19894 UIS.SAP.CE
#> 19895 UIS.SAP.CE.F
#> 19896 UIS.SAP.CE.M
#> 20141 UIS.YADULT.PROFILITERACY
#> 20142 UIS.YADULT.PROFILITERACY.F
#> 20143 UIS.YADULT.PROFILITERACY.GPIA
#> 20144 UIS.YADULT.PROFILITERACY.HSES
#> 20145 UIS.YADULT.PROFILITERACY.LSES
#> 20146 UIS.YADULT.PROFILITERACY.M
#> 20147 UIS.YADULT.PROFILITERACY.NAT
#> 20148 UIS.YADULT.PROFILITERACY.NON
#> 20149 UIS.YADULT.PROFILITERACY.NPIA
#> 20150 UIS.YADULT.PROFILITERACY.WPIA
#> 20151 UIS.YADULT.PROFINUMERACY
#> 20152 UIS.YADULT.PROFINUMERACY.F
#> 20153 UIS.YADULT.PROFINUMERACY.GPIA
#> 20154 UIS.YADULT.PROFINUMERACY.HSES
#> 20155 UIS.YADULT.PROFINUMERACY.LSES
#> 20156 UIS.YADULT.PROFINUMERACY.M
#> 20157 UIS.YADULT.PROFINUMERACY.NAT
#> 20158 UIS.YADULT.PROFINUMERACY.NON
#> 20159 UIS.YADULT.PROFINUMERACY.NPIA
#> 20160 UIS.YADULT.PROFINUMERACY.WPIA
#> name
#> 25 Access to electricity (% of total population)
#> 40 Access to electricity (% of rural population)
#> 41 Access to electricity (% of urban population)
#> 165 Access to Clean Fuels and Technologies for cooking (% of total population)
#> 199 Population census
#> 1173 Coverage of social safety net programs (% of population)
#> 1176 Coverage of social insurance programs (% of population)
#> 1179 Coverage of social protection and labor programs (% of population)
#> 1197 Barro-Lee: Percentage of female population age 15-19 with no education
#> 1198 Barro-Lee: Percentage of population age 15-19 with no education
#> 1199 Barro-Lee: Percentage of female population age 15+ with no education
#> 1200 Barro-Lee: Percentage of population age 15+ with no education
#> 1201 Barro-Lee: Percentage of female population age 20-24 with no education
#> 1202 Barro-Lee: Percentage of population age 20-24 with no education
#> 1203 Barro-Lee: Percentage of female population age 25-29 with no education
#> 1204 Barro-Lee: Percentage of population age 25-29 with no education
#> 1205 Barro-Lee: Percentage of female population age 25+ with no education
#> 1206 Barro-Lee: Percentage of population age 25+ with no education
#> 1207 Barro-Lee: Percentage of female population age 30-34 with no education
#> 1208 Barro-Lee: Percentage of population age 30-34 with no education
#> 1209 Barro-Lee: Percentage of female population age 35-39 with no education
#> 1210 Barro-Lee: Percentage of population age 35-39 with no education
#> 1211 Barro-Lee: Percentage of female population age 40-44 with no education
#> 1212 Barro-Lee: Percentage of population age 40-44 with no education
#> 1213 Barro-Lee: Percentage of female population age 45-49 with no education
#> 1214 Barro-Lee: Percentage of population age 45-49 with no education
#> 1215 Barro-Lee: Percentage of female population age 50-54 with no education
#> 1216 Barro-Lee: Percentage of population age 50-54 with no education
#> 1217 Barro-Lee: Percentage of female population age 55-59 with no education
#> 1218 Barro-Lee: Percentage of population age 55-59 with no education
#> 1219 Barro-Lee: Percentage of female population age 60-64 with no education
#> 1220 Barro-Lee: Percentage of population age 60-64 with no education
#> 1221 Barro-Lee: Percentage of female population age 65-69 with no education
#> 1222 Barro-Lee: Percentage of population age 65-69 with no education
#> 1223 Barro-Lee: Percentage of female population age 70-74 with no education
#> 1224 Barro-Lee: Percentage of population age 70-74 with no education
#> 1225 Barro-Lee: Percentage of female population age 75+ with no education
#> 1226 Barro-Lee: Percentage of population age 75+ with no education
#> 1227 Barro-Lee: Population in thousands, age 15-19, total
#> 1228 Barro-Lee: Population in thousands, age 15-19, female
#> 1229 Barro-Lee: Population in thousands, age 15+, total
#> 1230 Barro-Lee: Population in thousands, age 15+, female
#> 1231 Barro-Lee: Population in thousands, age 20-24, total
#> 1232 Barro-Lee: Population in thousands, age 20-24, female
#> 1233 Barro-Lee: Population in thousands, age 25-29, total
#> 1234 Barro-Lee: Population in thousands, age 25-29, female
#> 1235 Barro-Lee: Population in thousands, age 25+, total
#> 1236 Barro-Lee: Population in thousands, age 25+, female
#> 1237 Barro-Lee: Population in thousands, age 30-34, total
#> 1238 Barro-Lee: Population in thousands, age 30-34, female
#> 1239 Barro-Lee: Population in thousands, age 35-39, total
#> 1240 Barro-Lee: Population in thousands, age 35-39, female
#> 1241 Barro-Lee: Population in thousands, age 40-44, total
#> 1242 Barro-Lee: Population in thousands, age 40-44, female
#> 1243 Barro-Lee: Population in thousands, age 45-49, total
#> 1244 Barro-Lee: Population in thousands, age 45-49, female
#> 1245 Barro-Lee: Population in thousands, age 50-54, total
#> 1246 Barro-Lee: Population in thousands, age 50-54, female
#> 1247 Barro-Lee: Population in thousands, age 55-59, total
#> 1248 Barro-Lee: Population in thousands, age 55-59, female
#> 1249 Barro-Lee: Population in thousands, age 60-64, total
#> 1250 Barro-Lee: Population in thousands, age 60-64, female
#> 1251 Barro-Lee: Population in thousands, age 65-69, total
#> 1252 Barro-Lee: Population in thousands, age 65-69, female
#> 1253 Barro-Lee: Population in thousands, age 70-74, total
#> 1254 Barro-Lee: Population in thousands, age 70-74, female
#> 1255 Barro-Lee: Population in thousands, age 75+, total
#> 1256 Barro-Lee: Population in thousands, age 75+, female
#> 1257 Barro-Lee: Percentage of female population age 15-19 with primary schooling. Completed Primary
#> 1258 Barro-Lee: Percentage of population age 15-19 with primary schooling. Completed Primary
#> 1259 Barro-Lee: Percentage of female population age 15+ with primary schooling. Completed Primary
#> 1260 Barro-Lee: Percentage of population age 15+ with primary schooling. Completed Primary
#> 1261 Barro-Lee: Percentage of female population age 20-24 with primary schooling. Completed Primary
#> 1262 Barro-Lee: Percentage of population age 20-24 with primary schooling. Completed Primary
#> 1263 Barro-Lee: Percentage of female population age 25-29 with primary schooling. Completed Primary
#> 1264 Barro-Lee: Percentage of population age 25-29 with primary schooling. Completed Primary
#> 1265 Barro-Lee: Percentage of female population age 25+ with primary schooling. Completed Primary
#> 1266 Barro-Lee: Percentage of population age 25+ with primary schooling. Completed Primary
#> 1267 Barro-Lee: Percentage of female population age 30-34 with primary schooling. Completed Primary
#> 1268 Barro-Lee: Percentage of population age 30-34 with primary schooling. Completed Primary
#> 1269 Barro-Lee: Percentage of female population age 35-39 with primary schooling. Completed Primary
#> 1270 Barro-Lee: Percentage of population age 35-39 with primary schooling. Completed Primary
#> 1271 Barro-Lee: Percentage of female population age 40-44 with primary schooling. Completed Primary
#> 1272 Barro-Lee: Percentage of population age 40-44 with primary schooling. Completed Primary
#> 1273 Barro-Lee: Percentage of female population age 45-49 with primary schooling. Completed Primary
#> 1274 Barro-Lee: Percentage of population age 45-49 with primary schooling. Completed Primary
#> 1275 Barro-Lee: Percentage of female population age 50-54 with primary schooling. Completed Primary
#> 1276 Barro-Lee: Percentage of population age 50-54 with primary schooling. Completed Primary
#> 1277 Barro-Lee: Percentage of female population age 55-59 with primary schooling. Completed Primary
#> 1278 Barro-Lee: Percentage of population age 55-59 with primary schooling. Completed Primary
#> 1279 Barro-Lee: Percentage of female population age 60-64 with primary schooling. Completed Primary
#> 1280 Barro-Lee: Percentage of population age 60-64 with primary schooling. Completed Primary
#> 1281 Barro-Lee: Percentage of female population age 65-69 with primary schooling. Completed Primary
#> 1282 Barro-Lee: Percentage of population age 65-69 with primary schooling. Completed Primary
#> 1283 Barro-Lee: Percentage of female population age 70-74 with primary schooling. Completed Primary
#> 1284 Barro-Lee: Percentage of population age 70-74 with primary schooling. Completed Primary
#> 1285 Barro-Lee: Percentage of female population age 75+ with primary schooling. Completed Primary
#> 1286 Barro-Lee: Percentage of population age 75+ with primary schooling. Completed Primary
#> 1287 Barro-Lee: Percentage of female population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)
#> 1288 Barro-Lee: Percentage of population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)
#> 1289 Barro-Lee: Percentage of female population age 15+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1290 Barro-Lee: Percentage of population age 15+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1291 Barro-Lee: Percentage of female population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)
#> 1292 Barro-Lee: Percentage of population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)
#> 1293 Barro-Lee: Percentage of female population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)
#> 1294 Barro-Lee: Percentage of population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)
#> 1295 Barro-Lee: Percentage of female population age 25+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1296 Barro-Lee: Percentage of population age 25+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1297 Barro-Lee: Percentage of female population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)
#> 1298 Barro-Lee: Percentage of population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)
#> 1299 Barro-Lee: Percentage of female population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)
#> 1300 Barro-Lee: Percentage of population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)
#> 1301 Barro-Lee: Percentage of female population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)
#> 1302 Barro-Lee: Percentage of population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)
#> 1303 Barro-Lee: Percentage of female population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)
#> 1304 Barro-Lee: Percentage of population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)
#> 1305 Barro-Lee: Percentage of female population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)
#> 1306 Barro-Lee: Percentage of population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)
#> 1307 Barro-Lee: Percentage of female population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)
#> 1308 Barro-Lee: Percentage of population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)
#> 1309 Barro-Lee: Percentage of female population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)
#> 1310 Barro-Lee: Percentage of population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)
#> 1311 Barro-Lee: Percentage of female population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)
#> 1312 Barro-Lee: Percentage of population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)
#> 1313 Barro-Lee: Percentage of female population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)
#> 1314 Barro-Lee: Percentage of population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)
#> 1315 Barro-Lee: Percentage of female population age 75+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1316 Barro-Lee: Percentage of population age 75+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1377 Barro-Lee: Percentage of female population age 15-19 with secondary schooling. Completed Secondary
#> 1378 Barro-Lee: Percentage of population age 15-19 with secondary schooling. Completed Secondary
#> 1379 Barro-Lee: Percentage of female population age 15+ with secondary schooling. Completed Secondary
#> 1380 Barro-Lee: Percentage of population age 15+ with secondary schooling. Completed Secondary
#> 1381 Barro-Lee: Percentage of female population age 20-24 with secondary schooling. Completed Secondary
#> 1382 Barro-Lee: Percentage of population age 20-24 with secondary schooling. Completed Secondary
#> 1383 Barro-Lee: Percentage of female population age 25-29 with secondary schooling. Completed Secondary
#> 1384 Barro-Lee: Percentage of population age 25-29 with secondary schooling. Completed Secondary
#> 1385 Barro-Lee: Percentage of female population age 25+ with secondary schooling. Completed Secondary
#> 1386 Barro-Lee: Percentage of population age 25+ with secondary schooling. Completed Secondary
#> 1387 Barro-Lee: Percentage of female population age 30-34 with secondary schooling. Completed Secondary
#> 1388 Barro-Lee: Percentage of population age 30-34 with secondary schooling. Completed Secondary
#> 1389 Barro-Lee: Percentage of female population age 35-39 with secondary schooling. Completed Secondary
#> 1390 Barro-Lee: Percentage of population age 35-39 with secondary schooling. Completed Secondary
#> 1391 Barro-Lee: Percentage of female population age 40-44 with secondary schooling. Completed Secondary
#> 1392 Barro-Lee: Percentage of population age 40-44 with secondary schooling. Completed Secondary
#> 1393 Barro-Lee: Percentage of female population age 45-49 with secondary schooling. Completed Secondary
#> 1394 Barro-Lee: Percentage of population age 45-49 with secondary schooling. Completed Secondary
#> 1395 Barro-Lee: Percentage of female population age 50-54 with secondary schooling. Completed Secondary
#> 1396 Barro-Lee: Percentage of population age 50-54 with secondary schooling. Completed Secondary
#> 1397 Barro-Lee: Percentage of female population age 55-59 with secondary schooling. Completed Secondary
#> 1398 Barro-Lee: Percentage of population age 55-59 with secondary schooling. Completed Secondary
#> 1399 Barro-Lee: Percentage of female population age 60-64 with secondary schooling. Completed Secondary
#> 1400 Barro-Lee: Percentage of population age 60-64 with secondary schooling. Completed Secondary
#> 1401 Barro-Lee: Percentage of female population age 65-69 with secondary schooling. Completed Secondary
#> 1402 Barro-Lee: Percentage of population age 65-69 with secondary schooling. Completed Secondary
#> 1403 Barro-Lee: Percentage of female population age 70-74 with secondary schooling. Completed Secondary
#> 1404 Barro-Lee: Percentage of population age 70-74 with secondary schooling. Completed Secondary
#> 1405 Barro-Lee: Percentage of female population age 75+ with secondary schooling. Completed Secondary
#> 1406 Barro-Lee: Percentage of population age 75+ with secondary schooling. Completed Secondary
#> 1407 Barro-Lee: Percentage of female population age 15-19 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1408 Barro-Lee: Percentage of population age 15-19 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1409 Barro-Lee: Percentage of female population age 15+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1410 Barro-Lee: Percentage of population age 15+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1411 Barro-Lee: Percentage of female population age 20-24 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1412 Barro-Lee: Percentage of population age 20-24 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1413 Barro-Lee: Percentage of female population age 25-29 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1414 Barro-Lee: Percentage of population age 25-29 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1415 Barro-Lee: Percentage of female population age 25+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1416 Barro-Lee: Percentage of population age 25+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1417 Barro-Lee: Percentage of female population age 30-34 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1418 Barro-Lee: Percentage of population age 30-34 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1419 Barro-Lee: Percentage of female population age 35-39 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1420 Barro-Lee: Percentage of population age 35-39 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1421 Barro-Lee: Percentage of female population age 40-44 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1422 Barro-Lee: Percentage of population age 40-44 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1423 Barro-Lee: Percentage of female population age 45-49 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1424 Barro-Lee: Percentage of population age 45-49 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1425 Barro-Lee: Percentage of female population age 50-54 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1426 Barro-Lee: Percentage of population age 50-54 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1427 Barro-Lee: Percentage of female population age 55-59 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1428 Barro-Lee: Percentage of population age 55-59 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1429 Barro-Lee: Percentage of female population age 60-64 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1430 Barro-Lee: Percentage of population age 60-64 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1431 Barro-Lee: Percentage of female population age 65-69 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1432 Barro-Lee: Percentage of population age 65-69 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1433 Barro-Lee: Percentage of female population age 70-74 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1434 Barro-Lee: Percentage of population age 70-74 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1435 Barro-Lee: Percentage of female population age 75+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1436 Barro-Lee: Percentage of population age 75+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1467 Barro-Lee: Percentage of female population age 15-19 with tertiary schooling. Completed Tertiary
#> 1468 Barro-Lee: Percentage of population age 15-19 with tertiary schooling. Completed Tertiary
#> 1469 Barro-Lee: Percentage of female population age 15+ with tertiary schooling. Completed Tertiary
#> 1470 Barro-Lee: Percentage of population age 15+ with tertiary schooling. Completed Tertiary
#> 1471 Barro-Lee: Percentage of female population age 20-24 with tertiary schooling. Completed Tertiary
#> 1472 Barro-Lee: Percentage of population age 20-24 with tertiary schooling. Completed Tertiary
#> 1473 Barro-Lee: Percentage of female population age 25-29 with tertiary schooling. Completed Tertiary
#> 1474 Barro-Lee: Percentage of population age 25-29 with tertiary schooling. Completed Tertiary
#> 1475 Barro-Lee: Percentage of female population age 25+ with tertiary schooling. Completed Tertiary
#> 1476 Barro-Lee: Percentage of population age 25+ with tertiary schooling. Completed Tertiary
#> 1477 Barro-Lee: Percentage of female population age 30-34 with tertiary schooling. Completed Tertiary
#> 1478 Barro-Lee: Percentage of population age 30-34 with tertiary schooling. Completed Tertiary
#> 1479 Barro-Lee: Percentage of female population age 35-39 with tertiary schooling. Completed Tertiary
#> 1480 Barro-Lee: Percentage of population age 35-39 with tertiary schooling. Completed Tertiary
#> 1481 Barro-Lee: Percentage of female population age 40-44 with tertiary schooling. Completed Tertiary
#> 1482 Barro-Lee: Percentage of population age 40-44 with tertiary schooling. Completed Tertiary
#> 1483 Barro-Lee: Percentage of female population age 45-49 with tertiary schooling. Completed Tertiary
#> 1484 Barro-Lee: Percentage of population age 45-49 with tertiary schooling. Completed Tertiary
#> 1485 Barro-Lee: Percentage of female population age 50-54 with tertiary schooling. Completed Tertiary
#> 1486 Barro-Lee: Percentage of population age 50-54 with tertiary schooling. Completed Tertiary
#> 1487 Barro-Lee: Percentage of female population age 55-59 with tertiary schooling. Completed Tertiary
#> 1488 Barro-Lee: Percentage of population age 55-59 with tertiary schooling. Completed Tertiary
#> 1489 Barro-Lee: Percentage of female population age 60-64 with tertiary schooling. Completed Tertiary
#> 1490 Barro-Lee: Percentage of population age 60-64 with tertiary schooling. Completed Tertiary
#> 1491 Barro-Lee: Percentage of female population age 65-69 with tertiary schooling. Completed Tertiary
#> 1492 Barro-Lee: Percentage of population age 65-69 with tertiary schooling. Completed Tertiary
#> 1493 Barro-Lee: Percentage of female population age 70-74 with tertiary schooling. Completed Tertiary
#> 1494 Barro-Lee: Percentage of population age 70-74 with tertiary schooling. Completed Tertiary
#> 1495 Barro-Lee: Percentage of female population age 75+ with tertiary schooling. Completed Tertiary
#> 1496 Barro-Lee: Percentage of population age 75+ with tertiary schooling. Completed Tertiary
#> 1497 Barro-Lee: Percentage of female population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1498 Barro-Lee: Percentage of population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1499 Barro-Lee: Percentage of female population age 15+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1500 Barro-Lee: Percentage of population age 15+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1501 Barro-Lee: Percentage of female population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1502 Barro-Lee: Percentage of population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1503 Barro-Lee: Percentage of female population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1504 Barro-Lee: Percentage of population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1505 Barro-Lee: Percentage of female population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1506 Barro-Lee: Percentage of population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1507 Barro-Lee: Percentage of female population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1508 Barro-Lee: Percentage of population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1509 Barro-Lee: Percentage of female population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1510 Barro-Lee: Percentage of population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1511 Barro-Lee: Percentage of female population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1512 Barro-Lee: Percentage of population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1513 Barro-Lee: Percentage of female population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1514 Barro-Lee: Percentage of population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1515 Barro-Lee: Percentage of female population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1516 Barro-Lee: Percentage of population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1517 Barro-Lee: Percentage of female population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1518 Barro-Lee: Percentage of population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1519 Barro-Lee: Percentage of female population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1520 Barro-Lee: Percentage of population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1521 Barro-Lee: Percentage of female population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1522 Barro-Lee: Percentage of population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1523 Barro-Lee: Percentage of female population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1524 Barro-Lee: Percentage of population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1525 Barro-Lee: Percentage of female population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1526 Barro-Lee: Percentage of population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1916 Population
#> 2024 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Min. exposure, 2100
#> 2025 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Max exposure, 2100
#> 2026 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Min. exposure, 2050
#> 2027 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population (%) - Max exposure, 2050
#> 2029 Additional people below $1.90 as % of total population by impact - All impacts
#> 2030 Additional people below $1.90 as % of total population by impact - Agriculture Revenues
#> 2031 Additional people below $1.90 as % of total population by impact - Disasters
#> 2032 Additional people below $1.90 as % of total population by impact - Food prices
#> 2033 Additional people below $1.90 as % of total population by impact - Health
#> 2034 Additional people below $1.90 as % of total population by impact - Labor productivity
#> 2035 Additional people below $4 as % of total population by impact, by 2030 - Agriculture
#> 2036 Additional people below $4 as % of total population by impact, by 2030 - All Impacts
#> 2037 Additional people below $4 as % of total population by impact, by 2030 - Disasters
#> 2038 Additional people below $4 as % of total population by impact, by 2030 - Health
#> 2039 Additional people below $4 as % of total population by impact, by 2030 - Temperature
#> 2040 Change in income (%) for bottom 40% of the population by impact, by 2030 - Agriculture (original)
#> 2041 Change in income (%) for bottom 40% of the population by impact, by 2030 - All Impacts (original)
#> 2042 Change in income (%) for bottom 40% of the population by impact, by 2030 - Disasters (original)
#> 2043 Change in income (%) for bottom 40% of the population by impact, by 2030 - Health (original)
#> 2044 Change in income (%) for bottom 40% of the population by impact, by 2030 - Temperature (original)
#> 2045 Change in income (%) for bottom 40% of the population by impact - Agriculture Revenues (update)
#> 2046 Change in income (%) for bottom 40% of the population by impact - All impacts (update)
#> 2047 Change in income (%) for bottom 40% of the population by impact - Disasters (update)
#> 2048 Change in income (%) for bottom 40% of the population by impact - Food prices (update)
#> 2049 Change in income (%) for bottom 40% of the population by impact - Health (update)
#> 2050 Change in income (%) for bottom 40% of the population by impact - Labor productivity (update)
#> 2236 Population exposed to floods (share of population below $5.50)
#> 2237 Population exposed to floods (share of total population)
#> 2272 Macro drivers of GHG emissions growth in the period 2012-2018 - Population
#> 2358 Percentage of population with Primary Education
#> 2359 Percentage of population with Secondary Education
#> 2361 Mortality rate attributable to household air pollution (deaths per 100 000 population)
#> 2362 Mortality rate attributable to ambient air pollution (deaths per 100 000 population)
#> 2363 Share of population covered by at least one social protection benefit (%)
#> 2411 Percent of the population who cannot afford sufficient calories at 52% of income
#> 2423 Percent of the population who cannot afford a healthy diet at 52% of income
#> 2439 Percent of the population who cannot afford nutrient adequacy at 52% of income
#> 5429 Gross ODA aid disbursement for population programmes and reproductive health, DAC donors total (current US$)
#> 5966 Access to clean fuels and technologies for cooking, rural (% of rural population)
#> 5967 Access to clean fuels and technologies for cooking, urban (% of urban population)
#> 5968 Access to clean fuels and technologies for cooking (% of population)
#> 5971 Access to electricity, rural (% of rural population)
#> 5972 Access to electricity, urban (% of urban population)
#> 5973 Access to electricity (% of population)
#> 5999 Access to non-solid fuel, rural (% of rural population)
#> 6000 Access to non-solid fuel, urban (% of urban population)
#> 6001 Access to non-solid fuel (% of population)
#> 6013 Economically active population in agriculture (number)
#> 6014 Economically active population in agriculture, female (FAO, number)
#> 6015 Agricultural population (FAO, number)
#> 6016 Economically active population in agriculture, male (FAO, number)
#> 6064 PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)
#> 6065 PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)
#> 6066 PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)
#> 6067 PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)
#> 6075 Droughts, floods, extreme temperatures (% of population, average 1990-2009)
#> 6103 Non-agricultural population (FAO, number)
#> 6104 Population density (people per sq. km of land area)
#> 6105 Rural population living in areas where elevation is below 5 meters (% of total population)
#> 6106 Urban population living in areas where elevation is below 5 meters (% of total population)
#> 6107 Population living in areas where elevation is below 5 meters (% of total population)
#> 6108 Population living in slums (% of urban population)
#> 6113 Rural population density (rural population per sq. km of arable land)
#> 6114 Population density, rural (people per sq km)
#> 6120 Population in largest city
#> 6121 Population in the largest city (% of urban population)
#> 6122 Population in urban agglomerations of more than 1 million
#> 6123 Population in urban agglomerations of more than 1 million (% of total population)
#> 7474 Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)
#> 7475 Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)
#> 7476 Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)
#> 7477 Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)
#> 7478 Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)
#> 7479 Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)
#> 7480 Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)
#> 7481 Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)
#> 7482 Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)
#> 7945 Condom use in last intercourse (% of females at risk population)
#> 7946 Condom use in last intercourse (% of females at risk population): Q1 (lowest)
#> 7947 Condom use in last intercourse (% of females at risk population): Q2
#> 7948 Condom use in last intercourse (% of females at risk population): Q3
#> 7949 Condom use in last intercourse (% of females at risk population): Q4
#> 7950 Condom use in last intercourse (% of females at risk population): Q5 (highest)
#> 7951 Prevalence of HIV, total (% of population ages 15-49)
#> 7952 Prevalence of HIV, total (% of population ages 15-49): Q1 (lowest)
#> 7953 Prevalence of HIV, total (% of population ages 15-49): Q2
#> 7954 Prevalence of HIV, total (% of population ages 15-49): Q3
#> 7955 Prevalence of HIV, total (% of population ages 15-49): Q4
#> 7956 Prevalence of HIV, total (% of population ages 15-49): Q5 (highest)
#> 7993 Use of insecticide-treated bed nets (% of under-5 population)
#> 7994 Use of insecticide-treated bed nets (% of under-5 population): Q1 (lowest)
#> 7995 Use of insecticide-treated bed nets (% of under-5 population): Q2
#> 7996 Use of insecticide-treated bed nets (% of under-5 population): Q3
#> 7997 Use of insecticide-treated bed nets (% of under-5 population): Q4
#> 7998 Use of insecticide-treated bed nets (% of under-5 population): Q5 (highest)
#> 8011 Blood sugar measured in last 5 years (% of population at risk of diabetes)
#> 8012 Blood sugar measured in last 5 years (% of population at risk of diabetes): Q1 (lowest)
#> 8013 Blood sugar measured in last 5 years (% of population at risk of diabetes): Q2
#> 8014 Blood sugar measured in last 5 years (% of population at risk of diabetes): Q3
#> 8015 Blood sugar measured in last 5 years (% of population at risk of diabetes): Q4
#> 8016 Blood sugar measured in last 5 years (% of population at risk of diabetes): Q5 (highest)
#> 8041 Blood pressure measured in last 12 months (% of population age 18+)
#> 8042 Blood pressure measured in last 12 months (% of population age 18+): Q1 (lowest)
#> 8043 Blood pressure measured in last 12 months (% of population age 18+): Q2
#> 8044 Blood pressure measured in last 12 months (% of population age 18+): Q3
#> 8045 Blood pressure measured in last 12 months (% of population age 18+): Q4
#> 8046 Blood pressure measured in last 12 months (% of population age 18+): Q5 (highest)
#> 8047 Mean diastolic blood pressure, adult population (mmHg)
#> 8048 Mean diastolic blood pressure, adult population (mmHg): Q1 (lowest)
#> 8049 Mean diastolic blood pressure, adult population (mmHg): Q2
#> 8050 Mean diastolic blood pressure, adult population (mmHg): Q3
#> 8051 Mean diastolic blood pressure, adult population (mmHg): Q4
#> 8052 Mean diastolic blood pressure, adult population (mmHg): Q5 (highest)
#> 8053 High blood pressure or being treated for high blood pressure (% of adult population)
#> 8054 High blood pressure or being treated for high blood pressure (% of adult population): Q1 (lowest)
#> 8055 High blood pressure or being treated for high blood pressure (% of adult population): Q2
#> 8056 High blood pressure or being treated for high blood pressure (% of adult population): Q3
#> 8057 High blood pressure or being treated for high blood pressure (% of adult population): Q4
#> 8058 High blood pressure or being treated for high blood pressure (% of adult population): Q5 (highest)
#> 8059 Mean systolic blood pressure, adult population (mmHg)
#> 8060 Mean systolic blood pressure, adult population (mmHg): Q1 (lowest)
#> 8061 Mean systolic blood pressure, adult population (mmHg): Q2
#> 8062 Mean systolic blood pressure, adult population (mmHg): Q3
#> 8063 Mean systolic blood pressure, adult population (mmHg): Q4
#> 8064 Mean systolic blood pressure, adult population (mmHg): Q5 (highest)
#> 8065 Treated for high blood pressure (% of adult population)
#> 8066 Treated for high blood pressure (% of adult population): Q1 (lowest)
#> 8067 Treated for high blood pressure (% of adult population): Q2
#> 8068 Treated for high blood pressure (% of adult population): Q3
#> 8069 Treated for high blood pressure (% of adult population): Q4
#> 8070 Treated for high blood pressure (% of adult population): Q5 (highest)
#> 8077 Mean cholesterol, adult population (mmol/L)
#> 8078 High cholesterol or on treatment for high cholesterol (% of adult population)
#> 8079 Cholesterol measured in last five years (% of population at risk of high cholesterol)
#> 8080 Cholesterol measured in last five years (% of population at risk of high cholesterol): Q1 (lowest)
#> 8081 Cholesterol measured in last five years (% of population at risk of high cholesterol): Q2
#> 8082 Cholesterol measured in last five years (% of population at risk of high cholesterol): Q3
#> 8083 Cholesterol measured in last five years (% of population at risk of high cholesterol): Q4
#> 8084 Cholesterol measured in last five years (% of population at risk of high cholesterol): Q5 (highest)
#> 8085 Treated for raised blood glucose or diabetes (% of adult population)
#> 8086 Treated for raised blood glucose or diabetes (% of adult population): Q1 (lowest)
#> 8087 Treated for raised blood glucose or diabetes (% of adult population): Q2
#> 8088 Treated for raised blood glucose or diabetes (% of adult population): Q3
#> 8089 Treated for raised blood glucose or diabetes (% of adult population): Q4
#> 8090 Treated for raised blood glucose or diabetes (% of adult population): Q5 (highest)
#> 8091 Mean fasting blood glucose, adult population (mmol/L)
#> 8092 Impaired fasting glycaemia (% of adult population)
#> 8117 Inpatient care use in last 12 months (% of population 18+)
#> 8118 Inpatient care use in last 12 months (% of population 18+): Q1 (lowest)
#> 8119 Inpatient care use in last 12 months (% of population 18+): Q2
#> 8120 Inpatient care use in last 12 months (% of population 18+): Q3
#> 8121 Inpatient care use in last 12 months (% of population 18+): Q4
#> 8122 Inpatient care use in last 12 months (% of population 18+): Q5 (highest)
#> 8135 Prevalence of obesity, female, BMI > 30 (% of population 15-49)
#> 8136 Prevalence of obesity, female, BMI > 30 (% of population 15-49): Q1 (lowest)
#> 8137 Prevalence of obesity, female, BMI > 30 (% of population 15-49): Q2
#> 8138 Prevalence of obesity, female, BMI > 30 (% of population 15-49): Q3
#> 8139 Prevalence of obesity, female, BMI > 30 (% of population 15-49): Q4
#> 8140 Prevalence of obesity, female, BMI > 30 (% of population 15-49): Q5 (highest)
#> 8141 Prevalence of obesity, female, BMI > 30 (% of population 18+)
#> 8142 Prevalence of obesity, female, BMI > 30 (% of population 18+): Q1 (lowest)
#> 8143 Prevalence of obesity, female, BMI > 30 (% of population 18+): Q2
#> 8144 Prevalence of obesity, female, BMI > 30 (% of population 18+): Q3
#> 8145 Prevalence of obesity, female, BMI > 30 (% of population 18+): Q4
#> 8146 Prevalence of obesity, female, BMI > 30 (% of population 18+): Q5 (highest)
#> 8147 Prevalence of obesity, male, BMI > 30 (% of population 18+)
#> 8148 Prevalence of obesity, male, BMI > 30 (% of population 18+): Q1 (lowest)
#> 8149 Prevalence of obesity, male, BMI > 30 (% of population 18+): Q2
#> 8150 Prevalence of obesity, male, BMI > 30 (% of population 18+): Q3
#> 8151 Prevalence of obesity, male, BMI > 30 (% of population 18+): Q4
#> 8152 Prevalence of obesity, male, BMI > 30 (% of population 18+): Q5 (highest)
#> 8153 Prevalence of obesity, BMI > 30 (% of population 18+)
#> 8154 Prevalence of obesity, BMI > 30 (% of population 18+): Q1 (lowest)
#> 8155 Prevalence of obesity, BMI > 30 (% of population 18+): Q2
#> 8156 Prevalence of obesity, BMI > 30 (% of population 18+): Q3
#> 8157 Prevalence of obesity, BMI > 30 (% of population 18+): Q4
#> 8158 Prevalence of obesity, BMI > 30 (% of population 18+): Q5 (highest)
#> 8165 Prevalence of overweight, female, BMI > 25 (% of population ages 15-49)
#> 8166 Prevalence of overweight, female, BMI > 25 (% of population ages 15-49): Q1 (lowest)
#> 8167 Prevalence of overweight, female, BMI > 25 (% of population ages 15-49): Q2
#> 8168 Prevalence of overweight, female, BMI > 25 (% of population ages 15-49): Q3
#> 8169 Prevalence of overweight, female, BMI > 25 (% of population ages 15-49): Q4
#> 8170 Prevalence of overweight, female, BMI > 25 (% of population ages 15-49): Q5 (highest)
#> 8171 Prevalence of overweight, female, BMI > 25 (% of population 18+)
#> 8172 Prevalence of overweight, female, BMI > 25 (% of population 18+): Q1 (lowest)
#> 8173 Prevalence of overweight, female, BMI > 25 (% of population 18+): Q2
#> 8174 Prevalence of overweight, female, BMI > 25 (% of population 18+): Q3
#> 8175 Prevalence of overweight, female, BMI > 25 (% of population 18+): Q4
#> 8176 Prevalence of overweight, female, BMI > 25 (% of population 18+): Q5 (highest)
#> 8177 Prevalence of overweight, male, BMI > 25 (% of population 18+)
#> 8178 Prevalence of overweight, male, BMI > 25 (% of population 18+): Q1 (lowest)
#> 8179 Prevalence of overweight, male, BMI > 25 (% of population 18+): Q2
#> 8180 Prevalence of overweight, male, BMI > 25 (% of population 18+): Q3
#> 8181 Prevalence of overweight, male, BMI > 25 (% of population 18+): Q4
#> 8182 Prevalence of overweight, male, BMI > 25 (% of population 18+): Q5 (highest)
#> 8183 Prevalence of overweight, BMI > 25 (% of population 18+)
#> 8184 Prevalence of overweight, BMI > 25 (% of population 18+): Q1 (lowest)
#> 8185 Prevalence of overweight, BMI > 25 (% of population 18+): Q2
#> 8186 Prevalence of overweight, BMI > 25 (% of population 18+): Q3
#> 8187 Prevalence of overweight, BMI > 25 (% of population 18+): Q4
#> 8188 Prevalence of overweight, BMI > 25 (% of population 18+): Q5 (highest)
#> 8195 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%)
#> 8196 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8197 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q2
#> 8198 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q3
#> 8199 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q4
#> 8200 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health care expenditure (%): Q5 (highest)
#> 8202 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 8203 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8204 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2
#> 8205 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3
#> 8206 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4
#> 8207 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)
#> 8209 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 8210 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8211 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2
#> 8212 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3
#> 8213 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4
#> 8214 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)
#> 8216 Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 8217 Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8218 Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2
#> 8219 Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3
#> 8220 Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4
#> 8221 Proportion of population pushed below the $5.50 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)
#> 8223 Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 8224 Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8225 Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q2
#> 8226 Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q3
#> 8227 Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q4
#> 8228 Proportion of population pushed below the $21.70 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%): Q5 (highest)
#> 8229 Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%)
#> 8230 Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%) : Q1 (lowest)
#> 8231 Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%) : Q2
#> 8232 Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%) : Q3
#> 8233 Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%) : Q4
#> 8234 Proportion of population pushed by out-of-pocket health care expenditure below the societal poverty line, defined as the higher of the $1.90 ($ 2011 PPP) poverty line and a 50% of median consumption poverty line (%) : Q5 (highest)
#> 8242 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)
#> 8243 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8244 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q2
#> 8245 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q3
#> 8246 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q4
#> 8247 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%): Q5 (highest)
#> 8248 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)
#> 8249 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q1 (lowest)
#> 8250 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q2
#> 8251 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q3
#> 8252 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q4
#> 8253 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%): Q5 (highest)
#> 8708 Decadal Growth of Population (%)
#> 8709 Decadal Growth of Population, Rural (%)
#> 8710 Decadal Growth of Population, Urban (%)
#> 8711 Population, Rural (Thousands)
#> 8712 Population, Rural (%)
#> 8713 Population (Thousands)
#> 8714 Population, Urban (%)
#> 8775 Average Population Per Bank Office (In Thousands)
#> 8779 Number of Government Allopathic Doctors Per 100,000 Population
#> 8781 Government Hospitals Number of beds Per 100,000 Population
#> 8783 Government Hospitals (Number) Per 100,000 Population
#> 8863 Total Slum Population - Female (Number)
#> 8864 Total Slum Population - Male (Number)
#> 8865 Total Slum Population (Number)
#> 8880 Rural Road Density (KMs/1000 Population)
#> 8882 Urban Road Density (KMs Per 1000 Population)
#> 8972 Population covered by mobile cellular network (%)
#> 9027 Population coverage of mobile cellular telephony (%)
#> 9052 Individuals using the Internet (% of population)
#> 9176 Employment in the agricultural sector, aged 15-64, female (% of female employed population in working age)
#> 9177 Employment in the agricultural sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9178 Employment in the agricultural sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9179 Employment in the agricultural sector, aged 15-64, male (% of male employed population in working age)
#> 9180 Employment in the agricultural sector, aged 25-64 (% of employed population aged 25-64)
#> 9181 Employment in the agricultural sector, aged 15-64, rural (% of rural employed population in working age)
#> 9182 Employment in the agricultural sector, aged 15-64, urban (% of urban employed population in working age)
#> 9183 Employment in the agricultural sector, aged 15-24 (% of employed population aged 15-24)
#> 9184 Employment in the agricultural sector, aged 15-64, total (% of total employed population in working age)
#> 9185 Employment in the armed forces occupation group, aged 15-64, female (% of female employed population in working age)
#> 9186 Employment in the armed forces occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9187 Employment in the armed forces occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9188 Employment in the armed forces occupation group, aged 15-64, male (% of male employed population in working age)
#> 9189 Employment in the armed forces occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9190 Employment in the armed forces occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9191 Employment in the armed forces occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9192 Employment in the armed forces occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9193 Employment in the armed forces occupation group, aged 15-64, total (% of total employed population in working age)
#> 9194 Employment in the clerks occupation group, aged 15-64, female (% of female employed population in working age)
#> 9195 Employment in the clerks occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9196 Employment in the clerks occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9197 Employment in the clerks occupation group, aged 15-64, male (% of male employed population in working age)
#> 9198 Employment in the clerks occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9199 Employment in the clerks occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9200 Employment in the clerks occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9201 Employment in the clerks occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9202 Employment in the clerks occupation group, aged 15-64, total (% of total employed population in working age)
#> 9203 Employment in the construction sector, aged 15-64, female (% of female employed population in working age)
#> 9204 Employment in the construction sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9205 Employment in the construction sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9206 Employment in the construction sector, aged 15-64, male (% of male employed population in working age)
#> 9207 Employment in the construction sector, aged 25-64 (% of employed population aged 25-64)
#> 9208 Employment in the construction sector, aged 15-64, rural (% of rural employed population in working age)
#> 9209 Employment in the construction sector, aged 15-64, urban (% of urban employed population in working age)
#> 9210 Employment in the construction sector, aged 15-24 (% of employed population aged 15-24)
#> 9211 Employment in the construction sector, aged 15-64, total (% of total employed population in working age)
#> 9212 Employment in the commerce sector, aged 15-64, female (% of female employed population in working age)
#> 9213 Employment in the commerce sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9214 Employment in the commerce sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9215 Employment in the commerce sector, aged 15-64, male (% of male employed population in working age)
#> 9216 Employment in the commerce sector, aged 25-64 (% of employed population aged 25-64)
#> 9217 Employment in the commerce sector, aged 15-64, rural (% of rural employed population in working age)
#> 9218 Employment in the commerce sector, aged 15-64, urban (% of urban employed population in working age)
#> 9219 Employment in the commerce sector, aged 15-24 (% of employed population aged 15-24)
#> 9220 Employment in the commerce sector, aged 15-64, total (% of total employed population in working age)
#> 9221 Employed workers with a work contract, aged 15-64, female (% of female employed population in working age)
#> 9222 Employed workers with a work contract, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9223 Employed workers with a work contract, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9224 Employed workers with a work contract, aged 15-64, male (% of male employed population in working age)
#> 9225 Employed workers with a work contract, aged 25-64 (% of employed population aged 25-64)
#> 9226 Employed workers with a work contract, aged 15-64, rural (% of rural employed population in working age)
#> 9227 Employed workers with a work contract, aged 15-64, urban (% of urban employed population in working age)
#> 9228 Employed workers with a work contract, aged 15-24 (% of employed population aged 15-24)
#> 9229 Employed workers with a work contract, aged 15-64, total (% of total employed population in working age)
#> 9230 Employment in the craft workers occupation group, aged 15-64, female (% of female employed population in working age)
#> 9231 Employment in the craft workers occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9232 Employment in the craft workers occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9233 Employment in the craft workers occupation group, aged 15-64, male (% of male employed population in working age)
#> 9234 Employment in the craft workers occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9235 Employment in the craft workers occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9236 Employment in the craft workers occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9237 Employment in the craft workers occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9238 Employment in the craft workers occupation group, aged 15-64, total (% of total employed population in working age)
#> 9239 Employment in the eletricity and public utilities sector, aged 15-64, female (% of female employed population in working age)
#> 9240 Employment in the eletricity and public utilities sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9241 Employment in the eletricity and public utilities sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9242 Employment in the eletricity and public utilities sector, aged 15-64, male (% of male employed population in working age)
#> 9243 Employment in the eletricity and public utilities sector, aged 25-64 (% of employed population aged 25-64)
#> 9244 Employment in the eletricity and public utilities sector, aged 15-64, rural (% of rural employed population in working age)
#> 9245 Employment in the eletricity and public utilities sector, aged 15-64, urban (% of urban employed population in working age)
#> 9246 Employment in the eletricity and public utilities sector, aged 15-24 (% of employed population aged 15-24)
#> 9247 Employment in the eletricity and public utilities sector, aged 15-64, total (% of total employed population in working age)
#> 9248 Employment in the elementary occupation group, aged 15-64, female (% of female employed population in working age)
#> 9249 Employment in the elementary occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9250 Employment in the elementary occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9251 Employment in the elementary occupation group, aged 15-64, male (% of male employed population in working age)
#> 9252 Employment in the elementary occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9253 Employment in the elementary occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9254 Employment in the elementary occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9255 Employment in the elementary occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9256 Employment in the elementary occupation group, aged 15-64, total (% of total employed population in working age)
#> 9257 Employment in the financial and business services sector, aged 15-64, female (% of female employed population in working age)
#> 9258 Employment in the financial and business services sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9259 Employment in the financial and business services sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9260 Employment in the financial and business services sector, aged 15-64, male (% of male employed population in working age)
#> 9261 Employment in the financial and business services sector, aged 25-64 (% of employed population aged 25-64)
#> 9262 Employment in the financial and business services sector, aged 15-64, rural (% of rural employed population in working age)
#> 9263 Employment in the financial and business services sector, aged 15-64, urban (% of urban employed population in working age)
#> 9264 Employment in the financial and business services sector, aged 15-24 (% of employed population aged 15-24)
#> 9265 Employment in the financial and business services sector, aged 15-64, total (% of total employed population in working age)
#> 9266 Employed workers with health insurance, aged 15-64, female (% of female employed population in working age)
#> 9267 Employed workers with health insurance, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9268 Employed workers with health insurance, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9269 Employed workers with health insurance, aged 15-64, male (% of male employed population in working age)
#> 9270 Employed workers with health insurance, aged 25-64 (% of employed population aged 25-64)
#> 9271 Employed workers with health insurance, aged 15-64, rural (% of rural employed population in working age)
#> 9272 Employed workers with health insurance, aged 15-64, urban (% of urban employed population in working age)
#> 9273 Employed workers with health insurance, aged 15-24 (% of employed population aged 15-24)
#> 9274 Employed workers with health insurance, aged 15-64, total (% of total employed population in working age)
#> 9275 Informal job workers, aged 15-64, female (% of female employed population in working age)
#> 9276 Informal job workers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9277 Informal job workers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9278 Informal job workers, aged 15-64, male (% of male employed population in working age)
#> 9279 Informal job workers, aged 25-64 (% of employed population aged 25-64)
#> 9280 Informal job workers, aged 15-64, rural (% of rural employed population in working age)
#> 9281 Informal job workers, aged 15-64, urban (% of urban employed population in working age)
#> 9282 Informal job workers, aged 15-24 (% of employed population aged 15-24)
#> 9283 Informal job workers, aged 15-64, total (% of total employed population in working age)
#> 9284 Employment in the industrial sector, aged 15-64, female (% of female employed population in working age)
#> 9285 Employment in the industrial sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9286 Employment in the industrial sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9287 Employment in the industrial sector, aged 15-64, male (% of male employed population in working age)
#> 9288 Employment in the industrial sector, aged 25-64 (% of employed population aged 25-64)
#> 9289 Employment in the industrial sector, aged 15-64, rural (% of rural employed population in working age)
#> 9290 Employment in the industrial sector, aged 15-64, urban (% of urban employed population in working age)
#> 9291 Employment in the industrial sector, aged 15-24 (% of employed population aged 15-24)
#> 9292 Employment in the industrial sector, aged 15-64, total (% of total employed population in working age)
#> 9293 Employment in the machine operators occupation group, aged 15-64, female (% of female employed population in working age)
#> 9294 Employment in the machine operators occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9295 Employment in the machine operators occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9296 Employment in the machine operators occupation group, aged 15-64, male (% of male employed population in working age)
#> 9297 Employment in the machine operators occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9298 Employment in the machine operators occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9299 Employment in the machine operators occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9300 Employment in the machine operators occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9301 Employment in the machine operators occupation group, aged 15-64, total (% of total employed population in working age)
#> 9302 Employment in the manufacturing sector, aged 15-64, female (% of female employed population in working age)
#> 9303 Employment in the manufacturing sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9304 Employment in the manufacturing sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9305 Employment in the manufacturing sector, aged 15-64, male (% of male employed population in working age)
#> 9306 Employment in the manufacturing sector, aged 25-64 (% of employed population aged 25-64)
#> 9307 Employment in the manufacturing sector, aged 15-64, rural (% of rural employed population in working age)
#> 9308 Employment in the manufacturing sector, aged 15-64, urban (% of urban employed population in working age)
#> 9309 Employment in the manufacturing sector, aged 15-24 (% of employed population aged 15-24)
#> 9310 Employment in the manufacturing sector, aged 15-64, total (% of total employed population in working age)
#> 9311 Employment in the mining sector, aged 15-64, female (% of female employed population in working age)
#> 9312 Employment in the mining sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9313 Employment in the mining sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9314 Employment in the mining sector, aged 15-64, male (% of male employed population in working age)
#> 9315 Employment in the mining sector, aged 25-64 (% of employed population aged 25-64)
#> 9316 Employment in the mining sector, aged 15-64, rural (% of rural employed population in working age)
#> 9317 Employment in the mining sector, aged 15-64, urban (% of urban employed population in working age)
#> 9318 Employment in the mining sector, aged 15-24 (% of employed population aged 15-24)
#> 9319 Employment in the mining sector, aged 15-64, total (% of total employed population in working age)
#> 9320 Employers, aged 15-64, female (% of female employed population in working age)
#> 9321 Employers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9322 Employers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9323 Employers, aged 15-64, male (% of male employed population in working age)
#> 9324 Non-agricultural employers, aged 15-64, female (% of female employed population in working age)
#> 9325 Non-agricultural employers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9326 Non-agricultural employers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9327 Non-agricultural employers, aged 15-64, male (% of male employed population in working age)
#> 9328 Non-agricultural employers, aged 25-64 (% of employed population aged 25-64)
#> 9329 Non-agricultural employers, aged 15-64, rural (% of rural employed population in working age)
#> 9330 Non-agricultural employers, aged 15-64, urban (% of urban employed population in working age)
#> 9331 Non-agricultural employers, aged 15-24 (% of employed population aged 15-24)
#> 9332 Non-agricultural employers, aged 15-64, total (% of total employed population in working age)
#> 9333 Employers, aged 25-64 (% of employed population aged 25-64)
#> 9334 Employers, aged 15-64, rural (% of rural employed population in working age)
#> 9335 Employers, aged 15-64, urban (% of urban employed population in working age)
#> 9336 Employers, aged 15-24 (% of employed population aged 15-24)
#> 9337 Employers, aged 15-64, total (% of total employed population in working age)
#> 9338 Female in non-agricultural employment, aged 15-64, above primary education (% of employed female population with high education in working age)
#> 9339 Female in non-agricultural employment, aged 15-64, primary education and below (% of employed female population with low education in working age)
#> 9340 Female in non-agricultural employment, aged 25-64 (% of employed female population aged 25-64)
#> 9341 Female in non-agricultural employment, aged 15-64, rural (% of rural employed female population in working age)
#> 9342 Female in non-agricultural employment, aged 15-64, urban (% of urban employed female population in working age)
#> 9343 Female in non-agricultural employment, aged 15-24 (% of employed female population aged 15-24)
#> 9344 Female in non-agricultural employment, aged 15-64, total (% of total employed female population in working age)
#> 9352 Employment in the other services sector, aged 15-64, female (% of female employed population in working age)
#> 9353 Employment in the other services sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9354 Employment in the other services sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9355 Employment in the other services sector, aged 15-64, male (% of male employed population in working age)
#> 9356 Employment in the other services sector, aged 25-64 (% of employed population aged 25-64)
#> 9357 Employment in the other services sector, aged 15-64, rural (% of rural employed population in working age)
#> 9358 Employment in the other services sector, aged 15-64, urban (% of urban employed population in working age)
#> 9359 Employment in the other services sector, aged 15-24 (% of employed population aged 15-24)
#> 9360 Employment in the other services sector, aged 15-64, total (% of total employed population in working age)
#> 9361 Employment in the public administration sector, aged 15-64, female (% of female employed population in working age)
#> 9362 Employment in the public administration sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9363 Employment in the public administration sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9364 Employment in the public administration sector, aged 15-64, male (% of male employed population in working age)
#> 9365 Employment in the public administration sector, aged 25-64 (% of employed population aged 25-64)
#> 9366 Employment in the public administration sector, aged 15-64, rural (% of rural employed population in working age)
#> 9367 Employment in the public administration sector, aged 15-64, urban (% of urban employed population in working age)
#> 9368 Employment in the public administration sector, aged 15-24 (% of employed population aged 15-24)
#> 9369 Employment in the public administration sector, aged 15-64, total (% of total employed population in working age)
#> 9370 Employment in the professionals occupation group, aged 15-64, female (% of female employed population in working age)
#> 9371 Employment in the professionals occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9372 Employment in the professionals occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9373 Employment in the professionals occupation group, aged 15-64, male (% of male employed population in working age)
#> 9374 Employment in the professionals occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9375 Employment in the professionals occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9376 Employment in the professionals occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9377 Employment in the professionals occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9378 Employment in the professionals occupation group, aged 15-64, total (% of total employed population in working age)
#> 9379 Employment in the public sector, aged 15-64, female (% of female employed population in working age)
#> 9380 Employment in the public sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9381 Employment in the public sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9382 Employment in the public sector, aged 15-64, male (% of male employed population in working age)
#> 9383 Employment in the public sector, aged 25-64 (% of employed population aged 25-64)
#> 9384 Employment in the public sector, aged 15-64, rural (% of rural employed population in working age)
#> 9385 Employment in the public sector, aged 15-64, urban (% of urban employed population in working age)
#> 9386 Employment in the public sector, aged 15-24 (% of employed population aged 15-24)
#> 9387 Employment in the public sector, aged 15-64, total (% of total employed population in working age)
#> 9388 Self-employed workers, aged 15-64, female (% of female employed population in working age)
#> 9389 Self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9390 Self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9391 Self-employed workers, aged 15-64, male (% of male employed population in working age)
#> 9392 Non-agricultural self-employed workers, aged 15-64, female (% of female employed population in working age)
#> 9393 Non-agricultural self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9394 Non-agricultural self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9395 Non-agricultural self-employed workers, aged 15-64, male (% of male employed population in working age)
#> 9396 Non-agricultural self-employed workers, aged 25-64 (% of employed population aged 25-64)
#> 9397 Non-agricultural self-employed workers, aged 15-64, rural (% of rural employed population in working age)
#> 9398 Non-agricultural self-employed workers, aged 15-64, urban (% of urban employed population in working age)
#> 9399 Non-agricultural self-employed workers, aged 15-24 (% of employed population aged 15-24)
#> 9400 Non-agricultural self-employed workers, aged 15-64, total (% of total employed population in working age)
#> 9401 Self-employed workers, aged 25-64 (% of employed population aged 25-64)
#> 9402 Self-employed workers, aged 15-64, rural (% of rural employed population in working age)
#> 9403 Self-employed workers, aged 15-64, urban (% of urban employed population in working age)
#> 9404 Self-employed workers, aged 15-24 (% of employed population aged 15-24)
#> 9405 Self-employed workers, aged 15-64, total (% of total employed population in working age)
#> 9406 Employment in the senior officials occupation group, aged 15-64, female (% of female employed population in working age)
#> 9407 Employment in the senior officials occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9408 Employment in the senior officials occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9409 Employment in the senior officials occupation group, aged 15-64, male (% of male employed population in working age)
#> 9410 Employment in the senior officials occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9411 Employment in the senior officials occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9412 Employment in the senior officials occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9413 Employment in the senior officials occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9414 Employment in the senior officials occupation group, aged 15-64, total (% of total employed population in working age)
#> 9415 Employment in the service sector, aged 15-64, female (% of female employed population in working age)
#> 9416 Employment in the service sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9417 Employment in the service sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9418 Employment in the service sector, aged 15-64, male (% of male employed population in working age)
#> 9419 Employment in the service sector, aged 25-64 (% of employed population aged 25-64)
#> 9420 Employment in the service sector, aged 15-64, rural (% of rural employed population in working age)
#> 9421 Employment in the service sector, aged 15-64, urban (% of urban employed population in working age)
#> 9422 Employment in the service sector, aged 15-24 (% of employed population aged 15-24)
#> 9423 Employment in the service sector, aged 15-64, total (% of total employed population in working age)
#> 9424 Employment in the skilled agriculture occupation group, aged 15-64, female (% of female employed population in working age)
#> 9425 Employment in the skilled agriculture occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9426 Employment in the skilled agriculture occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9427 Employment in the skilled agriculture occupation group, aged 15-64, male (% of male employed population in working age)
#> 9428 Employment in the skilled agriculture occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9429 Employment in the skilled agriculture occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9430 Employment in the skilled agriculture occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9431 Employment in the skilled agriculture occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9432 Employment in the skilled agriculture occupation group, aged 15-64, total (% of total employed population in working age)
#> 9433 Employed workers with social security, aged 15-64, female (% of female employed population in working age)
#> 9434 Employed workers with social security, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9435 Employed workers with social security, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9436 Employed workers with social security, aged 15-64, male (% of male employed population in working age)
#> 9437 Employed workers with social security, aged 25-64 (% of employed population aged 25-64)
#> 9438 Employed workers with social security, aged 15-64, rural (% of rural employed population in working age)
#> 9439 Employed workers with social security, aged 15-64, urban (% of urban employed population in working age)
#> 9440 Employed workers with social security, aged 15-24 (% of employed population aged 15-24)
#> 9441 Employed workers with social security, aged 15-64, total (% of total employed population in working age)
#> 9442 Employment in the service and market sales occupation group, aged 15-64, female (% of female employed population in working age)
#> 9443 Employment in the service and market sales occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9444 Employment in the service and market sales occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9445 Employment in the service and market sales occupation group, aged 15-64, male (% of male employed population in working age)
#> 9446 Employment in the service and market sales occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9447 Employment in the service and market sales occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9448 Employment in the service and market sales occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9449 Employment in the service and market sales occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9450 Employment in the service and market sales occupation group, aged 15-64, total (% of total employed population in working age)
#> 9451 Employment in the technicians occupation group, aged 15-64, female (% of female employed population in working age)
#> 9452 Employment in the technicians occupation group, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9453 Employment in the technicians occupation group, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9454 Employment in the technicians occupation group, aged 15-64, male (% of male employed population in working age)
#> 9455 Employment in the technicians occupation group, aged 25-64 (% of employed population aged 25-64)
#> 9456 Employment in the technicians occupation group, aged 15-64, rural (% of rural employed population in working age)
#> 9457 Employment in the technicians occupation group, aged 15-64, urban (% of urban employed population in working age)
#> 9458 Employment in the technicians occupation group, aged 15-24 (% of employed population aged 15-24)
#> 9459 Employment in the technicians occupation group, aged 15-64, total (% of total employed population in working age)
#> 9460 Employment to population ratio, aged 15-64, female (% of female population in working age)
#> 9461 Employment to population ratio, aged 15-64, above primary education (% of population with high education in working age)
#> 9462 Employment to population ratio, aged 15-64, primary education and below (% of population with low education in working age)
#> 9463 Employment to population ratio, aged 15-64, male (% of male population in working age)
#> 9464 Employment to population ratio, aged 25-64 (% of population aged 25-64)
#> 9465 Employment to population ratio, aged 15-64, rural (% of rural population in working age)
#> 9466 Employment to population ratio, aged 15-64, urban (% of urban population in working age)
#> 9467 Employment to population ratio, aged 15-24 (% of population aged 15-24)
#> 9468 Employment to population ratio, aged 15-64, total (% of total population in working age)
#> 9469 Employment in the transport and communication sector, aged 15-64, female (% of female employed population in working age)
#> 9470 Employment in the transport and communication sector, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9471 Employment in the transport and communication sector, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9472 Employment in the transport and communication sector, aged 15-64, male (% of male employed population in working age)
#> 9473 Employment in the transport and communication sector, aged 25-64 (% of employed population aged 25-64)
#> 9474 Employment in the transport and communication sector, aged 15-64, rural (% of rural employed population in working age)
#> 9475 Employment in the transport and communication sector, aged 15-64, urban (% of urban employed population in working age)
#> 9476 Employment in the transport and communication sector, aged 15-24 (% of employed population aged 15-24)
#> 9477 Employment in the transport and communication sector, aged 15-64, total (% of total employed population in working age)
#> 9478 Unpaid workers, aged 15-64, female (% of female employed population in working age)
#> 9479 Unpaid workers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9480 Unpaid workers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9481 Unpaid workers, aged 15-64, male (% of male employed population in working age)
#> 9482 Non-agricultural unpaid employment, aged 15-64, female (% of female employed population in working age)
#> 9483 Non-agricultural unpaid employment, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9484 Non-agricultural unpaid employment, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9485 Non-agricultural unpaid employment, aged 15-64, male (% of male employed population in working age)
#> 9486 Non-agricultural unpaid employment, aged 25-64 (% of employed population aged 25-64)
#> 9487 Non-agricultural unpaid employment, aged 15-64, rural (% of rural employed population in working age)
#> 9488 Non-agricultural unpaid employment, aged 15-64, urban (% of urban employed population in working age)
#> 9489 Non-agricultural unpaid employment, aged 15-24 (% of employed population aged 15-24)
#> 9490 Non-agricultural unpaid employment, aged 15-64, total (% of total employed population in working age)
#> 9491 Unpaid workers, aged 25-64 (% of employed population aged 25-64)
#> 9492 Unpaid workers, aged 15-64, rural (% of rural employed population in working age)
#> 9493 Unpaid workers, aged 15-64, urban (% of urban employed population in working age)
#> 9494 Unpaid workers, aged 15-24 (% of employed population aged 15-24)
#> 9495 Unpaid workers, aged 15-64, total (% of total employed population in working age)
#> 9496 Unpaid or self-employed workers, aged 15-64, female (% of female employed population in working age)
#> 9497 Unpaid or self-employed workers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9498 Unpaid or self-employed workers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9499 Unpaid or self-employed workers, aged 15-64, male (% of male employed population in working age)
#> 9500 Unpaid or self-employed workers, aged 25-64 (% of employed population aged 25-64)
#> 9501 Unpaid or self-employed workers, aged 15-64, rural (% of rural employed population in working age)
#> 9502 Unpaid or self-employed workers, aged 15-64, urban (% of urban employed population in working age)
#> 9503 Unpaid or self-employed workers, aged 15-24 (% of employed population aged 15-24)
#> 9504 Unpaid or self-employed workers, aged 15-64, total (% of total employed population in working age)
#> 9512 Wage workers, aged 15-64, female (% of female employed population in working age)
#> 9513 Wage workers, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9514 Wage workers, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9515 Wage workers, aged 15-64, male (% of male employed population in working age)
#> 9516 Non-agricultural wage employment, aged 15-64, female (% of female employed population in working age)
#> 9517 Non-agricultural wage employment, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9518 Non-agricultural wage employment, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9519 Non-agricultural wage employment, aged 15-64, male (% of male employed population in working age)
#> 9520 Non-agricultural wage employment, aged 25-64 (% of employed population aged 25-64)
#> 9521 Non-agricultural wage employment, aged 15-64, rural (% of rural employed population in working age)
#> 9522 Non-agricultural wage employment, aged 15-64, urban (% of urban employed population in working age)
#> 9523 Non-agricultural wage employment, aged 15-24 (% of employed population aged 15-24)
#> 9524 Non-agricultural wage employment, aged 15-64, total (% of total employed population in working age)
#> 9525 Wage workers, aged 25-64 (% of employed population aged 25-64)
#> 9526 Wage workers, aged 15-64, rural (% of rural employed population in working age)
#> 9527 Wage workers, aged 15-64, urban (% of urban employed population in working age)
#> 9528 Wage workers, aged 15-24 (% of employed population aged 15-24)
#> 9529 Wage workers, aged 15-64, total (% of total employed population in working age)
#> 9530 Enrollment rate, aged 6-16, female (% of female population aged 6-16)
#> 9531 Enrollment rate, aged 6-16, above primary education (% of population with high education aged 6-16)
#> 9532 Enrollment rate, aged 6-16, primary education and below (% of population with low education aged 6-16)
#> 9533 Enrollment rate, aged 6-16, male (% of male population aged 6-16)
#> 9534 Enrollment rate, aged 6-16, rural (% of rural population aged 6-16)
#> 9535 Enrollment rate, aged 6-16, urban (% of urban population aged 6-16)
#> 9536 Enrollment rate, aged 15-16 (% of population aged 15-16)
#> 9537 Enrollment rate, aged 6-16, total (% of total population aged 6-16)
#> 9565 Workers with more than one jobs in last week, aged 15-64, female (% of female employed population in working age)
#> 9566 Workers with more than one jobs in last week, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9567 Workers with more than one jobs in last week, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9568 Workers with more than one jobs in last week, aged 15-64, male (% of male employed population in working age)
#> 9569 Workers with more than one jobs in last week, aged 25-64 (% of employed population aged 25-64)
#> 9570 Workers with more than one jobs in last week, aged 15-64, rural (% of rural employed population in working age)
#> 9571 Workers with more than one jobs in last week, aged 15-64, urban (% of urban employed population in working age)
#> 9572 Workers with more than one jobs in last week, aged 15-24 (% of employed population aged 15-24)
#> 9573 Workers with more than one jobs in last week, aged 15-64, total (% of total employed population in working age)
#> 9574 Population aged 0-14, female (% of female population)
#> 9575 Population aged 0-14, above primary education (% of population with high edcuation)
#> 9576 Population aged 0-14, primary education and below (% of population with low edcuation)
#> 9577 Population aged 0-14, male (% of male population)
#> 9578 Population aged 0-14, rural (% of rural population)
#> 9579 Population aged 0-14, urban (% of urban population)
#> 9580 Population aged 0-14, total (% of total population)
#> 9581 Population aged 15-24, female (% of female population)
#> 9582 Population aged 15-24, above primary education (% of population with high edcuation)
#> 9583 Population aged 15-24, primary education and below (% of population with low edcuation)
#> 9584 Population aged 15-24, male (% of male population)
#> 9585 Population aged 15-24, rural (% of rural population)
#> 9586 Population aged 15-24, urban (% of urban population)
#> 9587 Population aged 15-24, total (% of total population)
#> 9588 Working-age population, aged 15-64, female (% of female population)
#> 9589 Working-age population, aged 15-64, above primary education (% of population with high education)
#> 9590 Working-age population, aged 15-64, primary education and below (% of population with low education)
#> 9591 Working-age population, aged 15-64, male (% of male population)
#> 9592 Working-age population, aged 15-64, rural (% of rural population)
#> 9593 Working-age population, aged 15-64, urban (% of urban population)
#> 9594 Working-age population, aged 15-64, total (% of total population)
#> 9595 Population aged 25-64, female (% of female population)
#> 9596 Population aged 25-64, above primary education (% of population with high edcuation)
#> 9597 Population aged 25-64, primary education and below (% of population with low edcuation)
#> 9598 Population aged 25-64, male (% of male population)
#> 9599 Population aged 25-64, rural (% of rural population)
#> 9600 Population aged 25-64, urban (% of urban population)
#> 9601 Population aged 25-64, total (% of total population)
#> 9602 Population aged 65 and above, female (% of female population)
#> 9603 Population aged 65 and above, above primary education (% of population with high edcuation)
#> 9604 Population aged 65 and above, primary education and below (% of population with low edcuation)
#> 9605 Population aged 65 and above, male (% of male population)
#> 9606 Population aged 65 and above, rural (% of rural population)
#> 9607 Population aged 65 and above, urban (% of urban population)
#> 9608 Population aged 65 and above, total (% of total population)
#> 9612 Working-age population with no education, female (% of female population in working age)
#> 9613 Working-age population with no education, primary education and below (% of population with low education in working age)
#> 9614 Working-age population with no education, male (% of male population in working age)
#> 9615 Working-age population with no education, aged 25-64 (% of population aged 25-64)
#> 9616 Working-age population with no education, rural (% of rural population in working age)
#> 9617 Working-age population with no education, urban (% of urban population in working age)
#> 9618 Working-age population with no education, aged 15-24 (% of population aged 15-24)
#> 9619 Working-age population with no education, total (% of total population in working age)
#> 9620 Working-age population with primary education, female (% of female population in working age)
#> 9621 Working-age population with primary education, primary education and below (% of population with low education in working age)
#> 9622 Working-age population with primary education, male (% of male population in working age)
#> 9623 Working-age population with primary education, aged 25-64 (% of population aged 25-64)
#> 9624 Working-age population with primary education, rural (% of rural population in working age)
#> 9625 Working-age population with primary education, urban (% of urban population in working age)
#> 9626 Working-age population with primary education, aged 15-24 (% of population aged 15-24)
#> 9627 Working-age population with primary education, total (% of total population in working age)
#> 9628 Working-age population with secondary education, female (% of female population in working age)
#> 9629 Working-age population with secondary education, above primary education (% of population with high education in working age)
#> 9630 Working-age population with secondary education, male (% of male population in working age)
#> 9631 Working-age population with secondary education, aged 25-64 (% of population aged 25-64)
#> 9632 Working-age population with post-secondary education, female (% of female population in working age)
#> 9633 Working-age population with post-secondary education, above primary education (% of population with high education in working age)
#> 9634 Working-age population with post-secondary education, male (% of male population in working age)
#> 9635 Working-age population with post-secondary education, aged 25-64 (% of population aged 25-64)
#> 9636 Working-age population with post-secondary education, rural (% of rural population in working age)
#> 9637 Working-age population with post-secondary education, urban (% of urban population in working age)
#> 9638 Working-age population with post-secondary education, aged 15-24 (% of population aged 15-24)
#> 9639 Working-age population with post-secondary education, total (% of total population in working age)
#> 9640 Working-age population with secondary education, rural (% of rural population in working age)
#> 9641 Working-age population with secondary education, urban (% of urban population in working age)
#> 9642 Working-age population with secondary education, aged 15-24 (% of population aged 15-24)
#> 9643 Working-age population with secondary education, total (% of total population in working age)
#> 9644 Total sample population
#> 9645 Total sample population, female
#> 9646 Total sample population, above primary education
#> 9647 Total sample population, primary education and below
#> 9648 Total sample population, male
#> 9649 Total sample population, aged 25-64
#> 9650 Total sample population, rural
#> 9651 Total sample population, urban
#> 9652 Total sample population, aged 15-24
#> 9653 Urban population, female (% of female population)
#> 9654 Urban population, above primary education (% of population with high education)
#> 9655 Urban population, primary education and below (% of population with low education)
#> 9656 Urban population, male (% of male population)
#> 9657 Urban population, aged 25-64 (% of population aged 25-64)
#> 9658 Urban population, aged 15-24 (% of population aged 15-24)
#> 9659 Urban population, total (% of total population)
#> 9696 Underemployment, less than 35 hours per week, aged 15-64, female (% of female employed population in working age)
#> 9697 Underemployment, less than 35 hours per week, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9698 Underemployment, less than 35 hours per week, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9699 Underemployment, less than 35 hours per week, aged 15-64, male (% of male employed population in working age)
#> 9700 Underemployment, less than 35 hours per week, aged 25-64 (% of employed population aged 25-64)
#> 9701 Underemployment, less than 35 hours per week, aged 15-64, rural (% of rural employed population in working age)
#> 9702 Underemployment, less than 35 hours per week, aged 15-64, urban (% of urban employed population in working age)
#> 9703 Underemployment, less than 35 hours per week, aged 15-24 (% of employed population aged 15-24)
#> 9704 Underemployment, less than 35 hours per week, aged 15-64, total (% of total employed population in working age)
#> 9705 Excessive working hours, more than 48 hours per week, aged 15-64, female (% of female employed population in working age)
#> 9706 Excessive working hours, more than 48 hours per week, aged 15-64, above primary education (% of employed population with high education in working age)
#> 9707 Excessive working hours, more than 48 hours per week, aged 15-64, primary education and below (% of employed population with low education in working age)
#> 9708 Excessive working hours, more than 48 hours per week, aged 15-64, male (% of male employed population in working age)
#> 9709 Excessive working hours, more than 48 hours per week, aged 25-64 (% of employed population aged 25-64)
#> 9710 Excessive working hours, more than 48 hours per week, aged 15-64, rural (% of rural employed population in working age)
#> 9711 Excessive working hours, more than 48 hours per week, aged 15-64, urban (% of urban employed population in working age)
#> 9712 Excessive working hours, more than 48 hours per week, aged 15-24 (% of employed population aged 15-24)
#> 9713 Excessive working hours, more than 48 hours per week, aged 15-64, total (% of total employed population in working age)
#> 9748 Youth not in employment or education, aged 15-24, female (% of female youth population)
#> 9749 Youth not in employment or education, aged 15-24, above primary education (% of youth population with high education)
#> 9750 Youth not in employment or education, aged 15-24, primary education and below (% of youth population with low education)
#> 9751 Youth not in employment or education, aged 15-24, male (% of male youth population)
#> 9752 Youth not in employment or education, aged 15-24, rural (% of rural youth population)
#> 9753 Youth not in employment or education, aged 15-24, urban (% of urban youth population)
#> 9754 Youth not in employment or education, aged 15-24, total (% of total youth population)
#> 9826 Coverage of unemployment benefits and ALMP (% of population)
#> 11715 Coverage of social protection and labor programs (% of population)
#> 11993 Coverage of unemployment benefits and ALMP (% of population)
#> 11997 Coverage of unemployment benefits and ALMP in poorest quintile (% of population)
#> 12001 Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)
#> 12005 Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)
#> 12009 Coverage of unemployment benefits and ALMP in 4th quintile (% of population)
#> 12013 Coverage of unemployment benefits and ALMP in richest quintile (% of population)
#> 12165 Population in extreme poor (<$1.9 a day) only receiving Labor Market (%, preT)
#> 12166 Population in extreme poor (<$1.9 a day) only receiving Labor Market (%)
#> 12167 Population only receiving Labor Market (%, preT)
#> 12168 Population only receiving Labor Market (%) -rural
#> 12169 Population only receiving Labor Market (%)
#> 12170 Population only receiving Labor Market (%) -urban
#> 12171 Population in the 1st quintile (poorest) only receiving Labor Market (%, preT)
#> 12172 Population in the 1st quintile (poorest) only receiving Labor Market (%) -rural
#> 12173 Population in the 1st quintile (poorest) only receiving Labor Market (%)
#> 12174 Population in the 1st quintile (poorest) only receiving Labor Market (%) -urban
#> 12175 Population in extreme poor (<$1.9 a day) not receiving Social Protection (%, preT)
#> 12176 Population in extreme poor (<$1.9 a day) not receiving Social Protection (%)
#> 12177 Population not receiving Social Protection (%, preT)
#> 12178 Population not receiving Social Protection (%) -rural
#> 12179 Population not receiving Social Protection (%)
#> 12180 Population not receiving Social Protection (%) -urban
#> 12181 Population in the 1st quintile (poorest) not receiving Social Protection (%, preT)
#> 12182 Population in the 1st quintile (poorest) not receiving Social Protection (%) -rural
#> 12183 Population in the 1st quintile (poorest) not receiving Social Protection (%)
#> 12184 Population in the 1st quintile (poorest) not receiving Social Protection (%) -urban
#> 12185 Population in extreme poor (<$1.9 a day) receiving only 1 program (%, preT)
#> 12186 Population in extreme poor (<$1.9 a day) receiving only 1 program (%)
#> 12187 Population receiving only 1 program (%, preT)
#> 12188 Population receiving only 1 program (%) -rural
#> 12189 Population receiving only 1 program (%)
#> 12190 Population receiving only 1 program (%) -urban
#> 12191 Population in the 1st quintile (poorest) receiving 1 program (%, preT)
#> 12192 Population in the 1st quintile (poorest) receiving 1 program (%) -rural
#> 12193 Population in the 1st quintile (poorest) receiving 1 program (%)
#> 12194 Population in the 1st quintile (poorest) receiving 1 program (%) -urban
#> 12195 Population in extreme poor (<$1.9 a day) receiving 2 programs (%, preT)
#> 12196 Population in extreme poor (<$1.9 a day) receiving 2 programs (%)
#> 12197 Population receiving 2 programs (%, preT)
#> 12198 Population receiving 2 programs (%) -rural
#> 12199 Population receiving 2 programs (%)
#> 12200 Population receiving 2 programs (%) -urban
#> 12201 Population in the 1st quintile (poorest) receiving 2 programs (%, preT)
#> 12202 Population in the 1st quintile (poorest) receiving 2 programs (%) -rural
#> 12203 Population in the 1st quintile (poorest) receiving 2 programs (%)
#> 12204 Population in the 1st quintile (poorest) receiving 2 programs (%) -urban
#> 12205 Population in extreme poor (<$1.9 a day) receiving 3 programs (%, preT)
#> 12206 Population in extreme poor (<$1.9 a day) receiving 3 programs (%)
#> 12207 Population receiving 3 programs (%, preT)
#> 12208 Population receiving 3 programs (%) -rural
#> 12209 Population receiving 3 programs (%)
#> 12210 Population receiving 3 programs (%) -urban
#> 12211 Population in the 1st quintile (poorest) receiving 3 programs (%, preT)
#> 12212 Population in the 1st quintile (poorest) receiving 3 programs (%) -rural
#> 12213 Population in the 1st quintile (poorest) receiving 3 programs (%)
#> 12214 Population in the 1st quintile (poorest) receiving 3 programs (%) -urban
#> 12215 Population in extreme poor (<$1.9 a day) receiving 4 or more programs (%, preT)
#> 12216 Population in extreme poor (<$1.9 a day) receiving 4 or more programs (%)
#> 12217 Population receiving 4 or more programs (%, preT)
#> 12218 Population receiving 4 or more programs (%) -rural
#> 12219 Population receiving 4 or more programs (%)
#> 12220 Population receiving 4 or more programs (%) -urban
#> 12221 Population in the 1st quintile (poorest) receiving 4 or more programs (%, preT)
#> 12222 Population in the 1st quintile (poorest) receiving 4 or more programs (%) -rural
#> 12223 Population in the 1st quintile (poorest) receiving 4 or more programs (%)
#> 12224 Population in the 1st quintile (poorest) receiving 4 or more programs (%) -urban
#> 12748 Coverage of social safety net programs (% of population)
#> 12752 Coverage of social safety net programs in poorest quintile (% of population)
#> 12756 Coverage of social safety net programs in 2nd quintile (% of population)
#> 12760 Coverage of social safety net programs in 3rd quintile (% of population)
#> 12764 Coverage of social safety net programs in 4th quintile (% of population)
#> 12768 Coverage of social safety net programs in richest quintile (% of population)
#> 13893 Population in extreme poor (<$1.9 a day) only receiving All Social Assistance (%, preT)
#> 13894 Population in extreme poor (<$1.9 a day) only receiving All Social Assistance (%)
#> 13895 Population only receiving All Social Assistance (%, preT)
#> 13896 Population only receiving All Social Assistance (%) -rural
#> 13897 Population only receiving All Social Assistance (%)
#> 13898 Population only receiving All Social Assistance (%) -urban
#> 13899 Population in the 1st quintile (poorest) only receiving All Social Assistance (%, preT)
#> 13900 Population in the 1st quintile (poorest) only receiving All Social Assistance (%) -rural
#> 13901 Population in the 1st quintile (poorest) only receiving All Social Assistance (%)
#> 13902 Population in the 1st quintile (poorest) only receiving All Social Assistance (%) -urban
#> 13903 Population in extreme poor (<$1.9 a day) receiving Social Assistance and Other (%, preT)
#> 13904 Population in extreme poor (<$1.9 a day) receiving Social Assistance and Other (%)
#> 13905 Population receiving Social Assistance and Other (%, preT)
#> 13906 Population receiving Social Assistance and Other (%) -rural
#> 13907 Population receiving Social Assistance and Other (%)
#> 13908 Population receiving Social Assistance and Other (%) -urban
#> 13909 Population in the 1st quintile (poorest) receiving Social Assistance and Other (%, preT)
#> 13910 Population in the 1st quintile (poorest) receiving Social Assistance and Other (%) -rural
#> 13911 Population in the 1st quintile (poorest) receiving Social Assistance and Other (%)
#> 13912 Population in the 1st quintile (poorest) receiving Social Assistance and Other (%) -urban
#> 14019 Coverage of social insurance programs (% of population)
#> 14023 Coverage of social insurance programs in poorest quintile (% of population)
#> 14027 Coverage of social insurance programs in 2nd quintile (% of population)
#> 14031 Coverage of social insurance programs in 3rd quintile (% of population)
#> 14035 Coverage of social insurance programs in 4th quintile (% of population)
#> 14039 Coverage of social insurance programs in richest quintile (% of population)
#> 14330 Population in extreme poor (<$1.9 a day) receiving All Social Insurance and Labor Market (%, preT)
#> 14331 Population in extreme poor (<$1.9 a day) receiving All Social Insurance and Labor Market (%)
#> 14332 Population receiving All Social Insurance and Labor Market (%, preT)
#> 14333 Population receiving All Social Insurance and Labor Market (%) -rural
#> 14334 Population receiving All Social Insurance and Labor Market (%)
#> 14335 Population receiving All Social Insurance and Labor Market (%) -urban
#> 14336 Population in the 1st quintile (poorest) receiving All Social Insurance and Labor Market (%, preT)
#> 14337 Population in the 1st quintile (poorest) receiving All Social Insurance and Labor Market (%) -rural
#> 14338 Population in the 1st quintile (poorest) receiving All Social Insurance and Labor Market (%)
#> 14339 Population in the 1st quintile (poorest) receiving All Social Insurance and Labor Market (%) -urban
#> 14340 Population in extreme poor (<$1.9 a day) only receiving All Social Insurance (%, preT)
#> 14341 Population in extreme poor (<$1.9 a day) only receiving All Social Insurance (%)
#> 14342 Population only receiving All Social Insurance (%, preT)
#> 14343 Population only receiving All Social Insurance (%) -rural
#> 14344 Population only receiving All Social Insurance (%)
#> 14345 Population only receiving All Social Insurance (%) -urban
#> 14346 Population in the 1st quintile (poorest) only receiving All Social Insurance (%, preT)
#> 14347 Population in the 1st quintile (poorest) only receiving All Social Insurance (%) -rural
#> 14348 Population in the 1st quintile (poorest) only receiving All Social Insurance (%)
#> 14349 Population in the 1st quintile (poorest) only receiving All Social Insurance (%) -urban
#> 14446 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Female
#> 14447 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Male
#> 14448 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Total
#> 14449 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Female
#> 14450 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Male
#> 14451 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Total
#> 14452 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Female
#> 14453 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Male
#> 14454 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Total
#> 14455 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Female
#> 14456 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Male
#> 14457 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Total
#> 14458 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Female
#> 14459 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Male
#> 14460 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Total
#> 14461 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Female
#> 14462 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Male
#> 14463 Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Total
#> 14464 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Female
#> 14465 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Male
#> 14466 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Total
#> 14467 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Female
#> 14468 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Male
#> 14469 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Total
#> 14470 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Female
#> 14471 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Male
#> 14472 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Total
#> 14473 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Female
#> 14474 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Male
#> 14475 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Total
#> 14476 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Female
#> 14477 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Male
#> 14478 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Total
#> 14479 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Female
#> 14480 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Male
#> 14481 Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Total
#> 14482 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Female
#> 14483 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Male
#> 14484 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Total
#> 14485 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Female
#> 14486 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Male
#> 14487 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Total
#> 14488 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Female
#> 14489 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Male
#> 14490 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Total
#> 14491 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Female
#> 14492 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Male
#> 14493 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Total
#> 14494 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Female
#> 14495 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Male
#> 14496 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Total
#> 14497 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Female
#> 14498 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Male
#> 14499 Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Total
#> 14500 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Female
#> 14501 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Male
#> 14502 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Total
#> 14503 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Female
#> 14504 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Male
#> 14505 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Total
#> 14506 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Female
#> 14507 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Male
#> 14508 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Total
#> 14509 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Female
#> 14510 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Male
#> 14511 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Total
#> 14512 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Female
#> 14513 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Male
#> 14514 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Total
#> 14515 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Female
#> 14516 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Male
#> 14517 Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Total
#> 14518 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Female
#> 14519 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Male
#> 14520 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Total
#> 14521 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Female
#> 14522 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Male
#> 14523 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Total
#> 14524 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Female
#> 14525 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Male
#> 14526 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Total
#> 14527 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Female
#> 14528 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Male
#> 14529 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Total
#> 14530 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Female
#> 14531 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Male
#> 14532 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Total
#> 14533 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Female
#> 14534 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Male
#> 14535 Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Total
#> 14536 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Female
#> 14537 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Male
#> 14538 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Total
#> 14539 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Female
#> 14540 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Male
#> 14541 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Total
#> 14542 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Female
#> 14543 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Male
#> 14544 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Total
#> 14545 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Female
#> 14546 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Male
#> 14547 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Total
#> 14548 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Female
#> 14549 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Male
#> 14550 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Total
#> 14551 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Female
#> 14552 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Male
#> 14553 Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Total
#> 14554 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Female
#> 14555 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Male
#> 14556 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Total
#> 14557 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Female
#> 14558 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Male
#> 14559 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Total
#> 14560 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Female
#> 14561 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Male
#> 14562 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Total
#> 14563 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Female
#> 14564 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Male
#> 14565 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Total
#> 14566 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Female
#> 14567 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Male
#> 14568 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Total
#> 14569 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Female
#> 14570 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Male
#> 14571 Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Total
#> 14572 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Female
#> 14573 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Male
#> 14574 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Total
#> 14575 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Female
#> 14576 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Male
#> 14577 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Total
#> 14578 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Female
#> 14579 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Male
#> 14580 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Total
#> 14581 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Female
#> 14582 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Male
#> 14583 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Total
#> 14584 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Female
#> 14585 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Male
#> 14586 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Total
#> 14587 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Female
#> 14588 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Male
#> 14589 Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Total
#> 14590 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Female
#> 14591 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Male
#> 14592 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Total
#> 14593 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Female
#> 14594 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Male
#> 14595 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Total
#> 14596 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Female
#> 14597 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Male
#> 14598 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Total
#> 14599 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Female
#> 14600 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Male
#> 14601 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Total
#> 14602 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Female
#> 14603 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Male
#> 14604 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Total
#> 14605 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Female
#> 14606 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Male
#> 14607 Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Total
#> 14608 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Female
#> 14609 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Male
#> 14610 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Total
#> 14611 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Female
#> 14612 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Male
#> 14613 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Total
#> 14614 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Female
#> 14615 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Male
#> 14616 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Total
#> 14617 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Female
#> 14618 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Male
#> 14619 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Total
#> 14620 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Female
#> 14621 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Male
#> 14622 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Total
#> 14623 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Female
#> 14624 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Male
#> 14625 Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Total
#> 14626 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Female
#> 14627 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Male
#> 14628 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Total
#> 14629 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Female
#> 14630 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Male
#> 14631 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Total
#> 14632 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Female
#> 14633 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Male
#> 14634 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Total
#> 14635 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Female
#> 14636 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Male
#> 14637 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Total
#> 14638 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Female
#> 14639 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Male
#> 14640 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Total
#> 14641 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Female
#> 14642 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Male
#> 14643 Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Total
#> 14682 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Female
#> 14683 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Male
#> 14684 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Total
#> 14685 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Female
#> 14686 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Male
#> 14687 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Total
#> 14688 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Female
#> 14689 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Male
#> 14690 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Total
#> 14691 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Female
#> 14692 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Male
#> 14693 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Total
#> 14694 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Female
#> 14695 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Male
#> 14696 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Total
#> 14697 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Female
#> 14698 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Male
#> 14699 Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Total
#> 14700 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Female
#> 14701 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Male
#> 14702 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Total
#> 14703 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Female
#> 14704 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Male
#> 14705 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Total
#> 14706 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Female
#> 14707 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Male
#> 14708 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Total
#> 14709 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Female
#> 14710 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Male
#> 14711 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Total
#> 14712 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Female
#> 14713 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Male
#> 14714 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Total
#> 14715 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Female
#> 14716 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Male
#> 14717 Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Total
#> 14718 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Female
#> 14719 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Male
#> 14720 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Total
#> 14721 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Female
#> 14722 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Male
#> 14723 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Total
#> 14724 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Female
#> 14725 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Male
#> 14726 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Total
#> 14727 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Female
#> 14728 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Male
#> 14729 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Total
#> 14730 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Female
#> 14731 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Male
#> 14732 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Total
#> 14733 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Female
#> 14734 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Male
#> 14735 Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Total
#> 14736 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Female
#> 14737 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Male
#> 14738 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Total
#> 14739 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Female
#> 14740 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Male
#> 14741 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Total
#> 14742 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Female
#> 14743 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Male
#> 14744 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Total
#> 14745 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Female
#> 14746 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Male
#> 14747 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Total
#> 14748 Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Female
#> 14749 Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Male
#> 14750 Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Total
#> 14751 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Female
#> 14752 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Male
#> 14753 Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Total
#> 14841 Years of Population & Housing census
#> 15186 Literacy Rate for Population age 15 and over (in % of total population)
#> 15278 Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)
#> 15279 Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)
#> 15280 Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)
#> 15501 Net intake rate in grade 1, female (% of official school-age population)
#> 15502 Net intake rate in grade 1, male (% of official school-age population)
#> 15503 Net intake rate in grade 1 (% of official school-age population)
#> 15761 Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)
#> 15762 Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)
#> 15763 Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)
#> 15764 Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)
#> 15765 Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)
#> 15766 Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)
#> 15767 Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)
#> 15768 Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)
#> 15769 Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)
#> 15841 Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)
#> 15842 Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)
#> 15843 Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)
#> 15844 Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)
#> 15845 Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)
#> 15846 Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)
#> 15847 Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)
#> 15848 Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)
#> 15849 Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)
#> 15850 Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)
#> 15851 Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)
#> 15852 Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)
#> 16251 Inpatient admission rate (% of population )
#> 16258 Condom use, population ages 15-24, female (% of females ages 15-24)
#> 16259 Condom use, population ages 15-24, male (% of males ages 15-24)
#> 16269 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 0-4, female (% of female population ages 0-4)
#> 16270 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 0-4, male (% of male population ages 0-4)
#> 16271 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 0-4 (% of population ages 0-4)
#> 16272 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 5-14, female (% of female population ages 5-14)
#> 16273 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 5-14, male (% of male population ages 5-14)
#> 16274 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 5-14 (% of population ages 5-14)
#> 16275 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 15-59, female (% of female population ages 15-59)
#> 16276 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 15-59, male (% of male population ages 15-59)
#> 16277 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 15-59 (% of population ages 15-59)
#> 16278 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 60+, female (% of female population ages 60+)
#> 16279 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 60+, male (% of male population ages 60+)
#> 16280 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, ages 60+ (% of population ages 60+)
#> 16281 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, female (% of female population)
#> 16282 Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions, male (% of male population)
#> 16287 Cause of death, by injury, ages 0-4, female (% of female population ages 0-4)
#> 16288 Cause of death, by injury, ages 0-4, male (% of male population ages 0-4)
#> 16289 Cause of death, by injury, ages 0-4 (% of population ages 0-4)
#> 16290 Cause of death, by injury, ages 5-14, female (% of female population ages 5-14)
#> 16291 Cause of death, by injury, ages 5-14, male (% of male population ages 5-14)
#> 16292 Cause of death, by injury, ages 5-14 (% of population ages 5-14)
#> 16293 Cause of death, by injury, ages 15-59, female (% of female population ages 15-59)
#> 16294 Cause of death, by injury, ages 15-59, male (% of male population ages 15-59)
#> 16295 Cause of death, by injury, ages 15-59 (% of population ages 15-59)
#> 16296 Cause of death, by injury, ages 60+, female (% of female population ages 60+)
#> 16297 Cause of death, by injury, ages 60+, male (% of male population ages 60+)
#> 16298 Cause of death, by injury, ages 60+ (% of population ages 60+)
#> 16299 Cause of death, by injury, female (% of female population)
#> 16300 Cause of death, by injury, male (% of male population)
#> 16305 Cause of death, by non-communicable diseases, ages 0-4, female (% of female population ages 0-4)
#> 16306 Cause of death, by non-communicable diseases, ages 0-4, male (% of male population ages 0-4)
#> 16307 Cause of death, by non-communicable diseases, ages 0-4 (% of population ages 0-4)
#> 16308 Cause of death, by non-communicable diseases, ages 5-14, female (% of female population ages 5-14)
#> 16309 Cause of death, by non-communicable diseases, ages 5-14, male (% of male population ages 5-14)
#> 16310 Cause of death, by non-communicable diseases, ages 5-14 (% of population ages 5-14)
#> 16311 Cause of death, by non-communicable diseases, ages 15-59, female (% of female population ages 15-59)
#> 16312 Cause of death, by non-communicable diseases, ages 15-59, male (% of male population ages 15-59)
#> 16313 Cause of death, by non-communicable diseases, ages 15-59 (% of population ages 15-59)
#> 16314 Cause of death, by non-communicable diseases, ages 60+, female (% of female population ages 60+)
#> 16315 Cause of death, by non-communicable diseases, ages 60+, male (% of male population ages 60+)
#> 16316 Cause of death, by non-communicable diseases, ages 60+ (% of population ages 60+)
#> 16317 Cause of death, by non-communicable diseases, female (% of female population)
#> 16318 Cause of death, by non-communicable diseases, male (% of male population)
#> 16330 Women's share of population ages 15+ living with HIV (%)
#> 16333 Prevalence of HIV, total (% of population ages 15-49)
#> 16421 People using at least basic drinking water services (% of population): Q1 (lowest)
#> 16422 People using at least basic drinking water services (% of population): Q2
#> 16423 People using at least basic drinking water services (% of population): Q3
#> 16424 People using at least basic drinking water services (% of population): Q4
#> 16425 People using at least basic drinking water services (% of population): Q5 (highest)
#> 16426 People using at least basic drinking water services, rural (% of rural population): Q1 (lowest)
#> 16427 People using at least basic drinking water services, rural (% of rural population): Q2
#> 16428 People using at least basic drinking water services, rural (% of rural population): Q3
#> 16429 People using at least basic drinking water services, rural (% of rural population): Q4
#> 16430 People using at least basic drinking water services, rural (% of rural population): Q5 (highest)
#> 16431 People using at least basic drinking water services, rural (% of rural population)
#> 16432 People using at least basic drinking water services, urban (% of urban population): Q1 (lowest)
#> 16433 People using at least basic drinking water services, urban (% of urban population): Q2
#> 16434 People using at least basic drinking water services, urban (% of urban population): Q3
#> 16435 People using at least basic drinking water services, urban (% of urban population): Q4
#> 16436 People using at least basic drinking water services, urban (% of urban population): Q5 (highest)
#> 16437 People using at least basic drinking water services, urban (% of urban population)
#> 16438 People using at least basic drinking water services (% of population)
#> 16439 Improved water source, rural (% of rural population with access)
#> 16440 Improved water source, urban (% of urban population with access)
#> 16441 Improved water source (% of population with access)
#> 16442 People using safely managed drinking water services, rural (% of rural population)
#> 16443 People using safely managed drinking water services, urban (% of urban population)
#> 16444 People using safely managed drinking water services (% of population)
#> 16462 Incidence of HIV, ages 50+ (per 1,000 uninfected population ages 50+)
#> 16463 Incidence of HIV, ages 15-49, female (per 1,000 uninfected female population ages 15-49)
#> 16464 Incidence of HIV, ages 15-49, male (per 1,000 uninfected male population ages 15-49)
#> 16466 Incidence of HIV, all (per 1,000 uninfected population)
#> 16468 Incidence of HIV, ages 15-24, female (per 1,000 uninfected female population ages 15-24)
#> 16469 Incidence of HIV, ages 15-24, male (per 1,000 uninfected male population ages 15-24)
#> 16470 Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)
#> 16471 Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)
#> 16505 Immunization Coverage for Children under 5 years old (in % of children population under 5 years old)
#> 16537 Population per nurse
#> 16539 Specialist surgical workforce (per 100,000 population)
#> 16544 Incidence of malaria (per 1,000 population at risk)
#> 16567 Use of insecticide-treated bed nets (% of under-5 population)
#> 16618 Number of surgical procedures (per 100,000 population)
#> 16619 Health care (% of population with access)
#> 16620 Improved sanitation facilities (% of population with access)
#> 16621 Improved sanitation facilities, rural (% of rural population with access)
#> 16622 Improved sanitation facilities, urban (% of urban population with access)
#> 16623 Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)
#> 16624 Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)
#> 16625 Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)
#> 16655 People using at least basic sanitation services (% of population): Q1 (lowest)
#> 16656 People using at least basic sanitation services (% of population): Q2
#> 16657 People using at least basic sanitation services (% of population): Q3
#> 16658 People using at least basic sanitation services (% of population): Q4
#> 16659 People using at least basic sanitation services (% of population): Q5 (highest)
#> 16660 People using at least basic sanitation services, rural (% of rural population): Q1 (lowest)
#> 16661 People using at least basic sanitation services, rural (% of rural population): Q2
#> 16662 People using at least basic sanitation services, rural (% of rural population): Q3
#> 16663 People using at least basic sanitation services, rural (% of rural population): Q4
#> 16664 People using at least basic sanitation services, rural (% of rural population): Q5 (highest)
#> 16665 People using at least basic sanitation services, rural (% of rural population)
#> 16666 People using at least basic sanitation services, urban (% of urban population): Q1 (lowest)
#> 16667 People using at least basic sanitation services, urban (% of urban population): Q2
#> 16668 People using at least basic sanitation services, urban (% of urban population): Q3
#> 16669 People using at least basic sanitation services, urban (% of urban population): Q4
#> 16670 People using at least basic sanitation services, urban (% of urban population): Q5 (highest)
#> 16671 People using at least basic sanitation services, urban (% of urban population)
#> 16672 People using at least basic sanitation services (% of population)
#> 16696 Diabetes prevalence (% of population ages 20 to 79)
#> 16714 People with basic handwashing facilities including soap and water (% of population): Q1 (lowest)
#> 16715 People with basic handwashing facilities including soap and water (% of population): Q2
#> 16716 People with basic handwashing facilities including soap and water (% of population): Q3
#> 16717 People with basic handwashing facilities including soap and water (% of population): Q4
#> 16718 People with basic handwashing facilities including soap and water (% of population): Q5 (highest)
#> 16719 People with basic handwashing facilities including soap and water, rural (% of rural population): Q1 (lowest)
#> 16720 People with basic handwashing facilities including soap and water, rural (% of rural population): Q2
#> 16721 People with basic handwashing facilities including soap and water, rural (% of rural population): Q3
#> 16722 People with basic handwashing facilities including soap and water, rural (% of rural population): Q4
#> 16723 People with basic handwashing facilities including soap and water, rural (% of rural population): Q5 (highest)
#> 16724 People with basic handwashing facilities including soap and water, rural (% of rural population)
#> 16725 People with basic handwashing facilities including soap and water, urban (% of urban population): Q1 (lowest)
#> 16726 People with basic handwashing facilities including soap and water, urban (% of urban population): Q2
#> 16727 People with basic handwashing facilities including soap and water, urban (% of urban population): Q3
#> 16728 People with basic handwashing facilities including soap and water, urban (% of urban population): Q4
#> 16729 People with basic handwashing facilities including soap and water, urban (% of urban population): Q5 (highest)
#> 16730 People with basic handwashing facilities including soap and water, urban (% of urban population)
#> 16731 People with basic handwashing facilities including soap and water (% of population)
#> 16754 Prevalence of obesity, female (% of female population ages 18+)
#> 16755 Prevalence of obesity, male (% of male population ages 18+)
#> 16756 People practicing open defecation (% of population): Q1 (lowest)
#> 16757 People practicing open defecation (% of population): Q2
#> 16758 People practicing open defecation (% of population): Q3
#> 16759 People practicing open defecation (% of population): Q4
#> 16760 People practicing open defecation (% of population): Q5 (highest)
#> 16761 People practicing open defecation, rural (% of rural population): Q1 (lowest)
#> 16762 People practicing open defecation, rural (% of rural population): Q2
#> 16763 People practicing open defecation, rural (% of rural population): Q3
#> 16764 People practicing open defecation, rural (% of rural population): Q4
#> 16765 People practicing open defecation, rural (% of rural population): Q5 (highest)
#> 16766 People practicing open defecation, rural (% of rural population)
#> 16767 People practicing open defecation, urban (% of urban population): Q1 (lowest)
#> 16768 People practicing open defecation, urban (% of urban population): Q2
#> 16769 People practicing open defecation, urban (% of urban population): Q3
#> 16770 People practicing open defecation, urban (% of urban population): Q4
#> 16771 People practicing open defecation, urban (% of urban population): Q5 (highest)
#> 16772 People practicing open defecation, urban (% of urban population)
#> 16773 People practicing open defecation (% of population)
#> 16799 Mortality rate attributed to unintentional poisoning (per 100,000 population)
#> 16800 Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)
#> 16801 Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)
#> 16802 People using safely managed sanitation services, rural (% of rural population)
#> 16803 People using safely managed sanitation services, urban (% of urban population)
#> 16804 People using safely managed sanitation services (% of population)
#> 16819 Suicide mortality rate, female (per 100,000 female population)
#> 16820 Suicide mortality rate, male (per 100,000 male population)
#> 16821 Suicide mortality rate (per 100,000 population)
#> 16822 Mortality caused by road traffic injury, female (per 100,000 female population)
#> 16823 Mortality caused by road traffic injury, male (per 100,000 male population)
#> 16824 Mortality caused by road traffic injury (per 100,000 population)
#> 16825 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, female (per 100,000 female population)
#> 16826 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, male (per 100,000 male population)
#> 16827 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)
#> 16851 Deaths due to tuberculosis among HIV-negative people, high uncertainty bound (per 100,000 population)
#> 16852 Deaths due to tuberculosis among HIV-negative people, low uncertainty bound (per 100,000 population)
#> 16853 Tuberculosis prevalence rate (per 1000,000 population, WHO)
#> 16854 Tuberculosis prevalence rate, high uncertainty bound (per 1000,000 population, WHO)
#> 16855 Tuberculosis prevalence rate, low uncertainty bound (per 1000,000 population, WHO)
#> 16857 Proportion of population pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure (%)
#> 16859 Proportion of population pushed further below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 16861 Proportion of population pushed further below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 16863 Proportion of population pushed further below the 60% median consumption poverty line by out-of-pocket health care expenditure (%)
#> 16867 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 16871 Proportion of population pushed below the $3.20 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)
#> 16873 Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)
#> 16875 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)
#> 16877 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)
#> 16930 Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population)
#> 16931 Multidimensional poverty, Educational attainment (% of population deprived)
#> 16933 Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
#> 16934 Poverty headcount ratio at $1.90 a day, age 0-14 (2011 PPP) (% of population age 0-14)
#> 16935 Poverty headcount ratio at $1.90 a day, age 15-64 (2011 PPP) (% of population age 15-64)
#> 16936 Poverty headcount ratio at $1.90 a day, without education (2011 PPP) (% of population age 16+ without education)
#> 16937 Poverty headcount ratio at $1.90 a day, with primary education (2011 PPP) (% of population age 16+ with primary education)
#> 16938 Poverty headcount ratio at $1.90 a day, with secondary education (2011 PPP) (% of population age 16+ with secondary education)
#> 16939 Poverty headcount ratio at $1.90 a day, with Tertiary/post-secondary education (2011 PPP) (% of population age 16+ with Tertiary/post-secondary education)
#> 16940 Poverty headcount ratio at $1.90 a day, age 65+ (2011 PPP) (% of population age 65+)
#> 16942 Poverty headcount ratio at $1.90 a day, Female (2011 PPP) (% of female population)
#> 16943 Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population), first comparable values
#> 16944 Poverty headcount ratio at $1.90 a day, Male (2011 PPP) (% of male population)
#> 16945 Multidimensional poverty, Monetary poverty (% of population deprived)
#> 16946 Poverty headcount ratio at $1.90 a day, rural (2011 PPP) (% of rural population)
#> 16947 Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population), second comparable values
#> 16948 Share of total poor population (at $1.90 a day, 2011 PPP)
#> 16949 Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population), third comparable values
#> 16950 Poverty headcount ratio at $1.90 a day, urban (2011 PPP) (% of urban population)
#> 16951 Multidimensional poverty, Electricity (% of population deprived)
#> 16952 Multidimensional poverty, Educational enrollment (% of population deprived)
#> 16959 Multidimensional poverty, Headcount ratio (% of population)
#> 16960 Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population)
#> 16961 Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population), first comparable values
#> 16964 Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population), second comparable values
#> 16965 Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population), third comparable values
#> 16966 Multidimensional poverty headcount ratio (% of total population)
#> 16967 Multidimensional poverty headcount ratio, children (% of population ages 0-17)
#> 16968 Multidimensional poverty index, children (population ages 0-17) (scale 0-1)
#> 16969 Multidimensional poverty headcount ratio, female (% of female population)
#> 16972 Multidimensional poverty headcount ratio, male (% of male population)
#> 16976 Poverty headcount ratio at national poverty lines (% of population)
#> 16977 Poverty headcount ratio at national poverty lines (% of population), including noncomparable values
#> 16979 Poverty Rate (in % of population)
#> 16985 Rural poverty headcount ratio at national poverty lines (% of rural population)
#> 16986 Multidimensional poverty, Sanitation (% of population deprived)
#> 16987 Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population)
#> 16988 Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population), first comparable values
#> 16991 Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population), second comparable values
#> 16992 Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population), third comparable values
#> 16994 Urban poverty headcount ratio at national poverty lines (% of urban population)
#> 16995 Multidimensional poverty, Drinking water (% of population deprived)
#> 17001 Population living below 50 percent of median income or consumption (% of population)
#> 17002 Survey mean consumption or income per capita, bottom 40% of population (2011 PPP $ per day)
#> 17004 Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)
#> 17005 Survey mean consumption or income per capita, total population (2011 PPP $ per day)
#> 17006 Survey mean consumption or income per capita, total population (2005 PPP $ per day)
#> 17007 Annualized average growth rate in per capita real survey mean consumption or income, total population (%)
#> 17023 Employment to population ratio, ages 15-24, female (%) (national estimate)
#> 17024 Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)
#> 17025 Employment to population ratio, ages 15-24, male (%) (national estimate)
#> 17026 Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)
#> 17027 Employment to population ratio, ages 15-24, total (%) (national estimate)
#> 17028 Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)
#> 17050 Employment to population ratio, 15+, female (%) (national estimate)
#> 17051 Employment to population ratio, 15+, female (%) (modeled ILO estimate)
#> 17052 Employment to population ratio, 15+, male (%) (national estimate)
#> 17053 Employment to population ratio, 15+, male (%) (modeled ILO estimate)
#> 17054 Employment to population ratio, 15+, total (%) (national estimate)
#> 17055 Employment to population ratio, 15+, total (%) (modeled ILO estimate)
#> 17127 Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)
#> 17128 Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)
#> 17129 Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)
#> 17130 Labor force with advanced education, female (% of female working-age population with advanced education)
#> 17131 Labor force with advanced education, male (% of male working-age population with advanced education)
#> 17132 Labor force with advanced education (% of total working-age population with advanced education)
#> 17133 Labor force with basic education, female (% of female working-age population with basic education)
#> 17134 Labor force with basic education, male (% of male working-age population with basic education)
#> 17135 Labor force with basic education (% of total working-age population with basic education)
#> 17136 Labor participation rate, female (% of female population ages 25-34)
#> 17137 Labor participation rate, male (% of male population ages 25-34)
#> 17138 Labor participation rate, total (% of total population ages 25-34)
#> 17139 Labor participation rate, female (% of female population ages 25-54)
#> 17140 Labor participation rate, male (% of male population ages 25-54)
#> 17141 Labor participation rate, total (% of total population ages 25-54)
#> 17142 Labor participation rate, female (% of female population ages 35-54)
#> 17143 Labor participation rate, male (% of male population ages 35-54)
#> 17144 Labor participation rate, total (% of total population ages 35-54)
#> 17145 Labor participation rate, female (% of female population ages 55-64)
#> 17146 Labor participation rate, male (% of male population ages 55-64)
#> 17147 Labor participation rate, total (% of total population ages 55-64)
#> 17148 Labor participation rate, female (% of female population ages 65+)
#> 17149 Labor participation rate, male (% of male population ages 65+)
#> 17150 Labor participation rate, total (% of total population ages 65+)
#> 17151 Labor force participation rate, female (% of female population ages 15+) (national estimate)
#> 17152 Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)
#> 17155 Labor force participation rate, male (% of male population ages 15+) (national estimate)
#> 17156 Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)
#> 17157 Labor force participation rate, total (% of total population ages 15+) (national estimate)
#> 17158 Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)
#> 17161 Labor force with intermediate education, female (% of female working-age population with intermediate education)
#> 17162 Labor force with intermediate education, male (% of male working-age population with intermediate education)
#> 17163 Labor force with intermediate education (% of total working-age population with intermediate education)
#> 17204 Share of youth not in education, employment or training, female (% of female youth population)
#> 17205 Share of youth not in education, employment or training, male (% of male youth population)
#> 17206 Share of youth not in education, employment or training, total (% of youth population)
#> 17226 Emigration rate of tertiary educated (% of total tertiary educated population)
#> 17228 Foreign population
#> 17229 Foreign population (% of total population)
#> 17231 Inflows of foreign population
#> 17233 Refugee population by country or territory of asylum
#> 17234 Refugee population by country or territory of origin
#> 17236 International migrant stock (% of population)
#> 17240 Prevalence of undernourishment (population)
#> 17241 Prevalence of undernourishment (% of population)
#> 17244 Prevalence of moderate or severe food insecurity in the population (%)
#> 17246 Prevalence of severe food insecurity in the population (%)
#> 17267 Crude Birth Rate (per 1000 population)
#> 17274 Crude Birth Rate (per thousand population)
#> 17342 Population ages 00-04, female
#> 17343 Population ages 00-04, female (% of female population)
#> 17344 Population ages 00-04, male
#> 17345 Population ages 00-04, male (% of male population)
#> 17346 Population ages 0-14, female
#> 17347 Population ages 0-14, female (% of female population)
#> 17348 Population ages 0-14, male
#> 17349 Population ages 0-14, male (% of male population)
#> 17350 Population ages 0-14, total
#> 17351 Population ages 0-14 (% of total population)
#> 17352 Population 0-24 (% of total population)
#> 17353 Population, ages 3-5, female
#> 17354 Population, ages 3-5, male
#> 17355 Population, ages 3-5, total
#> 17356 Population, ages 4-6, female
#> 17357 Population, ages 4-6, male
#> 17358 Population, ages 4-6, total
#> 17359 Population ages 05-09, female
#> 17360 Population ages 05-09, female (% of female population)
#> 17361 Population, ages 5-9, female
#> 17362 Population ages 05-09, male
#> 17363 Population ages 05-09, male (% of male population)
#> 17364 Population, ages 5-9, male
#> 17365 Population, ages 5-9, total
#> 17366 Population, ages 5-10, female
#> 17367 Population, ages 5-10, male
#> 17368 Population, ages 5-10, total
#> 17369 Population, ages 5-11, female
#> 17370 Population, ages 5-11, male
#> 17371 Population, ages 5-11, total
#> 17372 Population, ages 6-9, female
#> 17373 Population, ages 6-9, male
#> 17374 Population, ages 6-9, total
#> 17375 Population, ages 6-10, female
#> 17376 Population, ages 6-10, male
#> 17377 Population, ages 6-10, total
#> 17378 Population, ages 6-11, female
#> 17379 Population, ages 6-11, male
#> 17380 Population, ages 6-11, total
#> 17381 Population, ages 6-12, female
#> 17382 Population, ages 6-12, male
#> 17383 Population, ages 6-12, total
#> 17384 Population, ages 7-9, female
#> 17385 Population, ages 7-9, male
#> 17386 Population, ages 7-9, total
#> 17387 Population, ages 7-10, female
#> 17388 Population, ages 7-10, male
#> 17389 Population, ages 7-10, total
#> 17390 Population, ages 7-11, female
#> 17391 Population, ages 7-11, male
#> 17392 Population, ages 7-11, total
#> 17393 Population, ages 7-12, female
#> 17394 Population, ages 7-12, male
#> 17395 Population, ages 7-12, total
#> 17396 Population, ages 7-13, female
#> 17397 Population, ages 7-13, male
#> 17398 Population, ages 7-13, total
#> 17399 Population ages 10-14, female
#> 17400 Population ages 10-14, female (% of female population)
#> 17401 Population, ages 10-14, female
#> 17402 Population ages 10-14, male
#> 17403 Population ages 10-14, male (% of male population)
#> 17404 Population, ages 10-14, male
#> 17405 Population, ages 10-14, total
#> 17406 Population, ages 10-15, female
#> 17407 Population, ages 10-15, male
#> 17408 Population, ages 10-15, total
#> 17409 Population, ages 10-16, female
#> 17410 Population, ages 10-16, male
#> 17411 Population, ages 10-16, total
#> 17412 Population, ages 10-17, female
#> 17413 Population, ages 10-17, male
#> 17414 Population, ages 10-17, total
#> 17415 Population, ages 10-18, female
#> 17416 Population, ages 10-18, male
#> 17417 Population, ages 10-18, total
#> 17418 Population, ages 11-15, female
#> 17419 Population, ages 11-15, male
#> 17420 Population, ages 11-15, total
#> 17421 Population, ages 11-16, female
#> 17422 Population, ages 11-16, male
#> 17423 Population, ages 11-16, total
#> 17424 Population, ages 11-17, female
#> 17425 Population, ages 11-17, male
#> 17426 Population, ages 11-17, total
#> 17427 Population, ages 11-18, female
#> 17428 Population, ages 11-18, male
#> 17429 Population, ages 11-18, total
#> 17430 Population, ages 12-15, female
#> 17431 Population, ages 12-15, male
#> 17432 Population, ages 12-15, total
#> 17433 Population, ages 12-16, female
#> 17434 Population, ages 12-16, male
#> 17435 Population, ages 12-16, total
#> 17436 Population, ages 12-17, female
#> 17437 Population, ages 12-17, male
#> 17438 Population, ages 12-17, total
#> 17439 Population, ages 12-18, female
#> 17440 Population, ages 12-18, male
#> 17441 Population, ages 12-18, total
#> 17442 Population, ages 13-16, female
#> 17443 Population, ages 13-16, male
#> 17444 Population, ages 13-16, total
#> 17445 Population, ages 13-17, female
#> 17446 Population, ages 13-17, male
#> 17447 Population, ages 13-17, total
#> 17448 Population, ages 13-18, female
#> 17449 Population, ages 13-18, male
#> 17450 Population, ages 13-18, total
#> 17451 Population, ages 13-19, female
#> 17452 Population, ages 13-19, male
#> 17453 Population, ages 13-19, total
#> 17454 Population, ages 14-18, female
#> 17455 Population, ages 14-18, male
#> 17456 Population, ages 14-18, total
#> 17457 Population, ages 14-19, female
#> 17458 Population, ages 14-19, male
#> 17459 Population, ages 14-19, total
#> 17460 Population ages 15-19, female
#> 17461 Population ages 15-19, female (% of female population)
#> 17462 Population ages 15-19, male
#> 17463 Population ages 15-19, male (% of male population)
#> 17464 Population, ages 15-24, female
#> 17465 Population, ages 15-24, male
#> 17466 Population, ages 15-24, total
#> 17467 Population ages 15-64, female
#> 17468 Population ages 15-64, female (% of female population)
#> 17469 Population aged 15-64, total
#> 17470 Population ages 15-64 (% of total)
#> 17471 Population ages 15-64, male
#> 17472 Population ages 15-64, male (% of male population)
#> 17473 Population ages 15-64, total
#> 17474 Population ages 15-64 (% of total population)
#> 17475 Population ages 20-24, female
#> 17476 Population ages 20-24, female (% of female population)
#> 17477 Population ages 20-24, male
#> 17478 Population ages 20-24, male (% of male population)
#> 17479 Population ages 25-29, female
#> 17480 Population ages 25-29, female (% of female population)
#> 17481 Population ages 25-29, male
#> 17482 Population ages 25-29, male (% of male population)
#> 17483 Population ages 30-34, female
#> 17484 Population ages 30-34, female (% of female population)
#> 17485 Population ages 30-34, male
#> 17486 Population ages 30-34, male (% of male population)
#> 17487 Population ages 35-39, female
#> 17488 Population ages 35-39, female (% of female population)
#> 17489 Population ages 35-39, male
#> 17490 Population ages 35-39, male (% of male population)
#> 17491 Population ages 40-44, female
#> 17492 Population ages 40-44, female (% of female population)
#> 17493 Population ages 40-44, male
#> 17494 Population ages 40-44, male (% of male population)
#> 17495 Population ages 45-49, female
#> 17496 Population ages 45-49, female (% of female population)
#> 17497 Population ages 45-49, male
#> 17498 Population ages 45-49, male (% of male population)
#> 17499 Population ages 50-54, female
#> 17500 Population ages 50-54, female (% of female population)
#> 17501 Population ages 50-54, male
#> 17502 Population ages 50-54, male (% of male population)
#> 17503 Population ages 55-59, female
#> 17504 Population ages 55-59, female (% of female population)
#> 17505 Population ages 55-59, male
#> 17506 Population ages 55-59, male (% of male population)
#> 17507 Population ages 60-64, female
#> 17508 Population ages 60-64, female (% of female population)
#> 17509 Population ages 60-64, male
#> 17510 Population ages 60-64, male (% of male population)
#> 17511 Population ages 65-69, female
#> 17512 Population ages 65-69, female (% of female population)
#> 17513 Population ages 65-69, male
#> 17514 Population ages 65-69, male (% of male population)
#> 17515 Population ages 65 and above, female
#> 17516 Population ages 65 and above, female (% of female population)
#> 17517 Population ages 65 and above, male
#> 17518 Population ages 65 and above, male (% of male population)
#> 17520 Population ages 65 and above, total
#> 17521 Population ages 65 and above (% of total population)
#> 17522 Population ages 70-74, female
#> 17523 Population ages 70-74, female (% of female population)
#> 17524 Population ages 70-74, male
#> 17525 Population ages 70-74, male (% of male population)
#> 17526 Population ages 75-79, female
#> 17527 Population ages 75-79, female (% of female population)
#> 17528 Population ages 75-79, male
#> 17529 Population ages 75-79, male (% of male population)
#> 17530 Population ages 80 and above, female
#> 17531 Population ages 80 and above, female (% of female population)
#> 17532 Population ages 80 and above, male
#> 17533 Population ages 80 and above, male (% of male population)
#> 17534 Age population, age 00, female, interpolated
#> 17535 Population, age 0, female
#> 17536 Age population, age 00, male, interpolated
#> 17537 Population, age 0, male
#> 17538 Population, age 0, total
#> 17539 Age population, age 01, female, interpolated
#> 17540 Population, age 1, female
#> 17541 Age population, age 01, male, interpolated
#> 17542 Population, age 1, male
#> 17543 Population, age 1, total
#> 17544 Age population, age 02, female, interpolated
#> 17545 Population, age 2, female
#> 17546 Age population, age 02, male, interpolated
#> 17547 Population, age 2, male
#> 17548 Population, age 2, total
#> 17549 Age population, age 03, female, interpolated
#> 17550 Population, age 3, female
#> 17551 Age population, age 03, male, interpolated
#> 17552 Population, age 3, male
#> 17553 Population, age 3, total
#> 17554 Age population, age 04, female, interpolated
#> 17555 Population, age 4, female
#> 17556 Age population, age 04, male, interpolated
#> 17557 Population, age 4, male
#> 17558 Population, age 4, total
#> 17559 Age population, age 05, female, interpolated
#> 17560 Population, age 5, female
#> 17561 Age population, age 05, male, interpolated
#> 17562 Population, age 5, male
#> 17563 Population, age 5, total
#> 17564 Age population, age 06, female, interpolated
#> 17565 Population, age 6, female
#> 17566 Age population, age 06, male, interpolated
#> 17567 Population, age 6, male
#> 17568 Population, age 6, total
#> 17569 Age population, age 07, female, interpolated
#> 17570 Population, age 7, female
#> 17571 Age population, age 07, male, interpolated
#> 17572 Population, age 7, male
#> 17573 Population, age 7, total
#> 17574 Age population, age 08, female, interpolated
#> 17575 Population, age 8, female
#> 17576 Age population, age 08, male, interpolated
#> 17577 Population, age 8, male
#> 17578 Population, age 8, total
#> 17579 Age population, age 09, female, interpolated
#> 17580 Population, age 9, female
#> 17581 Age population, age 09, male, interpolated
#> 17582 Population, age 9, male
#> 17583 Population, age 9, total
#> 17584 Age population, age 10, female, interpolated
#> 17585 Population, age 10, female
#> 17586 Age population, age 10, male, interpolated
#> 17587 Population, age 10, male
#> 17588 Population, age 10, total
#> 17589 Age population, age 11, female, interpolated
#> 17590 Population, age 11, female
#> 17591 Age population, age 11, male, interpolated
#> 17592 Population, age 11, male
#> 17593 Population, age 11, total
#> 17594 Age population, age 12, female, interpolated
#> 17595 Population, age 12, female
#> 17596 Age population, age 12, male, interpolated
#> 17597 Population, age 12, male
#> 17598 Population, age 12, total
#> 17599 Age population, age 13, female, interpolated
#> 17600 Population, age 13, female
#> 17601 Age population, age 13, male, interpolated
#> 17602 Population, age 13, male
#> 17603 Population, age 13, total
#> 17604 Age population, age 14, female, interpolated
#> 17605 Population, age 14, female
#> 17606 Age population, age 14, male, interpolated
#> 17607 Population, age 14, male
#> 17608 Population, age 14, total
#> 17609 Age population, age 15, female, interpolated
#> 17610 Population, age 15, female
#> 17611 Age population, age 15, male, interpolated
#> 17612 Population, age 15, male
#> 17613 Population, age 15, total
#> 17614 Age population, age 16, female, interpolated
#> 17615 Population, age 16, female
#> 17616 Age population, age 16, male, interpolated
#> 17617 Population, age 16, male
#> 17618 Population, age 16, total
#> 17619 Age population, age 17, female, interpolated
#> 17620 Population, age 17, female
#> 17621 Age population, age 17, male, interpolated
#> 17622 Population, age 17, male
#> 17623 Population, age 17, total
#> 17624 Age population, age 18, female, interpolated
#> 17625 Population, age 18, female
#> 17626 Age population, age 18, male, interpolated
#> 17627 Population, age 18, male
#> 17628 Population, age 18, total
#> 17629 Age population, age 19, female, interpolated
#> 17630 Population, age 19, female
#> 17631 Age population, age 19, male, interpolated
#> 17632 Population, age 19, male
#> 17633 Population, age 19, total
#> 17634 Age population, age 20, female, interpolated
#> 17635 Population, age 20, female
#> 17636 Age population, age 20, male, interpolated
#> 17637 Population, age 20, male
#> 17638 Population, age 20, total
#> 17639 Age population, age 21, female, interpolated
#> 17640 Population, age 21, female
#> 17641 Age population, age 21, male, interpolated
#> 17642 Population, age 21, male
#> 17643 Population, age 21, total
#> 17644 Age population, age 22, female, interpolated
#> 17645 Population, age 22, female
#> 17646 Age population, age 22, male, interpolated
#> 17647 Population, age 22, male
#> 17648 Population, age 22, total
#> 17649 Age population, age 23, female, interpolated
#> 17650 Population, age 23, female
#> 17651 Age population, age 23, male, interpolated
#> 17652 Population, age 23, male
#> 17653 Population, age 23, total
#> 17654 Age population, age 24, female, interpolated
#> 17655 Population, age 24, female
#> 17656 Age population, age 24, male, interpolated
#> 17657 Population, age 24, male
#> 17658 Population, age 24, total
#> 17659 Age population, age 25, female, interpolated
#> 17660 Population, age 25, female
#> 17661 Age population, age 25, male, interpolated
#> 17662 Population, age 25, male
#> 17663 Population, age 25, total
#> 17665 Age dependency ratio (% of working-age population)
#> 17666 Age dependency ratio, old (% of working-age population)
#> 17667 Age dependency ratio, young (% of working-age population)
#> 17668 Population growth (annual %)
#> 17669 Population density (people per sq km)
#> 17674 Population, total
#> 17675 Population, female
#> 17676 Population, female (% of total population)
#> 17677 SP.POP.TOTL.ICP:Population
#> 17678 SP.POP.TOTL.ICP.ZS:Population shares (World=100)
#> 17679 Population, male
#> 17680 Population, male (% of total population)
#> 17681 Population (% of total)
#> 17682 School age population, pre-primary education, female (number)
#> 17683 School age population, pre-primary education, both sexes (number)
#> 17684 School age population, pre-primary education, male (number)
#> 17685 School age population, last grade of primary education, female (number)
#> 17686 School age population, last grade of primary education, male (number)
#> 17687 School age population, last grade of primary education, both sexes (number)
#> 17688 School age population, primary education, female (number)
#> 17689 School age population, primary education, both sexes (number)
#> 17690 School age population, primary education, male (number)
#> 17702 Rural population
#> 17703 Rural population, female (% of total)
#> 17704 Rural population, male (% of total)
#> 17705 Rural population growth (annual %)
#> 17706 Rural population (% of total population)
#> 17707 School age population, lower secondary education, female (number)
#> 17708 School age population, lower secondary education, both sexes (number)
#> 17709 School age population, lower secondary education, male (number)
#> 17710 School age population, secondary education, female (number)
#> 17711 School age population, secondary education, both sexes (number)
#> 17712 School age population, secondary education, male (number)
#> 17713 School age population, upper secondary education, female (number)
#> 17714 School age population, upper secondary education, both sexes (number)
#> 17715 School age population, upper secondary education, male (number)
#> 17716 School age population, tertiary education, female (number)
#> 17717 School age population, tertiary education, both sexes (number)
#> 17718 School age population, tertiary education, male (number)
#> 17719 Urban population growth (annual %)
#> 17720 Population in largest city
#> 17721 Population in the largest city (% of urban population)
#> 17722 Population in urban agglomerations > 1 million
#> 17723 Population in urban agglomerations > 1 million (% of total pop)
#> 17724 Urban population
#> 17725 Urban population, female (% of total)
#> 17726 Urban population (% of total population)
#> 17727 Urban population, male (% of total)
#> 17728 Percentage of Population in Urban Areas (in % of Total Population)
#> 17771 Population & Housing census (Availability score over 20 years)
#> 18453 UIS: Percentage of population age 25+ whose highest level of education is primary, both sexes
#> 18454 UIS: Percentage of population age 25+ whose highest level of education is primary, female
#> 18455 UIS: Percentage of population age 25+ whose highest level of education is primary, male
#> 18456 UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Total
#> 18457 UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Female
#> 18458 UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Male
#> 18459 UIS: Percentage of population age 25+ with at least completed primary education (ISCED 1 or higher). Adjusted Gender Parity Index (GPIA)
#> 18460 UIS: Percentage of population age 25+ whose highest level of education is lower secondary, both sexes
#> 18461 UIS: Percentage of population age 25+ whose highest level of education is lower secondary, female
#> 18462 UIS: Percentage of population age 25+ whose highest level of education is lower secondary, male
#> 18463 UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Total
#> 18464 UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Female
#> 18465 UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Male
#> 18466 UIS: Percentage of population age 25+ with at least completed lower secondary education (ISCED 2 or higher). Adjusted Gender Parity Index (GPIA)
#> 18467 UIS: Percentage of population age 25+ whose highest level of education is upper secondary, both sexes
#> 18468 UIS: Percentage of population age 25+ whose highest level of education is upper secondary, female
#> 18469 UIS: Percentage of population age 25+ whose highest level of education is upper secondary, male
#> 18470 UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Total
#> 18471 UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Female
#> 18472 UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Male
#> 18473 UIS: Percentage of population age 25+ with at least completed upper secondary education (ISCED 3 or higher). Adjusted Gender Parity Index (GPIA)
#> 18474 UIS: Percentage of population age 25+ whose highest level of education is post-secondary non-tertiary, both sexes
#> 18475 UIS: Percentage of population age 25+ whose highest level of education is post-secondary non-tertiary, female
#> 18476 UIS: Percentage of population age 25+ whose highest level of education is post-secondary non-tertiary, male
#> 18477 UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Total
#> 18478 UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Female
#> 18479 UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Male
#> 18480 UIS: Percentage of population age 25+ with at least completed post-secondary education (ISCED 4 or higher). Adjusted Gender Parity Index (GPIA)
#> 18481 UIS: Percentage of population age 25+ whose highest level of education is short cycle tertiary, both sexes
#> 18482 UIS: Percentage of population age 25+ whose highest level of education is short cycle tertiary, female
#> 18483 UIS: Percentage of population age 25+ whose highest level of education is short cycle tertiary, male
#> 18484 UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Total
#> 18485 UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Female
#> 18486 UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Adjusted Gender Parity Index (GPIA)
#> 18487 UIS: Percentage of population age 25+ with at least a completed short-cycle tertiary degree (ISCED 5 or higher). Male
#> 18488 UIS: Percentage of population age 25+ whose highest level of education is Bachelor's or equivalent (ISCED 6), both sexes
#> 18489 UIS: Percentage of population age 25+ whose highest level of education is Bachelor's or equivalent (ISCED 6), female
#> 18490 UIS: Percentage of population age 25+ whose highest level of education is Bachelor's or equivalent (ISCED 6), male
#> 18491 UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Total
#> 18492 UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Female
#> 18493 UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Adjusted Gender Parity Index (GPIA)
#> 18494 UIS: Percentage of population age 25+ with at least a completed bachelor's or equivalent degree (ISCED 6 or higher). Male
#> 18495 UIS: Percentage of population age 25+ whose highest level of education is Master's or equivalent (ISCED 7), both sexes
#> 18496 UIS: Percentage of population age 25+ whose highest level of education is Master's or equivalent (ISCED 7), female
#> 18497 UIS: Percentage of population age 25+ whose highest level of education is Master's or equivalent (ISCED 7), male
#> 18498 UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Total
#> 18499 UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Female
#> 18500 UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Adjusted Gender Parity Index (GPIA)
#> 18501 UIS: Percentage of population age 25+ with at least a completed master's degree or equivalent (ISCED 7 or higher). Male
#> 18502 UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Total
#> 18503 UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Female
#> 18504 UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Adjusted Gender Parity Index (GPIA)
#> 18505 UIS: Percentage of population age 25+ with a doctoral degree or equivalent (ISCED 8). Male
#> 18506 UIS: Mean years of schooling (ISCED 1 or higher), population 25+ years, both sexes
#> 18507 UIS: Mean years of schooling (ISCED 1 or higher), population 25+ years, female
#> 18508 UIS: Mean years of schooling (ISCED 1 or higher), population 25+ years, male
#> 18509 UIS: Percentage of population age 25+ with no schooling, both sexes
#> 18510 UIS: Percentage of population age 25+ with no schooling, female
#> 18511 UIS: Percentage of population age 25+ with no schooling, male
#> 18512 UIS: Percentage of population age 25+ whose highest level of education is incomplete primary, both sexes
#> 18513 UIS: Percentage of population age 25+ whose highest level of education is incomplete primary, female
#> 18514 UIS: Percentage of population age 25+ whose highest level of education is incomplete primary, male
#> 18515 UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Total
#> 18516 UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Female
#> 18517 UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Adjusted Gender Parity Index (GPIA)
#> 18518 UIS: Percentage of population age 25+ with at least some primary (ISCED 1). Male
#> 18519 UIS: Percentage of population age 25+ with unknown educational attainment. Total
#> 18520 UIS: Percentage of population age 25+ with unknown educational attainment. Female
#> 18521 UIS: Percentage of population age 25+ with unknown educational attainment. Male
#> 18776 Illiterate population, 25-64 years, both sexes (number)
#> 18777 Illiterate population, 25-64 years, female (number)
#> 18778 Illiterate population, 25-64 years, male (number)
#> 18779 Illiterate population, 25-64 years, % female
#> 18780 Youth illiterate population, 15-24 years, both sexes (number)
#> 18781 Youth illiterate population, 15-24 years, female (number)
#> 18782 Youth illiterate population, 15-24 years, male (number)
#> 18783 Adult illiterate population, 15+ years, both sexes (number)
#> 18784 Adult illiterate population, 15+ years, female (number)
#> 18785 Adult illiterate population, 15+ years, male (number)
#> 18786 Elderly illiterate population, 65+ years, both sexes (number)
#> 18787 Elderly illiterate population, 65+ years, female (number)
#> 18788 Elderly illiterate population, 65+ years, male (number)
#> 18789 Youth illiterate population, 15-24 years, % female
#> 18790 Adult illiterate population, 15+ years, % female
#> 18791 Elderly illiterate population, 65+ years, % female
#> 18792 Youth literacy rate, population 15-24 years, female, adjusted location parity index (LPIA)
#> 18793 Youth literacy rate, population 15-24 years, adjusted gender parity index (GPIA)
#> 18794 Youth literacy rate, population 15-24 years, adjusted location parity index (LPIA)
#> 18795 Youth literacy rate, population 15-24 years, male, adjusted location parity index (LPIA)
#> 18796 Youth literacy rate, population 15-24 years, rural, both sexes (%)
#> 18797 Youth literacy rate, population 15-24 years, rural, female (%)
#> 18798 Youth literacy rate, population 15-24 years, rural, adjusted gender parity index (GPIA)
#> 18799 Youth literacy rate, population 15-24 years, rural, male (%)
#> 18800 Youth literacy rate, population 15-24 years, urban, both sexes (%)
#> 18801 Youth literacy rate, population 15-24 years, urban, female (%)
#> 18802 Youth literacy rate, population 15-24 years, urban, adjusted gender parity index (GPIA)
#> 18803 Youth literacy rate, population 15-24 years, urban, male (%)
#> 18804 Adult literacy rate, population 15+ years, female, adjusted location parity index (LPIA)
#> 18805 Adult literacy rate, population 15+ years, adjusted gender parity index (GPIA)
#> 18806 Adult literacy rate, population 15+ years, adjusted location parity index (LPIA)
#> 18807 Adult literacy rate, population 15+ years, male, adjusted location parity index (LPIA)
#> 18808 Adult literacy rate, population 15+ years, rural, both sexes (%)
#> 18809 Adult literacy rate, population 15+ years, rural, female (%)
#> 18810 Adult literacy rate, population 15+ years, rural, adjusted gender parity index (GPIA)
#> 18811 Adult literacy rate, population 15+ years, rural, male (%)
#> 18812 Adult literacy rate, population 15+ years, urban, both sexes (%)
#> 18813 Adult literacy rate, population 15+ years, urban, female (%)
#> 18814 Adult literacy rate, population 15+ years, urban, adjusted gender parity index (GPIA)
#> 18815 Adult literacy rate, population 15+ years, urban, male (%)
#> 18816 Literacy rate, population 25-64 years, both sexes (%)
#> 18817 Literacy rate, population 25-64 years, female (%)
#> 18818 Literacy rate, population 25-64 years, female, adjusted location parity index (LPIA)
#> 18819 Literacy rate, population 25-64 years, adjusted gender parity index (GPIA)
#> 18820 Literacy rate, population 25-64 years, adjusted location parity index (LPIA)
#> 18821 Literacy rate, population 25-64 years, male (%)
#> 18822 Literacy rate, population 25-64 years, male, adjusted location parity index (LPIA)
#> 18823 Literacy rate, population 25-64 years, rural, both sexes (%)
#> 18824 Literacy rate, population 25-64 years, rural, female (%)
#> 18825 Literacy rate, population 25-64 years, rural, adjusted gender parity index (GPIA)
#> 18826 Literacy rate, population 25-64 years, rural, male (%)
#> 18827 Literacy rate, population 25-64 years, urban, both sexes (%)
#> 18828 Literacy rate, population 25-64 years, urban, female (%)
#> 18829 Literacy rate, population 25-64 years, urban, adjusted gender parity index (GPIA)
#> 18830 Literacy rate, population 25-64 years, urban, male (%)
#> 18831 Elderly literacy rate, population 65+ years, both sexes (%)
#> 18832 Elderly literacy rate, population 65+ years, female (%)
#> 18833 Elderly literacy rate, population 65+ years, male (%)
#> 18834 Elderly literacy rate, population 65+ years, female, adjusted location parity index (LPIA)
#> 18835 Elderly literacy rate, population 65+ years, adjusted gender parity index (GPIA)
#> 18836 Elderly literacy rate, population 65+ years, adjusted location parity index (LPIA)
#> 18837 Elderly literacy rate, population 65+ years, male, adjusted location parity index (LPIA)
#> 18838 Elderly literacy rate, population 65+ years, rural, both sexes (%)
#> 18839 Elderly literacy rate, population 65+ years, rural, female (%)
#> 18840 Elderly literacy rate, population 65+ years, rural, adjusted gender parity index (GPIA)
#> 18841 Elderly literacy rate, population 65+ years, rural, male (%)
#> 18842 Elderly literacy rate, population 65+ years, urban, both sexes (%)
#> 18843 Elderly literacy rate, population 65+ years, urban, female (%)
#> 18844 Elderly literacy rate, population 65+ years, urban, adjusted gender parity index (GPIA)
#> 18845 Elderly literacy rate, population 65+ years, urban, male (%)
#> 19358 Participants in literacy programmes as a % of the illiterate population, both sexes
#> 19359 Participants in literacy programmes as a % of the illiterate population, female
#> 19360 Participants in literacy programmes as a % of the illiterate population, male
#> 19876 School age population, early childhood education, both sexes (number)
#> 19877 School age population, early childhood education, female (number)
#> 19878 School age population, early childhood education, male (number)
#> 19879 School age population, early childhood educational development programmes, both sexes (number)
#> 19880 School age population, early childhood educational development programmes, female (number)
#> 19881 School age population, early childhood educational development programmes, male (number)
#> 19882 School age population, one year before than official primary entry age, both sexes (number)
#> 19883 School age population, one year before than official primary entry age, female (number)
#> 19884 School age population, one year before than official primary entry age, male (number)
#> 19885 Population of the official entrance age to primary education, both sexes (number)
#> 19886 Population of the official entrance age to primary education, female (number)
#> 19887 Population of the official entrance age to primary education, male (number)
#> 19888 Population of the official entrance age to secondary general education, both sexes (number)
#> 19889 Population of the official entrance age to secondary general education, female (number)
#> 19890 Population of the official entrance age to secondary general education, male (number)
#> 19891 School age population, post-secondary non-tertiary education, both sexes (number)
#> 19892 School age population, post-secondary non-tertiary education, female (number)
#> 19893 School age population, post-secondary non-tertiary education, male (number)
#> 19894 Population of compulsory school age, both sexes (number)
#> 19895 Population of compulsory school age, female (number)
#> 19896 Population of compulsory school age, male (number)
#> 20141 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, both sexes (%)
#> 20142 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, female (%)
#> 20143 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, adjusted gender parity index (GPIA)
#> 20144 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, high socio-economic status (%)
#> 20145 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, low socio-economic status (%)
#> 20146 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, male (%)
#> 20147 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, non-immigrant background (%)
#> 20148 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, immigrant background (%)
#> 20149 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, adjusted native parity index (NPIA)
#> 20150 Proportion of population achieving at least a fixed level of proficiency in functional literacy skills, adjusted wealth parity index (WPIA)
#> 20151 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, both sexes (%)
#> 20152 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, female (%)
#> 20153 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, adjusted gender parity index (GPIA)
#> 20154 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, high socio-economic status (%)
#> 20155 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, low socio-economic status (%)
#> 20156 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, male (%)
#> 20157 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, non-immigrant background (%)
#> 20158 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, immigrant background (%)
#> 20159 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, adjusted native parity index (NPIA)
#> 20160 Proportion of population achieving at least a fixed level of proficiency in functional numeracy skills, adjusted wealth parity index (WPIA)
#> description
#> 25 Access to electricity is the percentage of population with access to electricity.
#> 40 Access to electricity is the percentage of rural population with access to electricity.
#> 41 Access to electricity is the percentage of total population with access to electricity.
#> 165
#> 199 Population censuses collect data on the size, distribution and composition of population and information on a broad range of social and economic characteristics of the population. It also provides sampling frames for household and other surveys. It is recommended that population censuses be conducted at least every 10 years.
#> 1173
#> 1176
#> 1179
#> 1197 Percentage of female population age 15-19 with no education
#> 1198 Percentage of population age 15-19 with no education
#> 1199 Percentage of female population age 15+ with no education
#> 1200 Percentage of population age 15+ with no education
#> 1201 Percentage of female population age 20-24 with no education
#> 1202 Percentage of population age 20-24 with no education
#> 1203 Percentage of female population age 25-29 with no education
#> 1204 Percentage of population age 25-29 with no education
#> 1205 Percentage of female population age 25+ with no education
#> 1206 Percentage of population age 25+ with no education
#> 1207 Percentage of female population age 30-34 with no education
#> 1208 Percentage of population age 30-34 with no education
#> 1209 Percentage of female population age 35-39 with no education
#> 1210 Percentage of population age 35-39 with no education
#> 1211 Percentage of female population age 40-44 with no education
#> 1212 Percentage of population age 40-44 with no education
#> 1213 Percentage of female population age 45-49 with no education
#> 1214 Percentage of population age 45-49 with no education
#> 1215 Percentage of female population age 50-54 with no education
#> 1216 Percentage of population age 50-54 with no education
#> 1217 Percentage of female population age 55-59 with no education
#> 1218 Percentage of population age 55-59 with no education
#> 1219 Percentage of female population age 60-64 with no education
#> 1220 Percentage of population age 60-64 with no education
#> 1221 Percentage of female population age 65-69 with no education
#> 1222 Percentage of population age 65-69 with no education
#> 1223 Percentage of female population age 70-74 with no education
#> 1224 Percentage of population age 70-74 with no education
#> 1225 Percentage of female population age 75+ with no education
#> 1226 Percentage of population age 75+ with no education
#> 1227 Population in thousands, age 15-19, total is the total population of 15-19 year olds in thousands estimated by Barro-Lee.
#> 1228 Population in thousands, age 15-19, female is the female population of 15-19 year olds in thousands estimated by Barro-Lee.
#> 1229 Population in thousands, age 15+, total is the total population over age 15 in thousands estimated by Barro-Lee.
#> 1230 Population in thousands, age 15+, female is the female population over age 15 in thousands estimated by Barro-Lee.
#> 1231 Population in thousands, age 20-24, total is the total population of 20-24 year olds in thousands estimated by Barro-Lee.
#> 1232 Population in thousands, age 20-24, female is the female population of 20-24 year olds in thousands estimated by Barro-Lee.
#> 1233 Population in thousands, age 25-29, total is the total population of 25-29 year olds in thousands estimated by Barro-Lee.
#> 1234 Population in thousands, age 25-29, female is the female population of 25-29 year olds in thousands estimated by Barro-Lee.
#> 1235 Population in thousands, age 25+, total is the total population over age 25 in thousands estimated by Barro-Lee.
#> 1236 Population in thousands, age 25+, female is the female population over age 25 in thousands estimated by Barro-Lee.
#> 1237 Population in thousands, age 30-34, total is the total population of 30-34 year olds in thousands estimated by Barro-Lee.
#> 1238 Population in thousands, age 30-34, female is the female population of 30-34 year olds in thousands estimated by Barro-Lee.
#> 1239 Population in thousands, age 35-39, total is the total population of 35-39 year olds in thousands estimated by Barro-Lee.
#> 1240 Population in thousands, age 35-39, female is the female population of 35-39 year olds in thousands estimated by Barro-Lee.
#> 1241 Population in thousands, age 40-44, total is the total population of 40-44 year olds in thousands estimated by Barro-Lee.
#> 1242 Population in thousands, age 40-44, female is the female population of 40-44 year olds in thousands estimated by Barro-Lee.
#> 1243 Population in thousands, age 45-49, total is the total population of 45-49 year olds in thousands estimated by Barro-Lee.
#> 1244 Population in thousands, age 45-49, female is the female population of 45-49 year olds in thousands estimated by Barro-Lee.
#> 1245 Population in thousands, age 50-54, total is the total population of 50-54 year olds in thousands estimated by Barro-Lee.
#> 1246 Population in thousands, age 50-54, female is the female population of 50-54 year olds in thousands estimated by Barro-Lee.
#> 1247 Population in thousands, age 55-59, total is the total population of 55-59 year olds in thousands estimated by Barro-Lee.
#> 1248 Population in thousands, age 55-59, female is the female population of 55-59 year olds in thousands estimated by Barro-Lee.
#> 1249 Population in thousands, age 60-64, total is the total population of 60-64 year olds in thousands estimated by Barro-Lee.
#> 1250 Population in thousands, age 60-64, female is the female population of 60-64 year olds in thousands estimated by Barro-Lee.
#> 1251 Population in thousands, age 65-69, total is the total population of 65-69 year olds in thousands estimated by Barro-Lee.
#> 1252 Population in thousands, age 65-69, female is the female population of 65-69 year olds in thousands estimated by Barro-Lee.
#> 1253 Population in thousands, age 70-74, total is the total population of 70-74 year olds in thousands estimated by Barro-Lee.
#> 1254 Population in thousands, age 70-74, female is the female population of 70-74 year olds in thousands estimated by Barro-Lee.
#> 1255 Population in thousands, age 75+, total is the total population over age 75 in thousands estimated by Barro-Lee.
#> 1256 Population in thousands, age 75+, female is the female population over age 75 in thousands estimated by Barro-Lee.
#> 1257 Percentage of female population age 15-19 with primary schooling. Completed Primary
#> 1258 Percentage of population age 15-19 with primary schooling. Completed Primary
#> 1259 Percentage of female population age 15+ with primary schooling. Completed Primary
#> 1260 Percentage of population age 15+ with primary schooling. Completed Primary
#> 1261 Percentage of female population age 20-24 with primary schooling. Completed Primary
#> 1262 Percentage of population age 20-24 with primary schooling. Completed Primary
#> 1263 Percentage of female population age 25-29 with primary schooling. Completed Primary
#> 1264 Percentage of population age 25-29 with primary schooling. Completed Primary
#> 1265 Percentage of female population age 25+ with primary schooling. Completed Primary
#> 1266 Percentage of population age 25+ with primary schooling. Completed Primary
#> 1267 Percentage of female population age 30-34 with primary schooling. Completed Primary
#> 1268 Percentage of population age 30-34 with primary schooling. Completed Primary
#> 1269 Percentage of female population age 35-39 with primary schooling. Completed Primary
#> 1270 Percentage of population age 35-39 with primary schooling. Completed Primary
#> 1271 Percentage of female population age 40-44 with primary schooling. Completed Primary
#> 1272 Percentage of population age 40-44 with primary schooling. Completed Primary
#> 1273 Percentage of female population age 45-49 with primary schooling. Completed Primary
#> 1274 Percentage of population age 45-49 with primary schooling. Completed Primary
#> 1275 Percentage of female population age 50-54 with primary schooling. Completed Primary
#> 1276 Percentage of population age 50-54 with primary schooling. Completed Primary
#> 1277 Percentage of female population age 55-59 with primary schooling. Completed Primary
#> 1278 Percentage of population age 55-59 with primary schooling. Completed Primary
#> 1279 Percentage of female population age 60-64 with primary schooling. Completed Primary
#> 1280 Percentage of population age 60-64 with primary schooling. Completed Primary
#> 1281 Percentage of female population age 65-69 with primary schooling. Completed Primary
#> 1282 Percentage of population age 65-69 with primary schooling. Completed Primary
#> 1283 Percentage of female population age 70-74 with primary schooling. Completed Primary
#> 1284 Percentage of population age 70-74 with primary schooling. Completed Primary
#> 1285 Percentage of female population age 75+ with primary schooling. Completed Primary
#> 1286 Percentage of population age 75+ with primary schooling. Completed Primary
#> 1287 Percentage of female population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)
#> 1288 Percentage of population age 15-19 with primary schooling. Total (Incomplete and Completed Primary)
#> 1289 Percentage of female population age 15+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1290 Percentage of population age 15+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1291 Percentage of female population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)
#> 1292 Percentage of population age 20-24 with primary schooling. Total (Incomplete and Completed Primary)
#> 1293 Percentage of female population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)
#> 1294 Percentage of population age 25-29 with primary schooling. Total (Incomplete and Completed Primary)
#> 1295 Percentage of female population age 25+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1296 Percentage of population age 25+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1297 Percentage of female population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)
#> 1298 Percentage of population age 30-34 with primary schooling. Total (Incomplete and Completed Primary)
#> 1299 Percentage of female population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)
#> 1300 Percentage of population age 35-39 with primary schooling. Total (Incomplete and Completed Primary)
#> 1301 Percentage of female population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)
#> 1302 Percentage of population age 40-44 with primary schooling. Total (Incomplete and Completed Primary)
#> 1303 Percentage of female population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)
#> 1304 Percentage of population age 45-49 with primary schooling. Total (Incomplete and Completed Primary)
#> 1305 Percentage of female population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)
#> 1306 Percentage of population age 50-54 with primary schooling. Total (Incomplete and Completed Primary)
#> 1307 Percentage of female population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)
#> 1308 Percentage of population age 55-59 with primary schooling. Total (Incomplete and Completed Primary)
#> 1309 Percentage of female population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)
#> 1310 Percentage of population age 60-64 with primary schooling. Total (Incomplete and Completed Primary)
#> 1311 Percentage of female population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)
#> 1312 Percentage of population age 65-69 with primary schooling. Total (Incomplete and Completed Primary)
#> 1313 Percentage of female population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)
#> 1314 Percentage of population age 70-74 with primary schooling. Total (Incomplete and Completed Primary)
#> 1315 Percentage of female population age 75+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1316 Percentage of population age 75+ with primary schooling. Total (Incomplete and Completed Primary)
#> 1377 Percentage of female population age 15-19 with secondary schooling. Completed Secondary
#> 1378 Percentage of population age 15-19 with secondary schooling. Completed Secondary
#> 1379 Percentage of female population age 15+ with secondary schooling. Completed Secondary
#> 1380 Percentage of population age 15+ with secondary schooling. Completed Secondary
#> 1381 Percentage of female population age 20-24 with secondary schooling. Completed Secondary
#> 1382 Percentage of population age 20-24 with secondary schooling. Completed Secondary
#> 1383 Percentage of female population age 25-29 with secondary schooling. Completed Secondary
#> 1384 Percentage of population age 25-29 with secondary schooling. Completed Secondary
#> 1385 Percentage of female population age 25+ with secondary schooling. Completed Secondary
#> 1386 Percentage of population age 25+ with secondary schooling. Completed Secondary
#> 1387 Percentage of female population age 30-34 with secondary schooling. Completed Secondary
#> 1388 Percentage of population age 30-34 with secondary schooling. Completed Secondary
#> 1389 Percentage of female population age 35-39 with secondary schooling. Completed Secondary
#> 1390 Percentage of population age 35-39 with secondary schooling. Completed Secondary
#> 1391 Percentage of female population age 40-44 with secondary schooling. Completed Secondary
#> 1392 Percentage of population age 40-44 with secondary schooling. Completed Secondary
#> 1393 Percentage of female population age 45-49 with secondary schooling. Completed Secondary
#> 1394 Percentage of population age 45-49 with secondary schooling. Completed Secondary
#> 1395 Percentage of female population age 50-54 with secondary schooling. Completed Secondary
#> 1396 Percentage of population age 50-54 with secondary schooling. Completed Secondary
#> 1397 Percentage of female population age 55-59 with secondary schooling. Completed Secondary
#> 1398 Percentage of population age 55-59 with secondary schooling. Completed Secondary
#> 1399 Percentage of female population age 60-64 with secondary schooling. Completed Secondary
#> 1400 Percentage of population age 60-64 with secondary schooling. Completed Secondary
#> 1401 Percentage of female population age 65-69 with secondary schooling. Completed Secondary
#> 1402 Percentage of population age 65-69 with secondary schooling. Completed Secondary
#> 1403 Percentage of female population age 70-74 with secondary schooling. Completed Secondary
#> 1404 Percentage of population age 70-74 with secondary schooling. Completed Secondary
#> 1405 Percentage of female population age 75+ with secondary schooling. Completed Secondary
#> 1406 Percentage of population age 75+ with secondary schooling. Completed Secondary
#> 1407 Percentage of female population age 15-19 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1408 Percentage of population age 15-19 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1409 Percentage of female population age 15+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1410 Percentage of population age 15+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1411 Percentage of female population age 20-24 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1412 Percentage of population age 20-24 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1413 Percentage of female population age 25-29 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1414 Percentage of population age 25-29 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1415 Percentage of female population age 25+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1416 Percentage of population age 25+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1417 Percentage of female population age 30-34 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1418 Percentage of population age 30-34 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1419 Percentage of female population age 35-39 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1420 Percentage of population age 35-39 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1421 Percentage of female population age 40-44 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1422 Percentage of population age 40-44 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1423 Percentage of female population age 45-49 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1424 Percentage of population age 45-49 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1425 Percentage of female population age 50-54 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1426 Percentage of population age 50-54 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1427 Percentage of female population age 55-59 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1428 Percentage of population age 55-59 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1429 Percentage of female population age 60-64 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1430 Percentage of population age 60-64 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1431 Percentage of female population age 65-69 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1432 Percentage of population age 65-69 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1433 Percentage of female population age 70-74 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1434 Percentage of population age 70-74 with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1435 Percentage of female population age 75+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1436 Percentage of population age 75+ with secondary schooling. Total (Incomplete and Completed Secondary)
#> 1467 Percentage of female population age 15-19 with tertiary schooling. Completed Tertiary
#> 1468 Percentage of population age 15-19 with tertiary schooling. Completed Tertiary
#> 1469 Percentage of female population age 15+ with tertiary schooling. Completed Tertiary
#> 1470 Percentage of population age 15+ with tertiary schooling. Completed Tertiary
#> 1471 Percentage of female population age 20-24 with tertiary schooling. Completed Tertiary
#> 1472 Percentage of population age 20-24 with tertiary schooling. Completed Tertiary
#> 1473 Percentage of female population age 25-29 with tertiary schooling. Completed Tertiary
#> 1474 Percentage of population age 25-29 with tertiary schooling. Completed Tertiary
#> 1475 Percentage of female population age 25+ with tertiary schooling. Completed Tertiary
#> 1476 Percentage of population age 25+ with tertiary schooling. Completed Tertiary
#> 1477 Percentage of female population age 30-34 with tertiary schooling. Completed Tertiary
#> 1478 Percentage of population age 30-34 with tertiary schooling. Completed Tertiary
#> 1479 Percentage of female population age 35-39 with tertiary schooling. Completed Tertiary
#> 1480 Percentage of population age 35-39 with tertiary schooling. Completed Tertiary
#> 1481 Percentage of female population age 40-44 with tertiary schooling. Completed Tertiary
#> 1482 Percentage of population age 40-44 with tertiary schooling. Completed Tertiary
#> 1483 Percentage of female population age 45-49 with tertiary schooling. Completed Tertiary
#> 1484 Percentage of population age 45-49 with tertiary schooling. Completed Tertiary
#> 1485 Percentage of female population age 50-54 with tertiary schooling. Completed Tertiary
#> 1486 Percentage of population age 50-54 with tertiary schooling. Completed Tertiary
#> 1487 Percentage of female population age 55-59 with tertiary schooling. Completed Tertiary
#> 1488 Percentage of population age 55-59 with tertiary schooling. Completed Tertiary
#> 1489 Percentage of female population age 60-64 with tertiary schooling. Completed Tertiary
#> 1490 Percentage of population age 60-64 with tertiary schooling. Completed Tertiary
#> 1491 Percentage of female population age 65-69 with tertiary schooling. Completed Tertiary
#> 1492 Percentage of population age 65-69 with tertiary schooling. Completed Tertiary
#> 1493 Percentage of female population age 70-74 with tertiary schooling. Completed Tertiary
#> 1494 Percentage of population age 70-74 with tertiary schooling. Completed Tertiary
#> 1495 Percentage of female population age 75+ with tertiary schooling. Completed Tertiary
#> 1496 Percentage of population age 75+ with tertiary schooling. Completed Tertiary
#> 1497 Percentage of female population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1498 Percentage of population age 15-19 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1499 Percentage of female population age 15+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1500 Percentage of population age 15+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1501 Percentage of female population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1502 Percentage of population age 20-24 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1503 Percentage of female population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1504 Percentage of population age 25-29 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1505 Percentage of female population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1506 Percentage of population age 25+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1507 Percentage of female population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1508 Percentage of population age 30-34 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1509 Percentage of female population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1510 Percentage of population age 35-39 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1511 Percentage of female population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1512 Percentage of population age 40-44 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1513 Percentage of female population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1514 Percentage of population age 45-49 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1515 Percentage of female population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1516 Percentage of population age 50-54 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1517 Percentage of female population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1518 Percentage of population age 55-59 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1519 Percentage of female population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1520 Percentage of population age 60-64 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1521 Percentage of female population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1522 Percentage of population age 65-69 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1523 Percentage of female population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1524 Percentage of population age 70-74 with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1525 Percentage of female population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1526 Percentage of population age 75+ with tertiary schooling. Total (Incomplete and Completed Tertiary)
#> 1916
#> 2024 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2100, determined by the minimum exposure scenario (RCP26)
#> 2025 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2100, determined by the maximum exposure scenario (RCP85)
#> 2026 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2050, determined by the minimum exposure scenario (RCP26)
#> 2027 Additional population exposed to annual coastal floods due to sea level rise, as a share of actual population, is reported as a percentage and is reflective of the population in the year 2050, determined by the maximum exposure scenario (RCP85)
#> 2029 Additional people below $1.90 as % of total population for all impacts, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2030 Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Agriculture Revenues, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2031 Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Disasters, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2032 Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Food Prices, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2033 Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Health, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2034 Additional people below $1.90 as % of total population by impact, from climate change impacts relating to Labor Productivity, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2035 Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Agriculture, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2036 Additional people below $4 as % of total population by impact, by 2030, from all impacts, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2037 Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Disasters, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2038 Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Health, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2039 Additional people below $4 as % of total population by impact, by 2030, from climate change impacts relating to Temperature, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2040 Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Agriculture, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2041 Change in income (%) for bottom 40% of the population for all impacts impact, by 2030, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2042 Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Disasters, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2043 Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Health, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2044 Change in income (%) for bottom 40% of the population by impact, by 2030, from climate change impacts relating to Temperature, is generated from data within Shock Waves : Managing the Impacts of Climate Change on Poverty. This report examines the potential impact of climate change and climate policies on poverty reduction. It also provides guidance on how to create a “win-win” situation so that climate change policies contribute to poverty reduction and poverty-reduction policies contribute to climate change mitigation and resilience building. The key finding of the report is that climate change represents a significant obstacle to the sustained eradication of poverty, but future impacts on poverty are determined by policy choices: rapid, inclusive, and climate-informed development can prevent most short-term impacts whereas immediate pro-poor, emissions-reduction policies can drastically limit long-term ones.
#> 2045
#> 2046
#> 2047
#> 2048
#> 2049
#> 2050
#> 2236 Share of population that falls into the defined poverty threshold of $5.50 (consumption per day) that is exposed to floods within a country
#> 2237 Share of total population that is exposed to floods within a country
#> 2272 Data reflects the impact of population (as a macro-driver) to total emission growth (excluding LUCF) across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020.
#> 2358 Education status as a percentage of the total population is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group.
#> 2359 Education status as a percentage of the total population is disaggregated from the Global Jobs Indicatos Database. The Database covers socio-demographics, labor force status and employment type, employment composition by sector and occupation, education level completed, hours worked, and earnings. The database was compiled from national surveys and subnational microdata which was first harmonized for the Bank-wide I2D2 database, then quality checked by the Jobs Group.
#> 2361 This indicator presents the mortality rate attributable to household air pollution as a factor of deaths per 100 000 population. Data is taken from the United Nations Sustainable Goals, representing Indicator 3.9.1: Crude death rate attributed to ambient air pollution (deaths per 100 000 population).
#> 2362 This indicator presents the mortality rate attributable to ambient air pollution as a factor of deaths per 100 000 population. Data is taken from the United Nations Sustainable Goals, representing Indicator 3.9.1: Crude death rate attributed to household air pollution (deaths per 100 000 population).
#> 2363 This indicator conveys the share of the population effectively covered by a social protection system, including social protection floors. It also provides the coverage rates of the main components of social protection: child and maternity benefits, support for persons without a job, persons with disabilities, victims of work injuries and older persons. For more information, refer to the concepts and definitions page. Alternate source for this information is UN open data hub, SDG Indicator 1.3.1: Proportion of population covered by social protection floors/systems (%) | Annual
#> 2411
#> 2423
#> 2439
#> 5429 This is described as population policy and administrative management; reproductive health care; family planning; STD control including HIV/AIDS and personnel development for population and reproductive health. Data are in current U.S. dollars.
#> 5966 Access to clean fuels and technologies for cooking, rural is the proportion of rural population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.
#> 5967 Access to clean fuels and technologies for cooking, urban is the proportion of urban population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.
#> 5968 Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.
#> 5971 Access to electricity, rural is the percentage of rural population with access to electricity.
#> 5972 Access to electricity, urban is the percentage of urban population with access to electricity.
#> 5973 Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources.
#> 5999 Access to non-solid fuel, rural is the percentage of rural population with access to non-solid fuel.
#> 6000 Access to non-solid fuel, urban is the percentage of urban population with access to non-solid fuel.
#> 6001 Access to non-solid fuel is the percentage of population with access to non-solid fuel.
#> 6013 Agricultural employment shows the number of workers in the agricultural sector.
#> 6014 Economically active female population in agriculture is that part of the economically active female population engaged in or seeking work in agriculture, hunting, fishing or forestry.
#> 6015 The Agricultural Population is defined as all persons depending for their livelihood on agriculture, hunting, fishing or forestry. This estimate comprises all persons actively engaged in agriculture and their non-working dependants. The Agricultural Population series are estimated by FAO based on the total population series obtained from UN Population Division ("World population prospects: The 2008 Revision") and the ratios of labour force in total population and agricultural labour force in total labour force from ILO: ("Economically active population, 1950-2010: The 4th Revision", ILO, Geneva, 1996). Direct information on agricultural population derived from national population censuses or surveys is scarce.
#> 6016 Economically active male population in agriculture is that part of the economically active male population engaged in or seeking work in agriculture, hunting, fishing or forestry.
#> 6064 Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.
#> 6065 Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 2 (IT-2) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 25 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.
#> 6066 Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.
#> 6067 Percent of population exposed to ambient concentrations of PM2.5 that exceed the WHO guideline value is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.
#> 6075 Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.
#> 6103 The non-agricultural population is obtained as a residual of agricultural population from the total population.
#> 6104 Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.
#> 6105 Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.
#> 6106 Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.
#> 6107 Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.
#> 6108 Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements.
#> 6113 Rural population density is the rural population divided by the arable land area. Rural population is calculated as the difference between the total population and the urban population. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.
#> 6114
#> 6120 Population in largest city is the urban population living in the country's largest metropolitan area.
#> 6121 Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.
#> 6122 Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people.
#> 6123 Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people.
#> 7474 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+).
#> 7475 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).
#> 7476 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+).
#> 7477 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+).
#> 7478 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+).
#> 7479 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+).
#> 7480 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+).
#> 7481 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24).
#> 7482 Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+).
#> 7945 Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse
#> 7946 Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse
#> 7947 Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse
#> 7948 Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse
#> 7949 Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse
#> 7950 Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and used a condom during last intercourse
#> 7951 Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2
#> 7952 Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2
#> 7953 Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2
#> 7954 Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2
#> 7955 Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2
#> 7956 Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2
#> 7993 Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.)
#> 7994 Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.)
#> 7995 Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.)
#> 7996 Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.)
#> 7997 Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.)
#> 7998 Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.)
#> 8011 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years
#> 8012 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years
#> 8013 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years
#> 8014 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years
#> 8015 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years
#> 8016 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their blood sugar measured in the last 5 years
#> 8041 Percentage of population over 18 having their blood pressure measured by health professional in the last year
#> 8042 Percentage of population over 18 having their blood pressure measured by health professional in the last year
#> 8043 Percentage of population over 18 having their blood pressure measured by health professional in the last year
#> 8044 Percentage of population over 18 having their blood pressure measured by health professional in the last year
#> 8045 Percentage of population over 18 having their blood pressure measured by health professional in the last year
#> 8046 Percentage of population over 18 having their blood pressure measured by health professional in the last year
#> 8047
#> 8048
#> 8049
#> 8050
#> 8051
#> 8052
#> 8053 Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-range may vary)
#> 8054 Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-range may vary)
#> 8055 Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-range may vary)
#> 8056 Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-range may vary)
#> 8057 Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-range may vary)
#> 8058 Percentage of adult population with high blood pressure or on treatment for high blood pressure (age-range may vary)
#> 8059
#> 8060
#> 8061
#> 8062
#> 8063
#> 8064
#> 8065 Percentage of adult population being treated for high blood pressure (age-range may vary)
#> 8066 Percentage of adult population being treated for high blood pressure (age-range may vary)
#> 8067 Percentage of adult population being treated for high blood pressure (age-range may vary)
#> 8068 Percentage of adult population being treated for high blood pressure (age-range may vary)
#> 8069 Percentage of adult population being treated for high blood pressure (age-range may vary)
#> 8070 Percentage of adult population being treated for high blood pressure (age-range may vary)
#> 8077
#> 8078 Percentage of adult population with high cholesterol or on treatment for high cholesterol (age-range may vary)
#> 8079 Percentage of adult population at risk (overweight or obese and older than 20, male and older than 34) having their cholesterol levels measured in the last 5 years
#> 8080 Percentage of adult population at risk (overweight or obese and older than 20, male and older than 34) having their cholesterol levels measured in the last 5 years
#> 8081 Percentage of adult population at risk (overweight or obese and older than 20, male and older than 34) having their cholesterol levels measured in the last 5 years
#> 8082 Percentage of adult population at risk (overweight or obese and older than 20, male and older than 34) having their cholesterol levels measured in the last 5 years
#> 8083 Percentage of adult population at risk (overweight or obese and older than 20, male and older than 34) having their cholesterol levels measured in the last 5 years
#> 8084 Percentage of adult population at risk (overweight or obese and older than 20, male and older than 34) having their cholesterol levels measured in the last 5 years
#> 8085 Percentage of adult population being treated for raised blood glucose or diabetes (age-range may vary)
#> 8086 Percentage of adult population being treated for raised blood glucose or diabetes (age-range may vary)
#> 8087 Percentage of adult population being treated for raised blood glucose or diabetes (age-range may vary)
#> 8088 Percentage of adult population being treated for raised blood glucose or diabetes (age-range may vary)
#> 8089 Percentage of adult population being treated for raised blood glucose or diabetes (age-range may vary)
#> 8090 Percentage of adult population being treated for raised blood glucose or diabetes (age-range may vary)
#> 8091
#> 8092 Percentage of adult population with impaired fasting glycaemia (age-range may vary)
#> 8117 Percentage of population age 18 and older using inpatient care in the last 12 months
#> 8118 Percentage of population age 18 and older using inpatient care in the last 12 months
#> 8119 Percentage of population age 18 and older using inpatient care in the last 12 months
#> 8120 Percentage of population age 18 and older using inpatient care in the last 12 months
#> 8121 Percentage of population age 18 and older using inpatient care in the last 12 months
#> 8122 Percentage of population age 18 and older using inpatient care in the last 12 months
#> 8135 Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8136 Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8137 Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8138 Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8139 Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8140 Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8141 Percentage of females aged 18 and older with BMI above 30
#> 8142 Percentage of females aged 18 and older with BMI above 30
#> 8143 Percentage of females aged 18 and older with BMI above 30
#> 8144 Percentage of females aged 18 and older with BMI above 30
#> 8145 Percentage of females aged 18 and older with BMI above 30
#> 8146 Percentage of females aged 18 and older with BMI above 30
#> 8147 Percentage of males aged 18 and older with BMI above 30
#> 8148 Percentage of males aged 18 and older with BMI above 30
#> 8149 Percentage of males aged 18 and older with BMI above 30
#> 8150 Percentage of males aged 18 and older with BMI above 30
#> 8151 Percentage of males aged 18 and older with BMI above 30
#> 8152 Percentage of males aged 18 and older with BMI above 30
#> 8153 Percentage of population aged 18 or older with BMI above 30
#> 8154 Percentage of population aged 18 or older with BMI above 30
#> 8155 Percentage of population aged 18 or older with BMI above 30
#> 8156 Percentage of population aged 18 or older with BMI above 30
#> 8157 Percentage of population aged 18 or older with BMI above 30
#> 8158 Percentage of population aged 18 or older with BMI above 30
#> 8165 Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8166 Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8167 Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8168 Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8169 Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8170 Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women and women having given birth in the three months preceding the survey)
#> 8171 Percentage of female population aged 18 or older with BMI above 25
#> 8172 Percentage of female population aged 18 or older with BMI above 25
#> 8173 Percentage of female population aged 18 or older with BMI above 25
#> 8174 Percentage of female population aged 18 or older with BMI above 25
#> 8175 Percentage of female population aged 18 or older with BMI above 25
#> 8176 Percentage of female population aged 18 or older with BMI above 25
#> 8177 Percentage of male population aged 18 or older with BMI above 25
#> 8178 Percentage of male population aged 18 or older with BMI above 25
#> 8179 Percentage of male population aged 18 or older with BMI above 25
#> 8180 Percentage of male population aged 18 or older with BMI above 25
#> 8181 Percentage of male population aged 18 or older with BMI above 25
#> 8182 Percentage of male population aged 18 or older with BMI above 25
#> 8183 Percentage of population aged 18 or older with BMI above 25
#> 8184 Percentage of population aged 18 or older with BMI above 25
#> 8185 Percentage of population aged 18 or older with BMI above 25
#> 8186 Percentage of population aged 18 or older with BMI above 25
#> 8187 Percentage of population aged 18 or older with BMI above 25
#> 8188 Percentage of population aged 18 or older with BMI above 25
#> 8195 Percentage of population pushed below 60% of median consumption by out-of-pocket health spending
#> 8196 Percentage of population pushed below 60% of median consumption by out-of-pocket health spending
#> 8197 Percentage of population pushed below 60% of median consumption by out-of-pocket health spending
#> 8198 Percentage of population pushed below 60% of median consumption by out-of-pocket health spending
#> 8199 Percentage of population pushed below 60% of median consumption by out-of-pocket health spending
#> 8200 Percentage of population pushed below 60% of median consumption by out-of-pocket health spending
#> 8202 Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending
#> 8203 Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending
#> 8204 Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending
#> 8205 Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending
#> 8206 Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending
#> 8207 Percentage of population pushed below 1.90 international $ per day consumption poverty line by out-of-pocket health spending
#> 8209 Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending
#> 8210 Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending
#> 8211 Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending
#> 8212 Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending
#> 8213 Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending
#> 8214 Percentage of population pushed below 3.20 international $ per day consumption poverty line by out-of-pocket health spending
#> 8216
#> 8217
#> 8218
#> 8219
#> 8220
#> 8221
#> 8223
#> 8224
#> 8225
#> 8226
#> 8227
#> 8228
#> 8229
#> 8230
#> 8231
#> 8232
#> 8233
#> 8234
#> 8242 Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure
#> 8243 Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure
#> 8244 Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure
#> 8245 Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure
#> 8246 Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure
#> 8247 Percentage of population with out-of-pocket health spending larger than 10% of total household expenditure
#> 8248 Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure
#> 8249 Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure
#> 8250 Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure
#> 8251 Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure
#> 8252 Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure
#> 8253 Percentage of population with out-of-pocket health spending larger than 25% of total household expenditure
#> 8708 Population growth rate over the 10 year period. This is simple growth rate calculation between two population observations that are 10 year apart.
#> 8709
#> 8710
#> 8711
#> 8712
#> 8713
#> 8714
#> 8775 Average Population Per Bank Office (In Thousands)
#> 8779 Health infrastructure indicator -- Number of Allopathic Doctors in Government Hospitals per 100,000 population
#> 8781 Health infrastructure indicator -- Number of beds in Government Hospitals (public) per 100,000 population
#> 8783 Health infrastructure indicator - Number of government hospitals (public) per 100,000 population
#> 8863
#> 8864
#> 8865
#> 8880 Rural roads density is measured in KMs of rural roads in the area (State, District) divided by population in thousands in that area (State, District)\nRural roads are roads within a district for which the specifications are lower than for district roads.
#> 8882 Urban roads density is measured in KMs of Urban roads in the area (State, District) divided by population in thousands in that area (State, District)\nUrban roads are roads within a limits of a Municipality, Military Cantonment, Port o a Railway Authority.
#> 8972 Population covered by a mobile-cellular network is the percentage of people within range of a mobile-cellular signal, irrespective of whether they are subscribers or users or not. This is calculated by dividing the number of people within range of a mobile-cellular signal by the total population and multiplying by 100.
#> 9027 Please cite the International Telecommunication Union for third-party use of these data. Percentage of population covered by mobile cellular telephony refers to the percentage of a country’s inhabitants that live within areas served by a mobile cellular signal, irrespective of whether or not they choose to use it. This should not be confused with the percentage of the land area covered by a mobile cellular signal or the percentage of the population that subscribe to mobile cellular service. Note that this measures the theoretical ability to use mobile cellular services if one has a cellular telephone and a subscription.
#> 9052 Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.
#> 9176
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#> 9364
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#> 9826
#> 11715 Coverage of social protection and labor programs (SPL) shows the percentage of population participating in social insurance, social safety net, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.
#> 11993 Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.
#> 11997 Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.
#> 12001 Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.
#> 12005 Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.
#> 12009 Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.
#> 12013 Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.
#> 12165 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12166 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12167 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12168 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12169 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12170 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12171 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12172 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12173 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12174 Percentage of population only receiving Labor Market programs (includes direct and indirect beneficiaries)
#> 12175 Percentage of population not receiving Social Protection programs
#> 12176 Percentage of population not receiving Social Protection programs
#> 12177 Percentage of population not receiving Social Protection programs
#> 12178 Percentage of population not receiving Social Protection programs
#> 12179 Percentage of population not receiving Social Protection programs
#> 12180 Percentage of population not receiving Social Protection programs
#> 12181 Percentage of population not receiving Social Protection programs
#> 12182 Percentage of population not receiving Social Protection programs
#> 12183 Percentage of population not receiving Social Protection programs
#> 12184 Percentage of population not receiving Social Protection programs
#> 12185 Percentage of population only receiving receiving only 1 SPL program (includes direct and indirect beneficiaries)
#> 12186 Percentage of population only receiving receiving only 1 SPL program (includes direct and indirect beneficiaries)
#> 12187 Percentage of population only receiving receiving only 1 SPL program (includes direct and indirect beneficiaries)
#> 12188 Percentage of population only receiving receiving only 1 SPL program (includes direct and indirect beneficiaries)
#> 12189 Percentage of population only receiving receiving only 1 SPL program (includes direct and indirect beneficiaries)
#> 12190 Percentage of population only receiving receiving only 1 SPL program (includes direct and indirect beneficiaries)
#> 12191 Percentage of population receiving 1 SPL program (includes direct and indirect beneficiaries)
#> 12192 Percentage of population receiving 1 SPL program (includes direct and indirect beneficiaries)
#> 12193 Percentage of population receiving 1 SPL program (includes direct and indirect beneficiaries)
#> 12194 Percentage of population receiving 1 SPL program (includes direct and indirect beneficiaries)
#> 12195 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12196 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12197 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12198 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12199 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12200 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12201 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12202 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12203 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12204 Percentage of population receiving 2 SPL programs (includes direct and indirect beneficiaries)
#> 12205 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12206 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12207 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12208 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12209 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12210 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12211 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12212 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12213 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12214 Percentage of population receiving 3 SPL programs (includes direct and indirect beneficiaries)
#> 12215 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12216 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12217 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12218 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12219 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12220 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12221 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12222 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12223 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12224 Percentage of population receiving 4 or more SPL programs (includes direct and indirect beneficiaries)
#> 12748 Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.
#> 12752 Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.
#> 12756 Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.
#> 12760 Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.
#> 12764 Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.
#> 12768 Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.
#> 13893 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13894 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13895 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13896 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13897 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13898 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13899 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13900 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13901 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13902 Percentage of population only receiving All Social Assistance programs (includes direct and indirect beneficiaries)
#> 13903 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13904 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13905 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13906 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13907 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13908 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13909 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13910 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13911 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 13912 Percentage of population receiving Social Assistance and Other program (includes direct and indirect beneficiaries)
#> 14019 Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.
#> 14023 Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.
#> 14027 Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.
#> 14031 Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.
#> 14035 Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.
#> 14039 Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.
#> 14330 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14331 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14332 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14333 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14334 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14335 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14336 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14337 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14338 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14339 Percentage of population only receiving All Social Insurance and Labor Market programs (includes direct and indirect beneficiaries)
#> 14340 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14341 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14342 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14343 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14344 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14345 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14346 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14347 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14348 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14349 Percentage of population only receiving All Social Insurance programs (includes direct and indirect beneficiaries)
#> 14446 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14447 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14448 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14449 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14450 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14451 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14452 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14453 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14454 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14455 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14456 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14457 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14458 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14459 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14460 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14461 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14462 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14463 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14464 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14465 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14466 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14467 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14468 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14469 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14470 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14471 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14472 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14473 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14474 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14475 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14476 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14477 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14478 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14479 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14480 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14481 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14482 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14483 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14484 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14485 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14486 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14487 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14488 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14489 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14490 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14491 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14492 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14493 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14494 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14495 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14496 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14497 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14498 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14499 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14500 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14501 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14502 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14503 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14504 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14505 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14506 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14507 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14508 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14509 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14510 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14511 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14512 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14513 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14514 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14515 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14516 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14517 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14518 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14519 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14520 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14521 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14522 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14523 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14524 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14525 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14526 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14527 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14528 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14529 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14530 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14531 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14532 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14533 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14534 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14535 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14536 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14537 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14538 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14539 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14540 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14541 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14542 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14543 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14544 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14545 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14546 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14547 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14548 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14549 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14550 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14551 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14552 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14553 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14554 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14555 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14556 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14557 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14558 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14559 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14560 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14561 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14562 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14563 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14564 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14565 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14566 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14567 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14568 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14569 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14570 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14571 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14572 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14573 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14574 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14575 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14576 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14577 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14578 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14579 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14580 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14581 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14582 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14583 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14584 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14585 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14586 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14587 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14588 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14589 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14590 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14591 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14592 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14593 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14594 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14595 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14596 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14597 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14598 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14599 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14600 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14601 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14602 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14603 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14604 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14605 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14606 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14607 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14608 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14609 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14610 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14611 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14612 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14613 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14614 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14615 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14616 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14617 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14618 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14619 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14620 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14621 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14622 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14623 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14624 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14625 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14626 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14627 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14628 Share of the population of the stated age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14629 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14630 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14631 Share of the population of the stated age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14632 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14633 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14634 Share of the population of the stated age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14635 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14636 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14637 Share of the population of the stated age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14638 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14639 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14640 Share of the population of the stated age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14641 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14642 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14643 Share of the population of the stated age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14682 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14683 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14684 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14685 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14686 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14687 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14688 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14689 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14690 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14691 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14692 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14693 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14694 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14695 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14696 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14697 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14698 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14699 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14700 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14701 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14702 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14703 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14704 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14705 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14706 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14707 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14708 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14709 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14710 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14711 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14712 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14713 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14714 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14715 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14716 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14717 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14718 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14719 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14720 Total population in thousands in the specified age group that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14721 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14722 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14723 Total population in thousands in the specified age group that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14724 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14725 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14726 Total population in thousands in the specified age group that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14727 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14728 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14729 Total population in thousands in the specified age group that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14730 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14731 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14732 Total population in thousands in the specified age group that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14733 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14734 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14735 Total population in thousands in the specified age group that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14736 Total population in thousands that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14737 Total population in thousands that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14738 Total population in thousands that has completed primary education or incomplete lower secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14739 Total population in thousands that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14740 Total population in thousands that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14741 Total population in thousands that has completed lower secondary or incomplete upper secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14742 Total population in thousands that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14743 Total population in thousands that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14744 Total population in thousands that has completed upper secondary or incomplete post-secondary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14745 Total population in thousands that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14746 Total population in thousands that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14747 Total population in thousands that has completed post-secondary or tertiary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14748 Total population in thousands that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14749 Total population in thousands that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14750 Total population in thousands that has never attended school. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14751 Total population in thousands that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14752 Total population in thousands that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14753 Total population in thousands that has pre-primary education or incomplete primary education as the highest level of educational attainment. Projections are based on collected census and survey data for the base year (around 2010) and the Medium Shared Socioeconomic Pathways (SSP2) projection model. The SSP2 is a middle-of-the-road scenario that combines medium fertility with medium mortality, medium migration, and the Global Education Trend (GET) education scenario. For more information and other projection models, consult the Wittgenstein Centre for Demography and Global Human Capital's website: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14841 SPI scores for sources dimension
#> 15186 Literacy Rate is the population aged 15 years and over who are able to read and write latin, arabic or other scripts, presented in percentage terms.
#> 15278 The percentage of population ages 25 and over that attained or completed primary education.
#> 15279 The percentage of population ages 25 and over that attained or completed primary education.
#> 15280 The percentage of population ages 25 and over that attained or completed primary education.
#> 15501 Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age.
#> 15502 Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age.
#> 15503 Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age.
#> 15761 The percentage of population ages 25 and over that attained or completed lower secondary education.
#> 15762 The percentage of population ages 25 and over that attained or completed lower secondary education.
#> 15763 The percentage of population ages 25 and over that attained or completed lower secondary education.
#> 15764 The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.
#> 15765 The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.
#> 15766 The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.
#> 15767 The percentage of population ages 25 and over that attained or completed upper secondary education.
#> 15768 The percentage of population ages 25 and over that attained or completed upper secondary education.
#> 15769 The percentage of population ages 25 and over that attained or completed upper secondary education.
#> 15841 The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.
#> 15842 The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.
#> 15843 The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.
#> 15844 The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.
#> 15845 The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.
#> 15846 The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.
#> 15847 The percentage of population ages 25 and over that attained or completed Master's or equivalent.
#> 15848 The percentage of population ages 25 and over that attained or completed Master's or equivalent.
#> 15849 The percentage of population ages 25 and over that attained or completed Master's or equivalent.
#> 15850 The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.
#> 15851 The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.
#> 15852 The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.
#> 16251 Inpatient admission rate is the percentage of the population admitted to hospitals during a year.
#> 16258 Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months.
#> 16259 Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months.
#> 16269 Number of female deaths ages 0-4 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 0-4, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16270 Number of male deaths ages 0-4 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 0-4, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16271 Number of deaths ages 0-4 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 0-4, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16272 Number of female deaths ages 5-14 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 5-14, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16273 Number of male deaths ages 5-14 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 5-14, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16274 Number of deaths ages 5-14 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 5-14, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16275 Number of female deaths ages 15-59 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 15-59, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16276 Number of male deaths ages 15-59 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 15-59, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16277 Number of deaths ages 15-59 due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 15-59, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16278 Number of female deaths ages 60+ due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths ages 60+, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16279 Number of male deaths ages 60+ due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths ages 60+, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16280 Number of deaths ages 60+ due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all deaths ages 60+, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16281 Number of female deaths due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all female deaths, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16282 Number of male deaths due to communicable diseases and maternal, prenatal and nutrition conditions divided by number of all male deaths, expressed by percentage. Communicable diseases and maternal, prenatal and nutrition conditions included infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.
#> 16287 Number of female deaths ages 0-4 due to injury divided by number of all female deaths ages 0-4, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16288 Number of male deaths ages 0-4 due to injury divided by number of all male deaths ages 0-4, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16289 Number of deaths ages 0-4 due to injury divided by number of all deaths ages 0-4, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16290 Number of female deaths ages 5-14 due to injury divided by number of all female deaths ages 5-14, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16291 Number of male deaths ages 5-14 due to injury divided by number of all male deaths ages 5-14, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16292 Number of deaths ages 5-14 due to injury divided by number of all deaths ages 5-14, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16293 Number of female deaths ages 15-59 due to injury divided by number of all female deaths ages 15-59, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16294 Number of male deaths ages 15-59 due to injury divided by number of all male deaths ages 15-59, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16295 Number of deaths ages 15-59 due to injury divided by number of all deaths ages 15-59, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16296 Number of female deaths ages 60+ due to injury divided by number of all female deaths ages 60+, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16297 Number of male deaths ages 60+ due to injury divided by number of all male deaths ages 60+, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16298 Number of deaths ages 60+ due to injury divided by number of all deaths ages 60+, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16299 Number of female deaths due to injury divided by number of all female deaths, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16300 Number of male deaths ages due to injury divided by number of all male deaths ages, expressed by percentage. Injury includes unintentional and intentional injuries.
#> 16305 Number of female deaths ages 0-4 due to non-communicable diseases divided by number of all female deaths ages 0-4, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16306 Number of male deaths ages 0-4 due to non-communicable diseases divided by number of all male deaths ages 0-4, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16307 Number of deaths ages 0-4 due to non-communicable diseases divided by number of all deaths ages 0-4, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16308 Number of female deaths ages 5-14 due to non-communicable diseases divided by number of all female deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16309 Number of male deaths ages 5-14 due to non-communicable diseases divided by number of all male deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16310 Number of deaths ages 5-14 due to non-communicable diseases divided by number of all deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16311 Number of female deaths ages 15-59 due to non-communicable diseases divided by number of all female deaths ages 15-59, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16312 Number of male deaths ages 15-59 due to non-communicable diseases divided by number of all male deaths ages 15-59, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16313 Number of deaths ages 15-59 due to non-communicable diseases divided by number of all deaths ages 15-59 expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16314 Number of female deaths ages 60+ due to non-communicable diseases divided by number of all female deaths ages 60+, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16315 Number of male deaths ages 60+ due to non-communicable diseases divided by number of all male deaths ages 60+, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16316 Number of deaths ages 60+ due to non-communicable diseases divided by number of all deaths ages 60+, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16317 Number of female deaths due to non-communicable diseases divided by number of all female deaths, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16318 Number of male deaths ages due to non-communicable diseases divided by number of all male deaths ages, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.
#> 16330 Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV.
#> 16333 Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.
#> 16421
#> 16422
#> 16423
#> 16424
#> 16425
#> 16426
#> 16427
#> 16428
#> 16429
#> 16430
#> 16431 The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
#> 16432
#> 16433
#> 16434
#> 16435
#> 16436
#> 16437 The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
#> 16438 The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
#> 16439 Access to an improved water source, rural, refers to the percentage of the rural population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection).
#> 16440 Access to an improved water source, urban, refers to the percentage of the urban population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection).
#> 16441 Access to an improved water source refers to the percentage of the population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection).
#> 16442 The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
#> 16443 The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
#> 16444 The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
#> 16462 Number of new HIV infections among uninfected populations ages 50+ expressed per 1,000 uninfected population ages 50+ in the year before the period.
#> 16463 Number of new HIV infections among uninfected female populations ages 15-49 expressed per 1,000 uninfected female population ages 15-49 in the year before the period.
#> 16464 Number of new HIV infections among uninfected male populations ages 15-49 expressed per 1,000 uninfected male population ages 15-49 in the year before the period.
#> 16466 Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.
#> 16468 Number of new HIV infections among uninfected male populations ages 15-24 expressed per 1,000 uninfected male population ages 15-24 in the year before the period.
#> 16469 Number of new HIV infections among uninfected female populations ages 15-24 expressed per 1,000 uninfected female population ages 15-24 in the year before the period.
#> 16470 Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period.
#> 16471 Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period.
#> 16505 Immunization is the process whereby weakened bacteria is injected or taken orally for the purpose of developing an immunity towards a particular disease.
#> 16537
#> 16539 Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population.
#> 16544 Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.
#> 16567 Use of insecticide-treated bed nets refers to the percentage of children under age five who slept under an insecticide-treated bednet to prevent malaria.
#> 16618 The number of procedures undertaken in an operating theatre per 100,000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain.
#> 16619
#> 16620 Access to improved sanitation facilities refers to the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet.
#> 16621 Access to improved sanitation facilities, rural, refers to the percentage of the rural population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet.
#> 16622 Access to improved sanitation facilities, urban, refers to the percentage of the urban population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet.
#> 16623 Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).
#> 16624 Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).
#> 16625 Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).
#> 16655
#> 16656
#> 16657
#> 16658
#> 16659
#> 16660
#> 16661
#> 16662
#> 16663
#> 16664
#> 16665 The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.
#> 16666
#> 16667
#> 16668
#> 16669
#> 16670
#> 16671 The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.
#> 16672 The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.
#> 16696 Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.
#> 16714 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16715 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16716 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16717 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16718 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16719 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16720 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16721 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16722 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16723 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16724 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16725 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16726 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16727 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16728 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16729 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16730 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16731 The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.
#> 16754 Prevalence of obesity adult is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is 30 kg/m² or higher. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.
#> 16755 Prevalence of obesity adult is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is 30 kg/m² or higher. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.
#> 16756
#> 16757
#> 16758
#> 16759
#> 16760
#> 16761
#> 16762
#> 16763
#> 16764
#> 16765
#> 16766 People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.
#> 16767
#> 16768
#> 16769
#> 16770
#> 16771
#> 16772 People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.
#> 16773 People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.
#> 16799 Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.
#> 16800 Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.
#> 16801 Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.
#> 16802 The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs.
#> 16803 The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs.
#> 16804 The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs.
#> 16819 Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).
#> 16820 Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).
#> 16821 Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).
#> 16822 Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population.
#> 16823 Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population.
#> 16824 Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population.
#> 16825 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.
#> 16826 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.
#> 16827 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.
#> 16851 The estimated number of deaths attributable to tuberculosis (TB) in a given time period.
#> 16852 The estimated number of deaths attributable to tuberculosis (TB) in a given time period.
#> 16853 The number of cases of tuberculosis (all forms) in a population at a given point in time (the middle of the calendar year), expressed as the rate per 100 000 population. It is sometimes referred to as "point prevalence". Estimates include cases of TB in people with HIV. Published values are rounded to three significant figures. Uncertainty bounds are provided in addition to best estimates.
#> 16854 The number of cases of tuberculosis (all forms) in a population at a given point in time (the middle of the calendar year), expressed as the rate per 100 000 population. It is sometimes referred to as "point prevalence" high uncertainty bound. Estimates include cases of TB in people with HIV. Published values are rounded to three significant figures. Uncertainty bounds are provided in addition to best estimates.
#> 16855 The number of cases of tuberculosis (all forms) in a population at a given point in time (the middle of the calendar year), expressed as the rate per 100 000 population. It is sometimes referred to as "point prevalence" low uncertainty bound. Estimates include cases of TB in people with HIV. Published values are rounded to three significant figures. Uncertainty bounds are provided in addition to best estimates.
#> 16857 Proportion of population pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure, expressed as a percentage of a total population of a country
#> 16859 Proportion of population pushed further below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $1.90 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16861 Proportion of population pushed further below the $3.20 ($2011 PPP) poverty line by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $3.20 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16863 Proportion of population pushed further below a relative poverty line of 60% of median per capita consumption by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16867 Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 1.90 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16871 Proportion of population pushed below the $3.20 ($2011 PPP) poverty line by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.20 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16873 Proportion of population pushed below a relative poverty line of 60% of median per capita consumption by out-of-pocket health care expenditure. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16875 Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16877 Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).
#> 16930 Poverty headcount ratio at $3.10 a day is the percentage of the population living on less than $3.10 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.
#> 16931 Multidimensional poverty, educational attainment (% of population deprived) is percentage of population deprived of primary educational attainment. A household is deprived if no adult (grade 9 equivalent age or above) has completed primary education.
#> 16933 Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.
#> 16934 Poverty headcount ratio at $1.90 a day, age 0-14 is the percentage of population age 0-14 living on less than $1.90 a day at 2011 international prices.
#> 16935 Poverty headcount ratio at $1.90 a day, age 15-64 is the percentage of population age 15-64 living on less than $1.90 a day at 2011 international prices.
#> 16936 Poverty headcount ratio at $1.90 a day, without education is the percentage of population age 16 and over without education living on less than $1.90 a day at 2011 international prices.
#> 16937 Poverty headcount ratio at $1.90 a day, with primary education is the percentage of population age 16 and over with primary education living on less than $1.90 a day at 2011 international prices.
#> 16938 Poverty headcount ratio at $1.90 a day, with secondary education is the percentage of population age 16 and over with secondary education living on less than $1.90 a day at 2011 international prices.
#> 16939 Poverty headcount ratio at $1.90 a day, with Tertiary/post-secondary education is the percentage of population age 16 and over with Tertiary/post-secondary education living on less than $1.90 a day at 2011 international prices.
#> 16940 Poverty headcount ratio at $1.90 a day, age 65+ is the percentage of population age 65 and over living on less than $1.90 a day at 2011 international prices.
#> 16942 Poverty headcount ratio at $1.90 a day, female is the percentage of female population living on less than $1.90 a day at 2011 international prices.
#> 16943
#> 16944 Poverty headcount ratio at $1.90 a day, male is the percentage of male population living on less than $1.90 a day at 2011 international prices.
#> 16945 Multidimensional poverty, Monetary poverty (% of population deprived) is the percentage of the population living on less than $1.90 a day at 2011 international prices. A household is deprived if income or expenditure, in 2011 purchasing power parity U.S. dollars, is less than US$1.90 per person per day. The indicator may differ from the one used for monitoring monetary poverty, if the multidimensional poverty measure comes from a different survey (or different version of the same survey).
#> 16946 Poverty headcount ratio at $1.90 a day, rural is the percentage of rural population living on less than $1.90 a day at 2011 international prices.
#> 16947
#> 16948 The share of total global poor population.
#> 16949
#> 16950 Poverty headcount ratio at $1.90 a day, urban is the percentage of urban population living on less than $1.90 a day at 2011 international prices.
#> 16951 Multidimensional poverty, electricity (% of population deprived) is percentage of population deprived of electricity. A household is deprived if it does not have access to electricity.
#> 16952 Multidimensional poverty, educational enrollment (% of population deprived) is percentage of population deprived of school enrollment. A household is deprived if at least one child (grade 8 equivalent age or below) is not enrolled in school.
#> 16959 Multidimensional poverty, headcount ratio (% of population) is the share of people who are considered multidimensionally deprived. It is estimated on the basis of three dimensions—monetary, education, and basic infrastructure access and an overall poverty cutoff of one-third of the weighted deprivations. Household is multidimensionally poor if it is deprived in more than a third of weighted deprivations.
#> 16960 Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.
#> 16961
#> 16964
#> 16965
#> 16966 The percentage of people who are multidimensionally poor
#> 16967 The percentage of children who are multidimensionally poor
#> 16968 Proportion of the child population that is multidimensionally poor adjusted by the intensity of the deprivations
#> 16969 The percentage of female population who are multidimensionally poor
#> 16972 The percentage of male population who are multidimensionally poor
#> 16976 National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.
#> 16977
#> 16979 Poverty Rate: Number of people live below the poverty line compared to total population.
#> 16985 Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines.
#> 16986 Multidimensional poverty, sanitation (% of population deprived) is percentage of population deprived of sanitation. A household is deprived if it does not have access to even a limited standard of sanitation.
#> 16987 Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.
#> 16988
#> 16991
#> 16992
#> 16994 Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines.
#> 16995 Multidimensional poverty, drinking water (% of population deprived) is percentage of population deprived of drinking water. A household is deprived if it does not have access to even a limited standard of drinking water.
#> 17001
#> 17002 Mean consumption or income per capita (2011 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.
#> 17004 The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.
#> 17005 Mean consumption or income per capita (2011 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.
#> 17006 Mean consumption or income per capita (2005 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.
#> 17007 The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.
#> 17023 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
#> 17024 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
#> 17025 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
#> 17026 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
#> 17027 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
#> 17028 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
#> 17050 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
#> 17051 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
#> 17052 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
#> 17053 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
#> 17054 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
#> 17055 Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
#> 17127 Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17128 Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17129 Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17130 The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17131 The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17132 The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17133 The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17134 The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17135 The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17136 Labor force participation rate is the proportion of the population ages 25-34 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17137 Labor force participation rate is the proportion of the population ages 25-34 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17138 Labor force participation rate is the proportion of the population ages 25-34 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17139 Labor force participation rate is the proportion of the population ages 25-54 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17140 Labor force participation rate is the proportion of the population ages 25-54 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17141 Labor force participation rate is the proportion of the population ages 25-54 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17142 Labor force participation rate is the proportion of the population ages 35-54 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17143 Labor force participation rate is the proportion of the population ages 35-54 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17144 Labor force participation rate is the proportion of the population ages 35-54 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17145 Labor force participation rate is the proportion of the population ages 55-64 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17146 Labor force participation rate is the proportion of the population ages 55-64 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17147 Labor force participation rate is the proportion of the population ages 55-64 that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17148 Labor force participation rate is the proportion of the population ages 65 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17149 Labor force participation rate is the proportion of the population ages 65 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17150 Labor force participation rate is the proportion of the population ages 65 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. The participation rates are harmonized to account for differences in national data collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The series includes both nationally reported and imputed data and only estimates that are national, meaning there are no geographic limitations in coverage.
#> 17151 Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17152 Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17155 Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17156 Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17157 Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17158 Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.
#> 17161 The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17162 The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17163 The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).
#> 17204 Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.
#> 17205 Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.
#> 17206 Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.
#> 17226 Emigration rate of tertiary educated shows the stock of emigrants ages 25 and older, residing in an OECD country other than that in which they were born, with at least one year of tertiary education as a percentage of the population age 25 and older with tertiary education.
#> 17228
#> 17229
#> 17231
#> 17233 Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted.
#> 17234 Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of origin generally refers to the nationality or country of citizenship of a claimant.
#> 17236 International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees. The data used to estimate the international migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population--people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population--that is, people who are citizens of a country other than the country in which they reside--are used as estimates. After the breakup of the Soviet Union in 1991 people living in one of the newly independent countries who were born in another were classified as international migrants. Estimates of migrant stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migrant stock for at least two points in time, interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migrants for countries that had no data.
#> 17240 Population below minimum dietary energy consumption is the population whose food intake is insufficient to meet dietary energy requirements continuously.
#> 17241 Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%.
#> 17244 The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.
#> 17246 The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.
#> 17267
#> 17274
#> 17342 Female population between the ages 0 to 4.
#> 17343 Female population between the ages 0 to 4 as a percentage of the total female population.
#> 17344 Male population between the ages 0 to 4.
#> 17345 Male population between the ages 0 to 4 as a percentage of the total male population.
#> 17346 Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17347 Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population.
#> 17348 Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17349 Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population.
#> 17350 Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17351 Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population.
#> 17352 Population ages 0 to 24 is the percentage of the total population that is in the age group 0 to 24.
#> 17353 Population, ages 3-5, female is the total number of females age 3-5.
#> 17354 Population, ages 3-5, male is the total number of males age 3-5.
#> 17355 Population, ages 3-5, total is the total population age 3-5.
#> 17356 Population, ages 4-6, female is the total number of females age 4-6.
#> 17357 Population, ages 4-6, male is the total number of males age 4-6.
#> 17358 Population, ages 4-6, total is the total population age 4-6.
#> 17359 Female population between the ages 5 to 9.
#> 17360 Female population between the ages 5 to 9 as a percentage of the total female population.
#> 17361 Population, ages 5-9, female is the total number of females age 5-9.
#> 17362 Male population between the ages 5 to 9.
#> 17363 Male population between the ages 5 to 9 as a percentage of the total male population.
#> 17364 Population, ages 5-9, male is the total number of males age 5-9.
#> 17365 Population, ages 5-9, total is the total population age 5-9.
#> 17366 Population, ages 5-10, female is the total number of females age 5-10.
#> 17367 Population, ages 5-10, male is the total number of males age 5-10.
#> 17368 Population, ages 5-10, total is the total population age 5-10.
#> 17369 Population, ages 5-11, female is the total number of females age 5-11.
#> 17370 Population, ages 5-11, male is the total number of males age 5-11.
#> 17371 Population, ages 5-11, total is the total population age 5-11.
#> 17372 Population, ages 6-9, male is the total number of males age 6-9.
#> 17373 Population, ages 6-9, total is the total population age 6-9.
#> 17374 Population, ages 6-9, female is the total number of females age 6-9.
#> 17375 Population, ages 6-10, female is the total number of females age 6-10.
#> 17376 Population, ages 6-10, male is the total number of males age 6-10.
#> 17377 Population, ages 6-10, total is the total population age 6-10.
#> 17378 Population, ages 6-11, female is the total number of females age 6-11.
#> 17379 Population, ages 6-11, male is the total number of males age 6-11.
#> 17380 Population, ages 6-11, total is the total population age 6-11.
#> 17381 Population, ages 6-12, female is the total number of females age 6-12.
#> 17382 Population, ages 6-12, male is the total number of males age 6-12.
#> 17383 Population, ages 6-12, total is the total population age 6-12.
#> 17384 Population, ages 7-9, female is the total number of females age 7-9.
#> 17385 Population, ages 7-9, male is the total number of males age 7-9.
#> 17386 Population, ages 7-9, total is the total population age 7-9.
#> 17387 Population, ages 7-10, female is the total number of females age 7-10.
#> 17388 Population, ages 7-10, male is the total number of males age 7-10.
#> 17389 Population, ages 7-10, total is the total population age 7-10.
#> 17390 Population, ages 7-11, female is the total number of females age 7-11.
#> 17391 Population, ages 7-11, male is the total number of males age 7-11.
#> 17392 Population, ages 7-11, total is the total population age 7-11.
#> 17393 Population, ages 7-12, female is the total number of females age 7-12.
#> 17394 Population, ages 7-12, male is the total number of males age 7-12.
#> 17395 Population, ages 7-12, total is the total population age 7-12.
#> 17396 Population, ages 7-13, female is the total number of females age 7-13.
#> 17397 Population, ages 7-13, male is the total number of males age 7-13.
#> 17398 Population, ages 7-13, total is the total population age 7-13.
#> 17399 Female population between the ages 10 to 14.
#> 17400 Female population between the ages 10 to 14 as a percentage of the total female population.
#> 17401 Population, ages 10-14, female is the total number of females age 10-14.
#> 17402 Male population between the ages 10 to 14.
#> 17403 Male population between the ages 10 to 14 as a percentage of the total male population.
#> 17404 Population, ages 10-14, male is the total number of males age 10-14.
#> 17405 Population, ages 10-14, total is the total population age 10-14.
#> 17406 Population, ages 10-15, female is the total number of females age 10-15.
#> 17407 Population, ages 10-15, male is the total number of males age 10-15.
#> 17408 Population, ages 10-15, total is the total population age 10-15.
#> 17409 Population, ages 10-16, female is the total number of females age 10-16.
#> 17410 Population, ages 10-16, male is the total number of males age 10-16.
#> 17411 Population, ages 10-16, total is the total population age 10-16.
#> 17412 Population, ages 10-17, female is the total number of females age 10-17.
#> 17413 Population, ages 10-17, male is the total number of males age 10-17.
#> 17414 Population, ages 10-17, total is the total population age 10-17.
#> 17415 Population, ages 10-18, female is the total number of females age 10-18.
#> 17416 Population, ages 10-18, male is the total number of males age 10-18.
#> 17417 Population, ages 10-18, total is the total population age 10-18.
#> 17418 Population, ages 11-15, female is the total number of females age 11-15.
#> 17419 Population, ages 11-15, male is the total number of males age 11-15.
#> 17420 Population, ages 11-15, total is the total population age 11-15.
#> 17421 Population, ages 11-16, female is the total number of females age 11-16.
#> 17422 Population, ages 11-16, male is the total number of males age 11-16.
#> 17423 Population, ages 11-16, total is the total population age 11-16.
#> 17424 Population, ages 11-17, female is the total number of females age 11-17.
#> 17425 Population, ages 11-17, male is the total number of males age 11-17.
#> 17426 Population, ages 11-17, total is the total population age 11-17.
#> 17427 Population, ages 11-18, female is the total number of females age 11-18.
#> 17428 Population, ages 11-18, male is the total number of males age 11-18.
#> 17429 Population, ages 11-18, total is the total population age 11-18.
#> 17430 Population, ages 12-15, female is the total number of females age 12-15.
#> 17431 Population, ages 12-15, male is the total number of males age 12-15.
#> 17432 Population, ages 12-15, total is the total population age 12-15.
#> 17433 Population, ages 12-16, female is the total number of females age 12-16.
#> 17434 Population, ages 12-16, male is the total number of males age 12-16.
#> 17435 Population, ages 12-16, total is the total population age 12-16.
#> 17436 Population, ages 12-17, female is the total number of females age 12-17.
#> 17437 Population, ages 12-17, male is the total number of males age 12-17.
#> 17438 Population, ages 12-17, total is the total population age 12-17.
#> 17439 Population, ages 12-18, female is the total number of females age 12-18.
#> 17440 Population, ages 12-18, male is the total number of males age 12-18.
#> 17441 Population, ages 12-18, total is the total population age 12-18.
#> 17442 Population, ages 13-16, female is the total number of females age 13-16.
#> 17443 Population, ages 13-16, male is the total number of males age 13-16.
#> 17444 Population, ages 13-16, total is the total population age 13-16.
#> 17445 Population, ages 13-17, female is the total number of females age 13-17.
#> 17446 Population, ages 13-17, male is the total number of males age 13-17.
#> 17447 Population, ages 13-17, total is the total population age 13-17.
#> 17448 Population, ages 13-18, female is the total number of females age 13-18.
#> 17449 Population, ages 13-18, male is the total number of males age 13-18.
#> 17450 Population, ages 13-18, total is the total population age 13-18.
#> 17451 Population, ages 13-19, female is the total number of females age 13-19.
#> 17452 Population, ages 13-19, male is the total number of males age 13-19.
#> 17453 Population, ages 13-19, total is the total population age 13-19.
#> 17454 Population, ages 14-18, female is the total number of females age 14-18.
#> 17455 Population, ages 14-18, male is the total number of males age 14-18.
#> 17456 Population, ages 14-18, total is the total population age 14-18.
#> 17457 Population, ages 14-19, female is the total number of females age 14-19.
#> 17458 Population, ages 14-19, male is the total number of males age 14-19.
#> 17459 Population, ages 14-19, total is the total population age 14-19.
#> 17460 Female population between the ages 15 to 19.
#> 17461 Female population between the ages 15 to 19 as a percentage of the total female population.
#> 17462 Male population between the ages 15 to 19.
#> 17463 Male population between the ages 15 to 19 as a percentage of the total male population.
#> 17464 Population, ages 15-24, female is the total number of females age 15-24.
#> 17465 Population, ages 15-24, male is the total number of males age 15-24.
#> 17466 Population, ages 15-24, total is the total population age 15-24.
#> 17467 Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17468 Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17469
#> 17470
#> 17471 Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17472 Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17473 Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17474 Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17475 Female population between the ages 20 to 24.
#> 17476 Female population between the ages 20 to 24 as a percentage of the total female population.
#> 17477 Male population between the ages 20 to 24.
#> 17478 Male population between the ages 20 to 24 as a percentage of the total male population.
#> 17479 Female population between the ages 25 to 29.
#> 17480 Female population between the ages 25 to 29 as a percentage of the total female population.
#> 17481 Male population between the ages 25 to 29.
#> 17482 Male population between the ages 25 to 29 as a percentage of the total male population.
#> 17483 Female population between the ages 30 to 34.
#> 17484 Female population between the ages 30 to 34 as a percentage of the total female population.
#> 17485 Male population between the ages 30 to 34.
#> 17486 Male population between the ages 30 to 34 as a percentage of the total male population.
#> 17487 Female population between the ages 35 to 39.
#> 17488 Female population between the ages 35 to 39 as a percentage of the total female population.
#> 17489 Male population between the ages 35 to 39.
#> 17490 Male population between the ages 35 to 39 as a percentage of the total male population.
#> 17491 Female population between the ages 40 to 44.
#> 17492 Female population between the ages 40 to 44 as a percentage of the total female population.
#> 17493 Male population between the ages 40 to 44.
#> 17494 Male population between the ages 40 to 44 as a percentage of the total male population.
#> 17495 Female population between the ages 45 to 49.
#> 17496 Female population between the ages 45 to 49 as a percentage of the total female population.
#> 17497 Male population between the ages 45 to 49.
#> 17498 Male population between the ages 45 to 49 as a percentage of the total male population.
#> 17499 Female population between the ages 50 to 54.
#> 17500 Female population between the ages 50 to 54 as a percentage of the total female population.
#> 17501 Male population between the ages 50 to 54.
#> 17502 Male population between the ages 50 to 54 as a percentage of the total male population.
#> 17503 Female population between the ages 55 to 59.
#> 17504 Female population between the ages 55 to 59 as a percentage of the total female population.
#> 17505 Male population between the ages 55 to 59.
#> 17506 Male population between the ages 55 to 59 as a percentage of the total male population.
#> 17507 Female population between the ages 60 to 64.
#> 17508 Female population between the ages 60 to 64 as a percentage of the total female population.
#> 17509 Male population between the ages 60 to 64.
#> 17510 Male population between the ages 60 to 64 as a percentage of the total male population.
#> 17511 Female population between the ages 65 to 69.
#> 17512 Female population between the ages 65 to 69 as a percentage of the total female population.
#> 17513 Male population between the ages 65 to 69.
#> 17514 Male population between the ages 65 to 69 as a percentage of the total male population.
#> 17515 Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17516 Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17517 Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17518 Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17520 Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17521 Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17522 Female population between the ages 70 to 74.
#> 17523 Female population between the ages 70 to 74 as a percentage of the total female population.
#> 17524 Male population between the ages 70 to 74.
#> 17525 Male population between the ages 70 to 74 as a percentage of the total male population.
#> 17526 Female population between the ages 75 to 79.
#> 17527 Female population between the ages 75 to 79 as a percentage of the total female population.
#> 17528 Male population between the ages 75 to 79.
#> 17529 Male population between the ages 75 to 79 as a percentage of the total male population.
#> 17530 Female population between the ages 80 and above.
#> 17531 Female population between the ages 80 and above as a percentage of the total female population.
#> 17532 Male population between the ages 80 and above.
#> 17533 Male population between the ages 80 and above as a percentage of the total male population.
#> 17534 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17535 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17536 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17537 Population, age 0, male refers to the male population at the specified age.
#> 17538 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17539 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17540 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17541 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17542 Population, age 1, male refers to the male population at the specified age.
#> 17543 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17544 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17545 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17546 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17547 Population, age 2, male refers to the male population at the specified age.
#> 17548 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17549 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17550 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17551 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17552 Population, age 3, male refers to the male population at the specified age.
#> 17553 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17554 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17555 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17556 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17557 Population, age 4, male refers to the male population at the specified age.
#> 17558 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17559 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17560 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17561 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17562 Population, age 5, male refers to the male population at the specified age.
#> 17563 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17564 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17565 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17566 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17567 Population, age 6, male refers to the male population at the specified age.
#> 17568 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17569 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17570 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17571 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17572 Population, age 7, male refers to the male population at the specified age.
#> 17573 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17574 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17575 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17576 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17577 Population, age 8, male refers to the male population at the specified age.
#> 17578 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17579 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17580 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17581 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17582 Population, age 9, male refers to the male population at the specified age.
#> 17583 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17584 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17585 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17586 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17587 Population, age 10, male refers to the male population at the specified age.
#> 17588 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17589 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17590 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17591 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17592 Population, age 11, male refers to the male population at the specified age.
#> 17593 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17594 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17595 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17596 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17597 Population, age 12, male refers to the male population at the specified age.
#> 17598 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17599 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17600 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17601 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17602 Population, age 13, male refers to the male population at the specified age.
#> 17603 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17604 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17605 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17606 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17607 Population, age 14, male refers to the male population at the specified age.
#> 17608 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17609 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17610 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17611 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17612 Population, age 15, male refers to the male population at the specified age.
#> 17613 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17614 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17615 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17616 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17617 Population, age 16, male refers to the male population at the specified age.
#> 17618 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17619 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17620 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17621 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17622 Population, age 17, male refers to the male population at the specified age.
#> 17623 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17624 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17625 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17626 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17627 Population, age 18, male refers to the male population at the specified age.
#> 17628 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17629 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17630 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17631 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17632 Population, age 19, male refers to the male population at the specified age.
#> 17633 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17634 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17635 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17636 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17637 Population, age 20, male refers to the male population at the specified age.
#> 17638 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17639 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17640 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17641 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17642 Population, age 21, male refers to the male population at the specified age.
#> 17643 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17644 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17645 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17646 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17647 Population, age 22, male refers to the male population at the specified age.
#> 17648 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17649 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17650 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17651 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17652 Population, age 23, male refers to the male population at the specified age.
#> 17653 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17654 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17655 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17656 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17657 Population, age 24, male refers to the male population at the specified age.
#> 17658 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17659 Age population, female refers to female population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17660 Age population, female refers to female population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17661 Age population, male refers to male population at the specified age level. The geographical areas included in the data are the same as the data source.
#> 17662 Population, age 25, male refers to the male population at the specified age.
#> 17663 Age population, total refers to total population at the specified age level, as estimated by the UNESCO Institute for Statistics.
#> 17665 Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.
#> 17666 Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.
#> 17667 Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.
#> 17668 Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17669
#> 17674 Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.
#> 17675 Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship.
#> 17676 Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17677
#> 17678
#> 17679 Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship.
#> 17680 Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 17681 Population Percentage of total is the share of first level administrative division (Admin 1 level) de facto mid-year population to total population.
#> 17682 Female population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration.
#> 17683 Population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration.
#> 17684 Male population of the age-group theoretically corresponding to pre-primary education as indicated by theoretical entrance age and duration.
#> 17685 Female population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration.
#> 17686 Male population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration.
#> 17687 Population of the age-group theoretically corresponding to the last grade of primary school as indicated by theoretical entrance age and duration.
#> 17688 Female population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration.
#> 17689 Population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration.
#> 17690 Male population of the age-group theoretically corresponding to primary education as indicated by theoretical entrance age and duration.
#> 17702 Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages.
#> 17703 Female rural population is the percentage of females who live in rural areas to total population.
#> 17704 Male rural population is the percentage males who live in rural areas to total population.
#> 17705 Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.
#> 17706 Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.
#> 17707 Female population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration.
#> 17708 Population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration.
#> 17709 Male population of the age-group theoretically corresponding to lower secondary education as indicated by theoretical entrance age and duration.
#> 17710 Female population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration.
#> 17711 Population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration.
#> 17712 Male population of the age-group theoretically corresponding to secondary education as indicated by theoretical entrance age and duration.
#> 17713 Female population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration.
#> 17714 Population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration.
#> 17715 Male population of the age-group theoretically corresponding to upper secondary education as indicated by theoretical entrance age and duration.
#> 17716 Female population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration.
#> 17717 Population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration.
#> 17718 Male population of the age-group theoretically corresponding to tertiary education as indicated by theoretical entrance age and duration.
#> 17719 Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.
#> 17720
#> 17721
#> 17722
#> 17723
#> 17724 Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.
#> 17725 Female urban population is the percentage of females who live in urban areas to total population.
#> 17726 Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.
#> 17727 Male urban population is the percentage of males who live in urban areas to total population.
#> 17728 Urban Area Refers to a village equivalent administrative area which satisfies certain criteria in terms of population density, percentage of agricultural households, and a number of urban facilities such as roads, formal education facilities, public health services, etc.
#> 17771 Population censuses collect data on the size, distribution and composition of population and information on a broad range of social and economic characteristics of the population. It also provides sampling frames for household and other surveys. Housing censuses provide information on the supply of housing units, the structural characteristics and facilities, and health and the development of normal family living conditions. Data obtained as part of the population census, including data on homeless persons, are often used in the presentation and analysis of the results of the housing census. It is recommended that population and housing censuses be conducted at least every 10 years.
#> 18453 The percentage of population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with completed primary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18454 The percentage of female population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed primary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18455 The percentage of male population (age 25 and over) with completed primary (ISCED 1) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed primary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18456 The percentage of population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18457 The percentage of female population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18458 The percentage of male population (age 25 and over) with at least completed primary education (ISCED 1 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18459
#> 18460 The percentage of population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18461 The percentage of female population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18462 The percentage of male population (age 25 and over) with completed lower secondary education (ISCED 2) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed lower secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18463 The percentage of population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed lower secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18464 The percentage of female population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed lower secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18465 The percentage of male population (age 25 and over) with at least completed lower secondary education (ISCED 2 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed lower secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18466
#> 18467 The percentage of population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18468 The percentage of female population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18469 The percentage of male population (age 25 and over) with completed upper secondary education (ISCED 3) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed upper secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18470 The percentage of population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed upper secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18471 The percentage of female population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed upper secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18472 The percentage of male population (age 25 and over) with at least completed upper secondary education (ISCED 3 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed upper secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18473
#> 18474 The percentage of population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18475 The percentage of female population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18476 The percentage of male population (age 25 and over) with completed post-secondary education (ISCED 4) as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed post-secondary education as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18477 The percentage of population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of persons aged 25 years and above with completed post-secondary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18478 The percentage of female population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of females aged 25 years and above who completed post-secondary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18479 The percentage of male population (age 25 and over) with at least completed post-secondary education (ISCED 4 or higher). This indicator is calculated by dividing the number of males aged 25 years and above who completed post-secondary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18480
#> 18481 The percentage of population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18482 The percentage of female population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18483 The percentage of male population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a short-cycle tertiary degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18484 The percentage of population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) or higher. This indicator is calculated by dividing the number of persons aged 25 years and above with a completed short-cycle tertiary degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18485 The percentage of female population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) or higher. This indicator is calculated by dividing the number of females aged 25 years and above who completed a short-cycle tertiary degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18486
#> 18487 The percentage of male population (age 25 and over) with a completed short-cycle tertiary degree (ISCED 5) or higher. This indicator is calculated by dividing the number of males aged 25 years and above who completed a short-cycle tertiary degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18488 The percentage of population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18489 The percentage of female population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18490 The percentage of male population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a bachelor's or equivalent degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18491 The percentage of population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) or higher. This indicator is calculated by dividing the number of persons aged 25 years and above with a completed bachelor's or equivalent degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18492 The percentage of female population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) or higher. This indicator is calculated by dividing the number of females aged 25 years and above who completed a bachelor's or equivalent degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18493
#> 18494 The percentage of male population (age 25 and over) with a completed bachelor's or equivalent degree (ISCED 6) or higher. This indicator is calculated by dividing the number of males aged 25 years and above who completed a bachelor's or equivalent degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18495 The percentage of population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18496 The percentage of female population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18497 The percentage of male population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) degree as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above who completed a master's or equivalent degree as the highest level of educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18498 The percentage of population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) or higher. This indicator is calculated by dividing the number of persons aged 25 years and above with a completed master's or equivalent degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18499 The percentage of female population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) or higher. This indicator is calculated by dividing the number of females aged 25 years and above who completed a master's or equivalent degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18500
#> 18501 The percentage of male population (age 25 and over) with a completed master's or equivalent degree (ISCED 7) or higher. This indicator is calculated by dividing the number of males aged 25 years and above who completed a master's or equivalent degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18502 The percentage of population (age 25 and over) with a completed doctoral or equivalent degree (ISCED 8). This indicator is calculated by dividing the number of persons aged 25 years and above with a completed doctoral or equivalent degree by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18503 The percentage of female population (age 25 and over) with a completed doctoral or equivalent degree (ISCED 8). This indicator is calculated by dividing the number of females aged 25 years and above who completed a doctoral or equivalent degree by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18504
#> 18505 The percentage of male population (age 25 and over) with a completed doctoral or equivalent degree (ISCED 8). This indicator is calculated by dividing the number of males aged 25 years and above who completed a doctoral or equivalent degree by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18506 Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18507 Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s female adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18508 Mean years of schooling (MYS) provides the average number of years of education (primary/ISCED 1 or higher) completed by a country’s male adult population (25 years and older), excluding years spent repeating grades. For further information and specific calculation methods, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18509 The percentage of the population (age 25 and over) with no education. This indicator is calculated by dividing the number of persons aged 25 years and above with no education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18510 The percentage of the female population (age 25 and over) with no education. This indicator is calculated by dividing the number of females aged 25 years and above with no education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18511 The percentage of the male population (age 25 and over) with no education. This indicator is calculated by dividing the number of males aged 25 years and above with no education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18512 The percentage of population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with incomplete primary education by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18513 The percentage of female population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above with incomplete primary education by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18514 The percentage of male population (age 25 and over) with incomplete primary as the highest level of educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above with incomplete primary education by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18515
#> 18516
#> 18517
#> 18518
#> 18519 The percentage of the population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of persons aged 25 years and above with unknown educational attainment by the total population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18520 The percentage of the female population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of females aged 25 years and above with unknown educational attainment by the total female population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18521 The percentage of the male population (age 25 and over) with unknown educational attainment. This indicator is calculated by dividing the number of males aged 25 years and above with unknown educational attainment by the total male population of the same age group and multiplying the result by 100. The UNESCO Institute for Statistics (UIS) educational attainment dataset shows the educational composition of the population aged 25 years and above and hence the stock and quality of human capital within a country. The dataset also reflects the structure and performance of the education system and its accumulated impact on human capital formation. For more information, visit the UNESCO Institute for Statistics website: http://www.uis.unesco.org/
#> 18776 Total number of adults from age 25 to age 64 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18777 Total number of females from age 25 to age 64 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18778 Total number of males from age 25 to age 64 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18779 Share of the adult illiterate population (age 25-64) that is female.
#> 18780 Total number of youth between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18781 Total number of females between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18782 Total number of males between age 15 and age 24 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18783 Total number of adults over age 15 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18784 Total number of females over age 15 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18785 Total number of males over age 15 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18786 Total number of adults over age 65 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18787 Total number of females over age 65 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18788 Total number of males over age 65 who cannot both read and write with understanding a short simple statement on their everyday life.
#> 18789 Share of the youth illiterate population that is female.
#> 18790 Share of the adult illiterate population (age 15+) that is female.
#> 18791 Share of the elderly illiterate population that is female.
#> 18792
#> 18793
#> 18794
#> 18795
#> 18796
#> 18797
#> 18798
#> 18799
#> 18800
#> 18801
#> 18802
#> 18803
#> 18804
#> 18805
#> 18806
#> 18807
#> 18808
#> 18809
#> 18810
#> 18811
#> 18812
#> 18813
#> 18814
#> 18815
#> 18816 Percentage of the population between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 25-64 years by the corresponding age group population and multiplying the result by 100.
#> 18817 Percentage of the female population between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 25-64 years by the corresponding age group population and multiplying the result by 100.
#> 18818
#> 18819
#> 18820
#> 18821 Percentage of the male population between age 25 and age 64 who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 25-64 years by the corresponding age group population and multiplying the result by 100.
#> 18822
#> 18823
#> 18824
#> 18825
#> 18826
#> 18827
#> 18828
#> 18829
#> 18830
#> 18831 Percentage of the population age 65 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of literates aged 65 years and over by the corresponding age group population and multiplying the result by 100.
#> 18832 Percentage of females age 65 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of female literates aged 65 years and over by the corresponding age group population and multiplying the result by 100.
#> 18833 Percentage of males age 65 and above who can, with understanding, read and write a short, simple statement on their everyday life. Generally, ‘literacy’ also encompasses ‘numeracy’, the ability to make simple arithmetic calculations. This indicator is calculated by dividing the number of male literates aged 65 years and over by the corresponding age group population and multiplying the result by 100.
#> 18834
#> 18835
#> 18836
#> 18837
#> 18838
#> 18839
#> 18840
#> 18841
#> 18842
#> 18843
#> 18844
#> 18845
#> 19358
#> 19359
#> 19360
#> 19876
#> 19877
#> 19878
#> 19879
#> 19880
#> 19881
#> 19882
#> 19883
#> 19884
#> 19885 Population of the age-group theoretically corresponding to the official entrance age to primary education. The official entrance age is the age at which students would enter a given programme or level of education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age.
#> 19886 Female population of the age-group theoretically corresponding to the official entrance age to primary education. The official entrance age is the age at which students would enter a given programme or level of education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age.
#> 19887 Male population of the age-group theoretically corresponding to the official entrance age to primary education. The official entrance age is the age at which students would enter a given programme or level of education assuming they start at the official entrance age for the lowest level of education, study full-time throughout and progressed through the system without repeating or skipping a grade. The theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age.
#> 19888 Population of the age-group theoretically corresponding to secondary general education as indicated by theoretical entrance age and duration.
#> 19889 Female population of the age-group theoretically corresponding to secondary general education as indicated by theoretical entrance age and duration.
#> 19890 Male population of the age-group theoretically corresponding to secondary general education as indicated by theoretical entrance age and duration.
#> 19891 Population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration.
#> 19892 Female population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration.
#> 19893 Male population of the age-group theoretically corresponding to post-secondary non-tertiary education as indicated by theoretical entrance age and duration.
#> 19894 Population of children within the age span that children are legally obliged to attend school.
#> 19895 Population of female children within the age span that children are legally obliged to attend school.
#> 19896 Population of male children within the age span that children are legally obliged to attend school.
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#> sourceDatabase
#> 25 Sustainable Energy for All
#> 40 Sustainable Energy for All
#> 41 Sustainable Energy for All
#> 165 Sustainable Energy for All
#> 199 Statistical Capacity Indicators
#> 1173 WDI Database Archives
#> 1176 WDI Database Archives
#> 1179 WDI Database Archives
#> 1197 Education Statistics
#> 1198 Education Statistics
#> 1199 Education Statistics
#> 1200 Education Statistics
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#> 1916 Global Public Procurement
#> 2024 Country Climate and Development Report (CCDR)
#> 2025 Country Climate and Development Report (CCDR)
#> 2026 Country Climate and Development Report (CCDR)
#> 2027 Country Climate and Development Report (CCDR)
#> 2029 Country Climate and Development Report (CCDR)
#> 2030 Country Climate and Development Report (CCDR)
#> 2031 Country Climate and Development Report (CCDR)
#> 2032 Country Climate and Development Report (CCDR)
#> 2033 Country Climate and Development Report (CCDR)
#> 2034 Country Climate and Development Report (CCDR)
#> 2035 Country Climate and Development Report (CCDR)
#> 2036 Country Climate and Development Report (CCDR)
#> 2037 Country Climate and Development Report (CCDR)
#> 2038 Country Climate and Development Report (CCDR)
#> 2039 Country Climate and Development Report (CCDR)
#> 2040 Country Climate and Development Report (CCDR)
#> 2041 Country Climate and Development Report (CCDR)
#> 2042 Country Climate and Development Report (CCDR)
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#> 2046 Country Climate and Development Report (CCDR)
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#> 2048 Country Climate and Development Report (CCDR)
#> 2049 Country Climate and Development Report (CCDR)
#> 2050 Country Climate and Development Report (CCDR)
#> 2236 Country Climate and Development Report (CCDR)
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#> 2272 Country Climate and Development Report (CCDR)
#> 2358 Country Climate and Development Report (CCDR)
#> 2359 Country Climate and Development Report (CCDR)
#> 2361 Country Climate and Development Report (CCDR)
#> 2362 Country Climate and Development Report (CCDR)
#> 2363 Country Climate and Development Report (CCDR)
#> 2411 Food Prices for Nutrition
#> 2423 Food Prices for Nutrition
#> 2439 Food Prices for Nutrition
#> 5429 Africa Development Indicators
#> 5966 World Development Indicators
#> 5967 World Development Indicators
#> 5968 World Development Indicators
#> 5971 World Development Indicators
#> 5972 World Development Indicators
#> 5973 World Development Indicators
#> 5999 WDI Database Archives
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#> 6013 Africa Development Indicators
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#> 6015 Africa Development Indicators
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#> 6064 World Development Indicators
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#> 6075 World Development Indicators
#> 6103 Africa Development Indicators
#> 6104 World Development Indicators
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#> 6113 Africa Development Indicators
#> 6114 WDI Database Archives
#> 6120 World Development Indicators
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#> 7945 Health Equity and Financial Protection Indicators
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#> 7947 Health Equity and Financial Protection Indicators
#> 7948 Health Equity and Financial Protection Indicators
#> 7949 Health Equity and Financial Protection Indicators
#> 7950 Health Equity and Financial Protection Indicators
#> 7951 Health Equity and Financial Protection Indicators
#> 7952 Health Equity and Financial Protection Indicators
#> 7953 Health Equity and Financial Protection Indicators
#> 7954 Health Equity and Financial Protection Indicators
#> 7955 Health Equity and Financial Protection Indicators
#> 7956 Health Equity and Financial Protection Indicators
#> 7993 Health Equity and Financial Protection Indicators
#> 7994 Health Equity and Financial Protection Indicators
#> 7995 Health Equity and Financial Protection Indicators
#> 7996 Health Equity and Financial Protection Indicators
#> 7997 Health Equity and Financial Protection Indicators
#> 7998 Health Equity and Financial Protection Indicators
#> 8011 Health Equity and Financial Protection Indicators
#> 8012 Health Equity and Financial Protection Indicators
#> 8013 Health Equity and Financial Protection Indicators
#> 8014 Health Equity and Financial Protection Indicators
#> 8015 Health Equity and Financial Protection Indicators
#> 8016 Health Equity and Financial Protection Indicators
#> 8041 Health Equity and Financial Protection Indicators
#> 8042 Health Equity and Financial Protection Indicators
#> 8043 Health Equity and Financial Protection Indicators
#> 8044 Health Equity and Financial Protection Indicators
#> 8045 Health Equity and Financial Protection Indicators
#> 8046 Health Equity and Financial Protection Indicators
#> 8047 Health Equity and Financial Protection Indicators
#> 8048 Health Equity and Financial Protection Indicators
#> 8049 Health Equity and Financial Protection Indicators
#> 8050 Health Equity and Financial Protection Indicators
#> 8051 Health Equity and Financial Protection Indicators
#> 8052 Health Equity and Financial Protection Indicators
#> 8053 Health Equity and Financial Protection Indicators
#> 8054 Health Equity and Financial Protection Indicators
#> 8055 Health Equity and Financial Protection Indicators
#> 8056 Health Equity and Financial Protection Indicators
#> 8057 Health Equity and Financial Protection Indicators
#> 8058 Health Equity and Financial Protection Indicators
#> 8059 Health Equity and Financial Protection Indicators
#> 8060 Health Equity and Financial Protection Indicators
#> 8061 Health Equity and Financial Protection Indicators
#> 8062 Health Equity and Financial Protection Indicators
#> 8063 Health Equity and Financial Protection Indicators
#> 8064 Health Equity and Financial Protection Indicators
#> 8065 Health Equity and Financial Protection Indicators
#> 8066 Health Equity and Financial Protection Indicators
#> 8067 Health Equity and Financial Protection Indicators
#> 8068 Health Equity and Financial Protection Indicators
#> 8069 Health Equity and Financial Protection Indicators
#> 8070 Health Equity and Financial Protection Indicators
#> 8077 Health Equity and Financial Protection Indicators
#> 8078 Health Equity and Financial Protection Indicators
#> 8079 Health Equity and Financial Protection Indicators
#> 8080 Health Equity and Financial Protection Indicators
#> 8081 Health Equity and Financial Protection Indicators
#> 8082 Health Equity and Financial Protection Indicators
#> 8083 Health Equity and Financial Protection Indicators
#> 8084 Health Equity and Financial Protection Indicators
#> 8085 Health Equity and Financial Protection Indicators
#> 8086 Health Equity and Financial Protection Indicators
#> 8087 Health Equity and Financial Protection Indicators
#> 8088 Health Equity and Financial Protection Indicators
#> 8089 Health Equity and Financial Protection Indicators
#> 8090 Health Equity and Financial Protection Indicators
#> 8091 Health Equity and Financial Protection Indicators
#> 8092 Health Equity and Financial Protection Indicators
#> 8117 Health Equity and Financial Protection Indicators
#> 8118 Health Equity and Financial Protection Indicators
#> 8119 Health Equity and Financial Protection Indicators
#> 8120 Health Equity and Financial Protection Indicators
#> 8121 Health Equity and Financial Protection Indicators
#> 8122 Health Equity and Financial Protection Indicators
#> 8135 Health Equity and Financial Protection Indicators
#> 8136 Health Equity and Financial Protection Indicators
#> 8137 Health Equity and Financial Protection Indicators
#> 8138 Health Equity and Financial Protection Indicators
#> 8139 Health Equity and Financial Protection Indicators
#> 8140 Health Equity and Financial Protection Indicators
#> 8141 Health Equity and Financial Protection Indicators
#> 8142 Health Equity and Financial Protection Indicators
#> 8143 Health Equity and Financial Protection Indicators
#> 8144 Health Equity and Financial Protection Indicators
#> 8145 Health Equity and Financial Protection Indicators
#> 8146 Health Equity and Financial Protection Indicators
#> 8147 Health Equity and Financial Protection Indicators
#> 8148 Health Equity and Financial Protection Indicators
#> 8149 Health Equity and Financial Protection Indicators
#> 8150 Health Equity and Financial Protection Indicators
#> 8151 Health Equity and Financial Protection Indicators
#> 8152 Health Equity and Financial Protection Indicators
#> 8153 Health Equity and Financial Protection Indicators
#> 8154 Health Equity and Financial Protection Indicators
#> 8155 Health Equity and Financial Protection Indicators
#> 8156 Health Equity and Financial Protection Indicators
#> 8157 Health Equity and Financial Protection Indicators
#> 8158 Health Equity and Financial Protection Indicators
#> 8165 Health Equity and Financial Protection Indicators
#> 8166 Health Equity and Financial Protection Indicators
#> 8167 Health Equity and Financial Protection Indicators
#> 8168 Health Equity and Financial Protection Indicators
#> 8169 Health Equity and Financial Protection Indicators
#> 8170 Health Equity and Financial Protection Indicators
#> 8171 Health Equity and Financial Protection Indicators
#> 8172 Health Equity and Financial Protection Indicators
#> 8173 Health Equity and Financial Protection Indicators
#> 8174 Health Equity and Financial Protection Indicators
#> 8175 Health Equity and Financial Protection Indicators
#> 8176 Health Equity and Financial Protection Indicators
#> 8177 Health Equity and Financial Protection Indicators
#> 8178 Health Equity and Financial Protection Indicators
#> 8179 Health Equity and Financial Protection Indicators
#> 8180 Health Equity and Financial Protection Indicators
#> 8181 Health Equity and Financial Protection Indicators
#> 8182 Health Equity and Financial Protection Indicators
#> 8183 Health Equity and Financial Protection Indicators
#> 8184 Health Equity and Financial Protection Indicators
#> 8185 Health Equity and Financial Protection Indicators
#> 8186 Health Equity and Financial Protection Indicators
#> 8187 Health Equity and Financial Protection Indicators
#> 8188 Health Equity and Financial Protection Indicators
#> 8195 Health Equity and Financial Protection Indicators
#> 8196 Health Equity and Financial Protection Indicators
#> 8197 Health Equity and Financial Protection Indicators
#> 8198 Health Equity and Financial Protection Indicators
#> 8199 Health Equity and Financial Protection Indicators
#> 8200 Health Equity and Financial Protection Indicators
#> 8202 Health Equity and Financial Protection Indicators
#> 8203 Health Equity and Financial Protection Indicators
#> 8204 Health Equity and Financial Protection Indicators
#> 8205 Health Equity and Financial Protection Indicators
#> 8206 Health Equity and Financial Protection Indicators
#> 8207 Health Equity and Financial Protection Indicators
#> 8209 Health Equity and Financial Protection Indicators
#> 8210 Health Equity and Financial Protection Indicators
#> 8211 Health Equity and Financial Protection Indicators
#> 8212 Health Equity and Financial Protection Indicators
#> 8213 Health Equity and Financial Protection Indicators
#> 8214 Health Equity and Financial Protection Indicators
#> 8216 Health Equity and Financial Protection Indicators
#> 8217 Health Equity and Financial Protection Indicators
#> 8218 Health Equity and Financial Protection Indicators
#> 8219 Health Equity and Financial Protection Indicators
#> 8220 Health Equity and Financial Protection Indicators
#> 8221 Health Equity and Financial Protection Indicators
#> 8223 Health Equity and Financial Protection Indicators
#> 8224 Health Equity and Financial Protection Indicators
#> 8225 Health Equity and Financial Protection Indicators
#> 8226 Health Equity and Financial Protection Indicators
#> 8227 Health Equity and Financial Protection Indicators
#> 8228 Health Equity and Financial Protection Indicators
#> 8229 Health Equity and Financial Protection Indicators
#> 8230 Health Equity and Financial Protection Indicators
#> 8231 Health Equity and Financial Protection Indicators
#> 8232 Health Equity and Financial Protection Indicators
#> 8233 Health Equity and Financial Protection Indicators
#> 8234 Health Equity and Financial Protection Indicators
#> 8242 Health Equity and Financial Protection Indicators
#> 8243 Health Equity and Financial Protection Indicators
#> 8244 Health Equity and Financial Protection Indicators
#> 8245 Health Equity and Financial Protection Indicators
#> 8246 Health Equity and Financial Protection Indicators
#> 8247 Health Equity and Financial Protection Indicators
#> 8248 Health Equity and Financial Protection Indicators
#> 8249 Health Equity and Financial Protection Indicators
#> 8250 Health Equity and Financial Protection Indicators
#> 8251 Health Equity and Financial Protection Indicators
#> 8252 Health Equity and Financial Protection Indicators
#> 8253 Health Equity and Financial Protection Indicators
#> 8708 Country Partnership Strategy for India (FY2013 - 17)
#> 8709 Country Partnership Strategy for India (FY2013 - 17)
#> 8710 Country Partnership Strategy for India (FY2013 - 17)
#> 8711 Country Partnership Strategy for India (FY2013 - 17)
#> 8712 Country Partnership Strategy for India (FY2013 - 17)
#> 8713 Country Partnership Strategy for India (FY2013 - 17)
#> 8714 Country Partnership Strategy for India (FY2013 - 17)
#> 8775 Country Partnership Strategy for India (FY2013 - 17)
#> 8779 Country Partnership Strategy for India (FY2013 - 17)
#> 8781 Country Partnership Strategy for India (FY2013 - 17)
#> 8783 Country Partnership Strategy for India (FY2013 - 17)
#> 8863 Country Partnership Strategy for India (FY2013 - 17)
#> 8864 Country Partnership Strategy for India (FY2013 - 17)
#> 8865 Country Partnership Strategy for India (FY2013 - 17)
#> 8880 Country Partnership Strategy for India (FY2013 - 17)
#> 8882 Country Partnership Strategy for India (FY2013 - 17)
#> 8972 WDI Database Archives
#> 9027 Africa Development Indicators
#> 9052 World Development Indicators
#> 9176 Global Jobs Indicators Database (JOIN)
#> 9177 Global Jobs Indicators Database (JOIN)
#> 9178 Global Jobs Indicators Database (JOIN)
#> 9179 Global Jobs Indicators Database (JOIN)
#> 9180 Global Jobs Indicators Database (JOIN)
#> 9181 Global Jobs Indicators Database (JOIN)
#> 9182 Global Jobs Indicators Database (JOIN)
#> 9183 Global Jobs Indicators Database (JOIN)
#> 9184 Global Jobs Indicators Database (JOIN)
#> 9185 Global Jobs Indicators Database (JOIN)
#> 9186 Global Jobs Indicators Database (JOIN)
#> 9187 Global Jobs Indicators Database (JOIN)
#> 9188 Global Jobs Indicators Database (JOIN)
#> 9189 Global Jobs Indicators Database (JOIN)
#> 9190 Global Jobs Indicators Database (JOIN)
#> 9191 Global Jobs Indicators Database (JOIN)
#> 9192 Global Jobs Indicators Database (JOIN)
#> 9193 Global Jobs Indicators Database (JOIN)
#> 9194 Global Jobs Indicators Database (JOIN)
#> 9195 Global Jobs Indicators Database (JOIN)
#> 9196 Global Jobs Indicators Database (JOIN)
#> 9197 Global Jobs Indicators Database (JOIN)
#> 9198 Global Jobs Indicators Database (JOIN)
#> 9199 Global Jobs Indicators Database (JOIN)
#> 9200 Global Jobs Indicators Database (JOIN)
#> 9201 Global Jobs Indicators Database (JOIN)
#> 9202 Global Jobs Indicators Database (JOIN)
#> 9203 Global Jobs Indicators Database (JOIN)
#> 9204 Global Jobs Indicators Database (JOIN)
#> 9205 Global Jobs Indicators Database (JOIN)
#> 9206 Global Jobs Indicators Database (JOIN)
#> 9207 Global Jobs Indicators Database (JOIN)
#> 9208 Global Jobs Indicators Database (JOIN)
#> 9209 Global Jobs Indicators Database (JOIN)
#> 9210 Global Jobs Indicators Database (JOIN)
#> 9211 Global Jobs Indicators Database (JOIN)
#> 9212 Global Jobs Indicators Database (JOIN)
#> 9213 Global Jobs Indicators Database (JOIN)
#> 9214 Global Jobs Indicators Database (JOIN)
#> 9215 Global Jobs Indicators Database (JOIN)
#> 9216 Global Jobs Indicators Database (JOIN)
#> 9217 Global Jobs Indicators Database (JOIN)
#> 9218 Global Jobs Indicators Database (JOIN)
#> 9219 Global Jobs Indicators Database (JOIN)
#> 9220 Global Jobs Indicators Database (JOIN)
#> 9221 Global Jobs Indicators Database (JOIN)
#> 9222 Global Jobs Indicators Database (JOIN)
#> 9223 Global Jobs Indicators Database (JOIN)
#> 9224 Global Jobs Indicators Database (JOIN)
#> 9225 Global Jobs Indicators Database (JOIN)
#> 9226 Global Jobs Indicators Database (JOIN)
#> 9227 Global Jobs Indicators Database (JOIN)
#> 9228 Global Jobs Indicators Database (JOIN)
#> 9229 Global Jobs Indicators Database (JOIN)
#> 9230 Global Jobs Indicators Database (JOIN)
#> 9231 Global Jobs Indicators Database (JOIN)
#> 9232 Global Jobs Indicators Database (JOIN)
#> 9233 Global Jobs Indicators Database (JOIN)
#> 9234 Global Jobs Indicators Database (JOIN)
#> 9235 Global Jobs Indicators Database (JOIN)
#> 9236 Global Jobs Indicators Database (JOIN)
#> 9237 Global Jobs Indicators Database (JOIN)
#> 9238 Global Jobs Indicators Database (JOIN)
#> 9239 Global Jobs Indicators Database (JOIN)
#> 9240 Global Jobs Indicators Database (JOIN)
#> 9241 Global Jobs Indicators Database (JOIN)
#> 9242 Global Jobs Indicators Database (JOIN)
#> 9243 Global Jobs Indicators Database (JOIN)
#> 9244 Global Jobs Indicators Database (JOIN)
#> 9245 Global Jobs Indicators Database (JOIN)
#> 9246 Global Jobs Indicators Database (JOIN)
#> 9247 Global Jobs Indicators Database (JOIN)
#> 9248 Global Jobs Indicators Database (JOIN)
#> 9249 Global Jobs Indicators Database (JOIN)
#> 9250 Global Jobs Indicators Database (JOIN)
#> 9251 Global Jobs Indicators Database (JOIN)
#> 9252 Global Jobs Indicators Database (JOIN)
#> 9253 Global Jobs Indicators Database (JOIN)
#> 9254 Global Jobs Indicators Database (JOIN)
#> 9255 Global Jobs Indicators Database (JOIN)
#> 9256 Global Jobs Indicators Database (JOIN)
#> 9257 Global Jobs Indicators Database (JOIN)
#> 9258 Global Jobs Indicators Database (JOIN)
#> 9259 Global Jobs Indicators Database (JOIN)
#> 9260 Global Jobs Indicators Database (JOIN)
#> 9261 Global Jobs Indicators Database (JOIN)
#> 9262 Global Jobs Indicators Database (JOIN)
#> 9263 Global Jobs Indicators Database (JOIN)
#> 9264 Global Jobs Indicators Database (JOIN)
#> 9265 Global Jobs Indicators Database (JOIN)
#> 9266 Global Jobs Indicators Database (JOIN)
#> 9267 Global Jobs Indicators Database (JOIN)
#> 9268 Global Jobs Indicators Database (JOIN)
#> 9269 Global Jobs Indicators Database (JOIN)
#> 9270 Global Jobs Indicators Database (JOIN)
#> 9271 Global Jobs Indicators Database (JOIN)
#> 9272 Global Jobs Indicators Database (JOIN)
#> 9273 Global Jobs Indicators Database (JOIN)
#> 9274 Global Jobs Indicators Database (JOIN)
#> 9275 Global Jobs Indicators Database (JOIN)
#> 9276 Global Jobs Indicators Database (JOIN)
#> 9277 Global Jobs Indicators Database (JOIN)
#> 9278 Global Jobs Indicators Database (JOIN)
#> 9279 Global Jobs Indicators Database (JOIN)
#> 9280 Global Jobs Indicators Database (JOIN)
#> 9281 Global Jobs Indicators Database (JOIN)
#> 9282 Global Jobs Indicators Database (JOIN)
#> 9283 Global Jobs Indicators Database (JOIN)
#> 9284 Global Jobs Indicators Database (JOIN)
#> 9285 Global Jobs Indicators Database (JOIN)
#> 9286 Global Jobs Indicators Database (JOIN)
#> 9287 Global Jobs Indicators Database (JOIN)
#> 9288 Global Jobs Indicators Database (JOIN)
#> 9289 Global Jobs Indicators Database (JOIN)
#> 9290 Global Jobs Indicators Database (JOIN)
#> 9291 Global Jobs Indicators Database (JOIN)
#> 9292 Global Jobs Indicators Database (JOIN)
#> 9293 Global Jobs Indicators Database (JOIN)
#> 9294 Global Jobs Indicators Database (JOIN)
#> 9295 Global Jobs Indicators Database (JOIN)
#> 9296 Global Jobs Indicators Database (JOIN)
#> 9297 Global Jobs Indicators Database (JOIN)
#> 9298 Global Jobs Indicators Database (JOIN)
#> 9299 Global Jobs Indicators Database (JOIN)
#> 9300 Global Jobs Indicators Database (JOIN)
#> 9301 Global Jobs Indicators Database (JOIN)
#> 9302 Global Jobs Indicators Database (JOIN)
#> 9303 Global Jobs Indicators Database (JOIN)
#> 9304 Global Jobs Indicators Database (JOIN)
#> 9305 Global Jobs Indicators Database (JOIN)
#> 9306 Global Jobs Indicators Database (JOIN)
#> 9307 Global Jobs Indicators Database (JOIN)
#> 9308 Global Jobs Indicators Database (JOIN)
#> 9309 Global Jobs Indicators Database (JOIN)
#> 9310 Global Jobs Indicators Database (JOIN)
#> 9311 Global Jobs Indicators Database (JOIN)
#> 9312 Global Jobs Indicators Database (JOIN)
#> 9313 Global Jobs Indicators Database (JOIN)
#> 9314 Global Jobs Indicators Database (JOIN)
#> 9315 Global Jobs Indicators Database (JOIN)
#> 9316 Global Jobs Indicators Database (JOIN)
#> 9317 Global Jobs Indicators Database (JOIN)
#> 9318 Global Jobs Indicators Database (JOIN)
#> 9319 Global Jobs Indicators Database (JOIN)
#> 9320 Global Jobs Indicators Database (JOIN)
#> 9321 Global Jobs Indicators Database (JOIN)
#> 9322 Global Jobs Indicators Database (JOIN)
#> 9323 Global Jobs Indicators Database (JOIN)
#> 9324 Global Jobs Indicators Database (JOIN)
#> 9325 Global Jobs Indicators Database (JOIN)
#> 9326 Global Jobs Indicators Database (JOIN)
#> 9327 Global Jobs Indicators Database (JOIN)
#> 9328 Global Jobs Indicators Database (JOIN)
#> 9329 Global Jobs Indicators Database (JOIN)
#> 9330 Global Jobs Indicators Database (JOIN)
#> 9331 Global Jobs Indicators Database (JOIN)
#> 9332 Global Jobs Indicators Database (JOIN)
#> 9333 Global Jobs Indicators Database (JOIN)
#> 9334 Global Jobs Indicators Database (JOIN)
#> 9335 Global Jobs Indicators Database (JOIN)
#> 9336 Global Jobs Indicators Database (JOIN)
#> 9337 Global Jobs Indicators Database (JOIN)
#> 9338 Global Jobs Indicators Database (JOIN)
#> 9339 Global Jobs Indicators Database (JOIN)
#> 9340 Global Jobs Indicators Database (JOIN)
#> 9341 Global Jobs Indicators Database (JOIN)
#> 9342 Global Jobs Indicators Database (JOIN)
#> 9343 Global Jobs Indicators Database (JOIN)
#> 9344 Global Jobs Indicators Database (JOIN)
#> 9352 Global Jobs Indicators Database (JOIN)
#> 9353 Global Jobs Indicators Database (JOIN)
#> 9354 Global Jobs Indicators Database (JOIN)
#> 9355 Global Jobs Indicators Database (JOIN)
#> 9356 Global Jobs Indicators Database (JOIN)
#> 9357 Global Jobs Indicators Database (JOIN)
#> 9358 Global Jobs Indicators Database (JOIN)
#> 9359 Global Jobs Indicators Database (JOIN)
#> 9360 Global Jobs Indicators Database (JOIN)
#> 9361 Global Jobs Indicators Database (JOIN)
#> 9362 Global Jobs Indicators Database (JOIN)
#> 9363 Global Jobs Indicators Database (JOIN)
#> 9364 Global Jobs Indicators Database (JOIN)
#> 9365 Global Jobs Indicators Database (JOIN)
#> 9366 Global Jobs Indicators Database (JOIN)
#> 9367 Global Jobs Indicators Database (JOIN)
#> 9368 Global Jobs Indicators Database (JOIN)
#> 9369 Global Jobs Indicators Database (JOIN)
#> 9370 Global Jobs Indicators Database (JOIN)
#> 9371 Global Jobs Indicators Database (JOIN)
#> 9372 Global Jobs Indicators Database (JOIN)
#> 9373 Global Jobs Indicators Database (JOIN)
#> 9374 Global Jobs Indicators Database (JOIN)
#> 9375 Global Jobs Indicators Database (JOIN)
#> 9376 Global Jobs Indicators Database (JOIN)
#> 9377 Global Jobs Indicators Database (JOIN)
#> 9378 Global Jobs Indicators Database (JOIN)
#> 9379 Global Jobs Indicators Database (JOIN)
#> 9380 Global Jobs Indicators Database (JOIN)
#> 9381 Global Jobs Indicators Database (JOIN)
#> 9382 Global Jobs Indicators Database (JOIN)
#> 9383 Global Jobs Indicators Database (JOIN)
#> 9384 Global Jobs Indicators Database (JOIN)
#> 9385 Global Jobs Indicators Database (JOIN)
#> 9386 Global Jobs Indicators Database (JOIN)
#> 9387 Global Jobs Indicators Database (JOIN)
#> 9388 Global Jobs Indicators Database (JOIN)
#> 9389 Global Jobs Indicators Database (JOIN)
#> 9390 Global Jobs Indicators Database (JOIN)
#> 9391 Global Jobs Indicators Database (JOIN)
#> 9392 Global Jobs Indicators Database (JOIN)
#> 9393 Global Jobs Indicators Database (JOIN)
#> 9394 Global Jobs Indicators Database (JOIN)
#> 9395 Global Jobs Indicators Database (JOIN)
#> 9396 Global Jobs Indicators Database (JOIN)
#> 9397 Global Jobs Indicators Database (JOIN)
#> 9398 Global Jobs Indicators Database (JOIN)
#> 9399 Global Jobs Indicators Database (JOIN)
#> 9400 Global Jobs Indicators Database (JOIN)
#> 9401 Global Jobs Indicators Database (JOIN)
#> 9402 Global Jobs Indicators Database (JOIN)
#> 9403 Global Jobs Indicators Database (JOIN)
#> 9404 Global Jobs Indicators Database (JOIN)
#> 9405 Global Jobs Indicators Database (JOIN)
#> 9406 Global Jobs Indicators Database (JOIN)
#> 9407 Global Jobs Indicators Database (JOIN)
#> 9408 Global Jobs Indicators Database (JOIN)
#> 9409 Global Jobs Indicators Database (JOIN)
#> 9410 Global Jobs Indicators Database (JOIN)
#> 9411 Global Jobs Indicators Database (JOIN)
#> 9412 Global Jobs Indicators Database (JOIN)
#> 9413 Global Jobs Indicators Database (JOIN)
#> 9414 Global Jobs Indicators Database (JOIN)
#> 9415 Global Jobs Indicators Database (JOIN)
#> 9416 Global Jobs Indicators Database (JOIN)
#> 9417 Global Jobs Indicators Database (JOIN)
#> 9418 Global Jobs Indicators Database (JOIN)
#> 9419 Global Jobs Indicators Database (JOIN)
#> 9420 Global Jobs Indicators Database (JOIN)
#> 9421 Global Jobs Indicators Database (JOIN)
#> 9422 Global Jobs Indicators Database (JOIN)
#> 9423 Global Jobs Indicators Database (JOIN)
#> 9424 Global Jobs Indicators Database (JOIN)
#> 9425 Global Jobs Indicators Database (JOIN)
#> 9426 Global Jobs Indicators Database (JOIN)
#> 9427 Global Jobs Indicators Database (JOIN)
#> 9428 Global Jobs Indicators Database (JOIN)
#> 9429 Global Jobs Indicators Database (JOIN)
#> 9430 Global Jobs Indicators Database (JOIN)
#> 9431 Global Jobs Indicators Database (JOIN)
#> 9432 Global Jobs Indicators Database (JOIN)
#> 9433 Global Jobs Indicators Database (JOIN)
#> 9434 Global Jobs Indicators Database (JOIN)
#> 9435 Global Jobs Indicators Database (JOIN)
#> 9436 Global Jobs Indicators Database (JOIN)
#> 9437 Global Jobs Indicators Database (JOIN)
#> 9438 Global Jobs Indicators Database (JOIN)
#> 9439 Global Jobs Indicators Database (JOIN)
#> 9440 Global Jobs Indicators Database (JOIN)
#> 9441 Global Jobs Indicators Database (JOIN)
#> 9442 Global Jobs Indicators Database (JOIN)
#> 9443 Global Jobs Indicators Database (JOIN)
#> 9444 Global Jobs Indicators Database (JOIN)
#> 9445 Global Jobs Indicators Database (JOIN)
#> 9446 Global Jobs Indicators Database (JOIN)
#> 9447 Global Jobs Indicators Database (JOIN)
#> 9448 Global Jobs Indicators Database (JOIN)
#> 9449 Global Jobs Indicators Database (JOIN)
#> 9450 Global Jobs Indicators Database (JOIN)
#> 9451 Global Jobs Indicators Database (JOIN)
#> 9452 Global Jobs Indicators Database (JOIN)
#> 9453 Global Jobs Indicators Database (JOIN)
#> 9454 Global Jobs Indicators Database (JOIN)
#> 9455 Global Jobs Indicators Database (JOIN)
#> 9456 Global Jobs Indicators Database (JOIN)
#> 9457 Global Jobs Indicators Database (JOIN)
#> 9458 Global Jobs Indicators Database (JOIN)
#> 9459 Global Jobs Indicators Database (JOIN)
#> 9460 Global Jobs Indicators Database (JOIN)
#> 9461 Global Jobs Indicators Database (JOIN)
#> 9462 Global Jobs Indicators Database (JOIN)
#> 9463 Global Jobs Indicators Database (JOIN)
#> 9464 Global Jobs Indicators Database (JOIN)
#> 9465 Global Jobs Indicators Database (JOIN)
#> 9466 Global Jobs Indicators Database (JOIN)
#> 9467 Global Jobs Indicators Database (JOIN)
#> 9468 Global Jobs Indicators Database (JOIN)
#> 9469 Global Jobs Indicators Database (JOIN)
#> 9470 Global Jobs Indicators Database (JOIN)
#> 9471 Global Jobs Indicators Database (JOIN)
#> 9472 Global Jobs Indicators Database (JOIN)
#> 9473 Global Jobs Indicators Database (JOIN)
#> 9474 Global Jobs Indicators Database (JOIN)
#> 9475 Global Jobs Indicators Database (JOIN)
#> 9476 Global Jobs Indicators Database (JOIN)
#> 9477 Global Jobs Indicators Database (JOIN)
#> 9478 Global Jobs Indicators Database (JOIN)
#> 9479 Global Jobs Indicators Database (JOIN)
#> 9480 Global Jobs Indicators Database (JOIN)
#> 9481 Global Jobs Indicators Database (JOIN)
#> 9482 Global Jobs Indicators Database (JOIN)
#> 9483 Global Jobs Indicators Database (JOIN)
#> 9484 Global Jobs Indicators Database (JOIN)
#> 9485 Global Jobs Indicators Database (JOIN)
#> 9486 Global Jobs Indicators Database (JOIN)
#> 9487 Global Jobs Indicators Database (JOIN)
#> 9488 Global Jobs Indicators Database (JOIN)
#> 9489 Global Jobs Indicators Database (JOIN)
#> 9490 Global Jobs Indicators Database (JOIN)
#> 9491 Global Jobs Indicators Database (JOIN)
#> 9492 Global Jobs Indicators Database (JOIN)
#> 9493 Global Jobs Indicators Database (JOIN)
#> 9494 Global Jobs Indicators Database (JOIN)
#> 9495 Global Jobs Indicators Database (JOIN)
#> 9496 Global Jobs Indicators Database (JOIN)
#> 9497 Global Jobs Indicators Database (JOIN)
#> 9498 Global Jobs Indicators Database (JOIN)
#> 9499 Global Jobs Indicators Database (JOIN)
#> 9500 Global Jobs Indicators Database (JOIN)
#> 9501 Global Jobs Indicators Database (JOIN)
#> 9502 Global Jobs Indicators Database (JOIN)
#> 9503 Global Jobs Indicators Database (JOIN)
#> 9504 Global Jobs Indicators Database (JOIN)
#> 9512 Global Jobs Indicators Database (JOIN)
#> 9513 Global Jobs Indicators Database (JOIN)
#> 9514 Global Jobs Indicators Database (JOIN)
#> 9515 Global Jobs Indicators Database (JOIN)
#> 9516 Global Jobs Indicators Database (JOIN)
#> 9517 Global Jobs Indicators Database (JOIN)
#> 9518 Global Jobs Indicators Database (JOIN)
#> 9519 Global Jobs Indicators Database (JOIN)
#> 9520 Global Jobs Indicators Database (JOIN)
#> 9521 Global Jobs Indicators Database (JOIN)
#> 9522 Global Jobs Indicators Database (JOIN)
#> 9523 Global Jobs Indicators Database (JOIN)
#> 9524 Global Jobs Indicators Database (JOIN)
#> 9525 Global Jobs Indicators Database (JOIN)
#> 9526 Global Jobs Indicators Database (JOIN)
#> 9527 Global Jobs Indicators Database (JOIN)
#> 9528 Global Jobs Indicators Database (JOIN)
#> 9529 Global Jobs Indicators Database (JOIN)
#> 9530 Global Jobs Indicators Database (JOIN)
#> 9531 Global Jobs Indicators Database (JOIN)
#> 9532 Global Jobs Indicators Database (JOIN)
#> 9533 Global Jobs Indicators Database (JOIN)
#> 9534 Global Jobs Indicators Database (JOIN)
#> 9535 Global Jobs Indicators Database (JOIN)
#> 9536 Global Jobs Indicators Database (JOIN)
#> 9537 Global Jobs Indicators Database (JOIN)
#> 9565 Global Jobs Indicators Database (JOIN)
#> 9566 Global Jobs Indicators Database (JOIN)
#> 9567 Global Jobs Indicators Database (JOIN)
#> 9568 Global Jobs Indicators Database (JOIN)
#> 9569 Global Jobs Indicators Database (JOIN)
#> 9570 Global Jobs Indicators Database (JOIN)
#> 9571 Global Jobs Indicators Database (JOIN)
#> 9572 Global Jobs Indicators Database (JOIN)
#> 9573 Global Jobs Indicators Database (JOIN)
#> 9574 Global Jobs Indicators Database (JOIN)
#> 9575 Global Jobs Indicators Database (JOIN)
#> 9576 Global Jobs Indicators Database (JOIN)
#> 9577 Global Jobs Indicators Database (JOIN)
#> 9578 Global Jobs Indicators Database (JOIN)
#> 9579 Global Jobs Indicators Database (JOIN)
#> 9580 Global Jobs Indicators Database (JOIN)
#> 9581 Global Jobs Indicators Database (JOIN)
#> 9582 Global Jobs Indicators Database (JOIN)
#> 9583 Global Jobs Indicators Database (JOIN)
#> 9584 Global Jobs Indicators Database (JOIN)
#> 9585 Global Jobs Indicators Database (JOIN)
#> 9586 Global Jobs Indicators Database (JOIN)
#> 9587 Global Jobs Indicators Database (JOIN)
#> 9588 Global Jobs Indicators Database (JOIN)
#> 9589 Global Jobs Indicators Database (JOIN)
#> 9590 Global Jobs Indicators Database (JOIN)
#> 9591 Global Jobs Indicators Database (JOIN)
#> 9592 Global Jobs Indicators Database (JOIN)
#> 9593 Global Jobs Indicators Database (JOIN)
#> 9594 Global Jobs Indicators Database (JOIN)
#> 9595 Global Jobs Indicators Database (JOIN)
#> 9596 Global Jobs Indicators Database (JOIN)
#> 9597 Global Jobs Indicators Database (JOIN)
#> 9598 Global Jobs Indicators Database (JOIN)
#> 9599 Global Jobs Indicators Database (JOIN)
#> 9600 Global Jobs Indicators Database (JOIN)
#> 9601 Global Jobs Indicators Database (JOIN)
#> 9602 Global Jobs Indicators Database (JOIN)
#> 9603 Global Jobs Indicators Database (JOIN)
#> 9604 Global Jobs Indicators Database (JOIN)
#> 9605 Global Jobs Indicators Database (JOIN)
#> 9606 Global Jobs Indicators Database (JOIN)
#> 9607 Global Jobs Indicators Database (JOIN)
#> 9608 Global Jobs Indicators Database (JOIN)
#> 9612 Global Jobs Indicators Database (JOIN)
#> 9613 Global Jobs Indicators Database (JOIN)
#> 9614 Global Jobs Indicators Database (JOIN)
#> 9615 Global Jobs Indicators Database (JOIN)
#> 9616 Global Jobs Indicators Database (JOIN)
#> 9617 Global Jobs Indicators Database (JOIN)
#> 9618 Global Jobs Indicators Database (JOIN)
#> 9619 Global Jobs Indicators Database (JOIN)
#> 9620 Global Jobs Indicators Database (JOIN)
#> 9621 Global Jobs Indicators Database (JOIN)
#> 9622 Global Jobs Indicators Database (JOIN)
#> 9623 Global Jobs Indicators Database (JOIN)
#> 9624 Global Jobs Indicators Database (JOIN)
#> 9625 Global Jobs Indicators Database (JOIN)
#> 9626 Global Jobs Indicators Database (JOIN)
#> 9627 Global Jobs Indicators Database (JOIN)
#> 9628 Global Jobs Indicators Database (JOIN)
#> 9629 Global Jobs Indicators Database (JOIN)
#> 9630 Global Jobs Indicators Database (JOIN)
#> 9631 Global Jobs Indicators Database (JOIN)
#> 9632 Global Jobs Indicators Database (JOIN)
#> 9633 Global Jobs Indicators Database (JOIN)
#> 9634 Global Jobs Indicators Database (JOIN)
#> 9635 Global Jobs Indicators Database (JOIN)
#> 9636 Global Jobs Indicators Database (JOIN)
#> 9637 Global Jobs Indicators Database (JOIN)
#> 9638 Global Jobs Indicators Database (JOIN)
#> 9639 Global Jobs Indicators Database (JOIN)
#> 9640 Global Jobs Indicators Database (JOIN)
#> 9641 Global Jobs Indicators Database (JOIN)
#> 9642 Global Jobs Indicators Database (JOIN)
#> 9643 Global Jobs Indicators Database (JOIN)
#> 9644 Global Jobs Indicators Database (JOIN)
#> 9645 Global Jobs Indicators Database (JOIN)
#> 9646 Global Jobs Indicators Database (JOIN)
#> 9647 Global Jobs Indicators Database (JOIN)
#> 9648 Global Jobs Indicators Database (JOIN)
#> 9649 Global Jobs Indicators Database (JOIN)
#> 9650 Global Jobs Indicators Database (JOIN)
#> 9651 Global Jobs Indicators Database (JOIN)
#> 9652 Global Jobs Indicators Database (JOIN)
#> 9653 Global Jobs Indicators Database (JOIN)
#> 9654 Global Jobs Indicators Database (JOIN)
#> 9655 Global Jobs Indicators Database (JOIN)
#> 9656 Global Jobs Indicators Database (JOIN)
#> 9657 Global Jobs Indicators Database (JOIN)
#> 9658 Global Jobs Indicators Database (JOIN)
#> 9659 Global Jobs Indicators Database (JOIN)
#> 9696 Global Jobs Indicators Database (JOIN)
#> 9697 Global Jobs Indicators Database (JOIN)
#> 9698 Global Jobs Indicators Database (JOIN)
#> 9699 Global Jobs Indicators Database (JOIN)
#> 9700 Global Jobs Indicators Database (JOIN)
#> 9701 Global Jobs Indicators Database (JOIN)
#> 9702 Global Jobs Indicators Database (JOIN)
#> 9703 Global Jobs Indicators Database (JOIN)
#> 9704 Global Jobs Indicators Database (JOIN)
#> 9705 Global Jobs Indicators Database (JOIN)
#> 9706 Global Jobs Indicators Database (JOIN)
#> 9707 Global Jobs Indicators Database (JOIN)
#> 9708 Global Jobs Indicators Database (JOIN)
#> 9709 Global Jobs Indicators Database (JOIN)
#> 9710 Global Jobs Indicators Database (JOIN)
#> 9711 Global Jobs Indicators Database (JOIN)
#> 9712 Global Jobs Indicators Database (JOIN)
#> 9713 Global Jobs Indicators Database (JOIN)
#> 9748 Global Jobs Indicators Database (JOIN)
#> 9749 Global Jobs Indicators Database (JOIN)
#> 9750 Global Jobs Indicators Database (JOIN)
#> 9751 Global Jobs Indicators Database (JOIN)
#> 9752 Global Jobs Indicators Database (JOIN)
#> 9753 Global Jobs Indicators Database (JOIN)
#> 9754 Global Jobs Indicators Database (JOIN)
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#> 11715 World Development Indicators
#> 11993 World Development Indicators
#> 11997 World Development Indicators
#> 12001 World Development Indicators
#> 12005 World Development Indicators
#> 12009 World Development Indicators
#> 12013 World Development Indicators
#> 12165 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12166 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12167 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12168 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12169 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12170 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12171 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12172 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12173 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12174 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12175 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12176 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12177 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 12179 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 12181 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 12183 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 12184 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 12186 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 12189 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 12752 World Development Indicators
#> 12756 World Development Indicators
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#> 12764 World Development Indicators
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#> 13893 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 13894 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 13895 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 13896 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 13902 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 13905 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 13906 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 13907 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 13908 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 13910 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 13912 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 14023 World Development Indicators
#> 14027 World Development Indicators
#> 14031 World Development Indicators
#> 14035 World Development Indicators
#> 14039 World Development Indicators
#> 14330 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14331 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14332 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 14334 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14335 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14336 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14337 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14338 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14339 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14340 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14341 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14342 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14343 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14344 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14345 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14346 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14347 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14348 The Atlas of Social Protection: Indicators of Resilience and Equity
#> 14349 The Atlas of Social Protection: Indicators of Resilience and Equity
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#> 14841 Statistical Performance Indicators (SPI)
#> 15186 Indonesia Database for Policy and Economic Research
#> 15278 World Development Indicators
#> 15279 World Development Indicators
#> 15280 World Development Indicators
#> 15501 World Development Indicators
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#> 15761 World Development Indicators
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#> 15841 World Development Indicators
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#> 15850 World Development Indicators
#> 15851 World Development Indicators
#> 15852 World Development Indicators
#> 16251 WDI Database Archives
#> 16258 World Development Indicators
#> 16259 World Development Indicators
#> 16269 Gender Statistics
#> 16270 Gender Statistics
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#> 16330 World Development Indicators
#> 16333 World Development Indicators
#> 16421 Health Nutrition and Population Statistics by Wealth Quintile
#> 16422 Health Nutrition and Population Statistics by Wealth Quintile
#> 16423 Health Nutrition and Population Statistics by Wealth Quintile
#> 16424 Health Nutrition and Population Statistics by Wealth Quintile
#> 16425 Health Nutrition and Population Statistics by Wealth Quintile
#> 16426 Health Nutrition and Population Statistics by Wealth Quintile
#> 16427 Health Nutrition and Population Statistics by Wealth Quintile
#> 16428 Health Nutrition and Population Statistics by Wealth Quintile
#> 16429 Health Nutrition and Population Statistics by Wealth Quintile
#> 16430 Health Nutrition and Population Statistics by Wealth Quintile
#> 16431 World Development Indicators
#> 16432 Health Nutrition and Population Statistics by Wealth Quintile
#> 16433 Health Nutrition and Population Statistics by Wealth Quintile
#> 16434 Health Nutrition and Population Statistics by Wealth Quintile
#> 16435 Health Nutrition and Population Statistics by Wealth Quintile
#> 16436 Health Nutrition and Population Statistics by Wealth Quintile
#> 16437 World Development Indicators
#> 16438 World Development Indicators
#> 16439 WDI Database Archives
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#> 16441 WDI Database Archives
#> 16442 World Development Indicators
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#> 16462 Health Nutrition and Population Statistics
#> 16463 Gender Statistics
#> 16464 Gender Statistics
#> 16466 World Development Indicators
#> 16468 Gender Statistics
#> 16469 Gender Statistics
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#> 16471 World Development Indicators
#> 16505 Indonesia Database for Policy and Economic Research
#> 16537 WDI Database Archives
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#> 16619 WDI Database Archives
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#> 16623 World Development Indicators
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#> 16625 World Development Indicators
#> 16655 Health Nutrition and Population Statistics by Wealth Quintile
#> 16656 Health Nutrition and Population Statistics by Wealth Quintile
#> 16657 Health Nutrition and Population Statistics by Wealth Quintile
#> 16658 Health Nutrition and Population Statistics by Wealth Quintile
#> 16659 Health Nutrition and Population Statistics by Wealth Quintile
#> 16660 Health Nutrition and Population Statistics by Wealth Quintile
#> 16661 Health Nutrition and Population Statistics by Wealth Quintile
#> 16662 Health Nutrition and Population Statistics by Wealth Quintile
#> 16663 Health Nutrition and Population Statistics by Wealth Quintile
#> 16664 Health Nutrition and Population Statistics by Wealth Quintile
#> 16665 World Development Indicators
#> 16666 Health Nutrition and Population Statistics by Wealth Quintile
#> 16667 Health Nutrition and Population Statistics by Wealth Quintile
#> 16668 Health Nutrition and Population Statistics by Wealth Quintile
#> 16669 Health Nutrition and Population Statistics by Wealth Quintile
#> 16670 Health Nutrition and Population Statistics by Wealth Quintile
#> 16671 World Development Indicators
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#> 16714 Health Nutrition and Population Statistics by Wealth Quintile
#> 16715 Health Nutrition and Population Statistics by Wealth Quintile
#> 16716 Health Nutrition and Population Statistics by Wealth Quintile
#> 16717 Health Nutrition and Population Statistics by Wealth Quintile
#> 16718 Health Nutrition and Population Statistics by Wealth Quintile
#> 16719 Health Nutrition and Population Statistics by Wealth Quintile
#> 16720 Health Nutrition and Population Statistics by Wealth Quintile
#> 16721 Health Nutrition and Population Statistics by Wealth Quintile
#> 16722 Health Nutrition and Population Statistics by Wealth Quintile
#> 16723 Health Nutrition and Population Statistics by Wealth Quintile
#> 16724 World Development Indicators
#> 16725 Health Nutrition and Population Statistics by Wealth Quintile
#> 16726 Health Nutrition and Population Statistics by Wealth Quintile
#> 16727 Health Nutrition and Population Statistics by Wealth Quintile
#> 16728 Health Nutrition and Population Statistics by Wealth Quintile
#> 16729 Health Nutrition and Population Statistics by Wealth Quintile
#> 16730 World Development Indicators
#> 16731 World Development Indicators
#> 16754 Gender Statistics
#> 16755 Gender Statistics
#> 16756 Health Nutrition and Population Statistics by Wealth Quintile
#> 16757 Health Nutrition and Population Statistics by Wealth Quintile
#> 16758 Health Nutrition and Population Statistics by Wealth Quintile
#> 16759 Health Nutrition and Population Statistics by Wealth Quintile
#> 16760 Health Nutrition and Population Statistics by Wealth Quintile
#> 16761 Health Nutrition and Population Statistics by Wealth Quintile
#> 16762 Health Nutrition and Population Statistics by Wealth Quintile
#> 16763 Health Nutrition and Population Statistics by Wealth Quintile
#> 16764 Health Nutrition and Population Statistics by Wealth Quintile
#> 16765 Health Nutrition and Population Statistics by Wealth Quintile
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#> 16767 Health Nutrition and Population Statistics by Wealth Quintile
#> 16768 Health Nutrition and Population Statistics by Wealth Quintile
#> 16769 Health Nutrition and Population Statistics by Wealth Quintile
#> 16770 Health Nutrition and Population Statistics by Wealth Quintile
#> 16771 Health Nutrition and Population Statistics by Wealth Quintile
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#> 16773 World Development Indicators
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#> 16800 World Development Indicators
#> 16801 World Development Indicators
#> 16802 World Development Indicators
#> 16803 World Development Indicators
#> 16804 World Development Indicators
#> 16819 World Development Indicators
#> 16820 World Development Indicators
#> 16821 World Development Indicators
#> 16822 Gender Statistics
#> 16823 Gender Statistics
#> 16824 World Development Indicators
#> 16825 Gender Statistics
#> 16826 Gender Statistics
#> 16827 World Development Indicators
#> 16851 Africa Development Indicators
#> 16852 Africa Development Indicators
#> 16853 Africa Development Indicators
#> 16854 Africa Development Indicators
#> 16855 Africa Development Indicators
#> 16857 WDI Database Archives
#> 16859 Health Nutrition and Population Statistics
#> 16861 Health Nutrition and Population Statistics
#> 16863 Health Nutrition and Population Statistics
#> 16867 World Development Indicators
#> 16871 World Development Indicators
#> 16873 Health Nutrition and Population Statistics
#> 16875 World Development Indicators
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#> 16930 WDI Database Archives
#> 16931 Poverty and Equity
#> 16933 World Development Indicators
#> 16934 Poverty and Equity
#> 16935 Poverty and Equity
#> 16936 Poverty and Equity
#> 16937 Poverty and Equity
#> 16938 Poverty and Equity
#> 16939 Poverty and Equity
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#> 16943 Poverty and Equity
#> 16944 Poverty and Equity
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#> 16946 Poverty and Equity
#> 16947 Poverty and Equity
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#> 16951 Poverty and Equity
#> 16952 Poverty and Equity
#> 16959 Poverty and Equity
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#> 16961 Poverty and Equity
#> 16964 Poverty and Equity
#> 16965 Poverty and Equity
#> 16966 World Development Indicators
#> 16967 World Development Indicators
#> 16968 World Development Indicators
#> 16969 World Development Indicators
#> 16972 World Development Indicators
#> 16976 World Development Indicators
#> 16977 Poverty and Equity
#> 16979 Indonesia Database for Policy and Economic Research
#> 16985 WDI Database Archives
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#> 16987 World Development Indicators
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#> 16992 Poverty and Equity
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#> 17001 Poverty and Equity
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#> 17004 World Development Indicators
#> 17005 World Development Indicators
#> 17006 WDI Database Archives
#> 17007 World Development Indicators
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#> 17134 World Development Indicators
#> 17135 World Development Indicators
#> 17136 Africa Development Indicators
#> 17137 Africa Development Indicators
#> 17138 Africa Development Indicators
#> 17139 Africa Development Indicators
#> 17140 Africa Development Indicators
#> 17141 Africa Development Indicators
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#> 17148 Africa Development Indicators
#> 17149 Africa Development Indicators
#> 17150 Africa Development Indicators
#> 17151 World Development Indicators
#> 17152 World Development Indicators
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#> 17205 World Development Indicators
#> 17206 World Development Indicators
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#> 17228 WDI Database Archives
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#> 17233 World Development Indicators
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#> 17240 Africa Development Indicators
#> 17241 World Development Indicators
#> 17244 World Development Indicators
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#> 17342 Health Nutrition and Population Statistics
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#> 17350 World Development Indicators
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#> 17352 Africa Development Indicators
#> 17353 Education Statistics
#> 17354 Education Statistics
#> 17355 Education Statistics
#> 17356 Education Statistics
#> 17357 Education Statistics
#> 17358 Education Statistics
#> 17359 Health Nutrition and Population Statistics
#> 17360 World Development Indicators
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#> 17394 Education Statistics
#> 17395 Education Statistics
#> 17396 Education Statistics
#> 17397 Education Statistics
#> 17398 Education Statistics
#> 17399 Health Nutrition and Population Statistics
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#> 17401 Education Statistics
#> 17402 Health Nutrition and Population Statistics
#> 17403 World Development Indicators
#> 17404 Education Statistics
#> 17405 Education Statistics
#> 17406 Education Statistics
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#> 17457 Education Statistics
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#> 17460 Health Nutrition and Population Statistics
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#> 17462 Health Nutrition and Population Statistics
#> 17463 World Development Indicators
#> 17464 Education Statistics
#> 17465 Education Statistics
#> 17466 Education Statistics
#> 17467 World Development Indicators
#> 17468 World Development Indicators
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#> 17470 WDI Database Archives
#> 17471 World Development Indicators
#> 17472 World Development Indicators
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#> 17475 Health Nutrition and Population Statistics
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#> 17517 World Development Indicators
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#> 17550 Education Statistics
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#> 17567 Education Statistics
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#> 17661 Health Nutrition and Population Statistics
#> 17662 Education Statistics
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#> 17666 World Development Indicators
#> 17667 World Development Indicators
#> 17668 World Development Indicators
#> 17669 WDI Database Archives
#> 17674 World Development Indicators
#> 17675 World Development Indicators
#> 17676 World Development Indicators
#> 17677 ICP 2017
#> 17678 ICP 2017
#> 17679 World Development Indicators
#> 17680 World Development Indicators
#> 17681 Subnational Population
#> 17682 Education Statistics
#> 17683 Education Statistics
#> 17684 Education Statistics
#> 17685 Education Statistics
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#> 17687 Education Statistics
#> 17688 Education Statistics
#> 17689 Education Statistics
#> 17690 Education Statistics
#> 17702 World Development Indicators
#> 17703 Gender Statistics
#> 17704 Gender Statistics
#> 17705 World Development Indicators
#> 17706 World Development Indicators
#> 17707 Education Statistics
#> 17708 Education Statistics
#> 17709 Education Statistics
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#> 17719 World Development Indicators
#> 17720 WDI Database Archives
#> 17721 WDI Database Archives
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#> 17724 World Development Indicators
#> 17725 Gender Statistics
#> 17726 World Development Indicators
#> 17727 Gender Statistics
#> 17728 Indonesia Database for Policy and Economic Research
#> 17771 Statistical Performance Indicators (SPI)
#> 18453 Education Statistics
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#> sourceOrganization
#> 25 World Bank Global Electrification Database 2012
#> 40 World Bank Global Electrification Database 2013
#> 41 World Bank Global Electrification Database 2014
#> 165
#> 199 World Bank Microdata library. Original source: United Nations Statistical Division (UNSD), 2010 World Population and Housing Censuses Programme
#> 1173
#> 1176
#> 1179
#> 1197 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1198 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1199 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1200 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1201 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1202 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1203 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1204 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1205 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1206 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1207 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1208 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1209 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1210 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1211 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1212 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1213 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1214 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1215 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1216 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1217 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1218 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1219 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1220 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1221 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1222 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1223 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1224 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1225 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1226 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1227 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1228 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1229 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1230 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1231 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1232 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1233 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1234 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
#> 1235 Robert J. Barro and Jong-Wha Lee: http://www.barrolee.com/
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#> 1916
#> 2024 Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z
#> 2025 Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z
#> 2026 Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z
#> 2027 Kulp, S.A., Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat Commun 10, 4844 (2019). https://doi.org/10.1038/s41467-019-12808-z
#> 2029 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2030 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2031 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2032 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2033 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2034 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2035 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2036 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2037 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2038 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2039 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2040 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2041 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2042 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2043 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
#> 2044 Hallegatte, Stephane; Bangalore, Mook; Bonzanigo, Laura; Fay, Marianne; Kane, Tamaro; Narloch, Ulf; Rozenberg, Julie; Treguer, David; Vogt-Schilb, Adrien. 2016. Shock Waves : Managing the Impacts of Climate Change on Poverty. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/22787 License: CC BY 3.0 IGO.
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#> 2236 Rentschler, Jun; Salhab, Melda. 2020. People in Harm's Way : Flood Exposure and Poverty in 189 Countries. Policy Research Working Paper;No. 9447. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34655 License: CC BY 3.0 IGO.
#> 2237 Rentschler, Jun; Salhab, Melda. 2020. People in Harm's Way : Flood Exposure and Poverty in 189 Countries. Policy Research Working Paper;No. 9447. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34655 License: CC BY 3.0 IGO.
#> 2272 Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org.
#> 2358 Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)
#> 2359 Global Jobs Indicators Database, The World Bank. Available at: https://databank.worldbank.org/source/global-jobs-indicators-database-(join)
#> 2361 UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/
#> 2362 UN Open Data Hub. Available at: https://unstats-undesa.opendata.arcgis.com/
#> 2363 International Labour Organization - ILOSTAT database, https://ilostat.ilo.org/data
#> 2411
#> 2423
#> 2439
#> 5429 Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline.
#> 5966 WHO Global Health Observatory (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution)
#> 5967 WHO Global Health Observatory (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution)
#> 5968 WHO Global Health Observatory (https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution)
#> 5971 World Bank Global Electrification Database from "Tracking SDG 7: The Energy Progress Report" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO).
#> 5972 World Bank Global Electrification Database from "Tracking SDG 7: The Energy Progress Report" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO).
#> 5973 World Bank Global Electrification Database from "Tracking SDG 7: The Energy Progress Report" led jointly by the custodian agencies: the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), the World Bank and the World Health Organization (WHO).
#> 5999 World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database.
#> 6000 World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database.
#> 6001 World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database.
#> 6013 Food and Agriculture Organization, Production Yearbook and data files.
#> 6014 Food and Agriculture Organization, electronic files and web site.
#> 6015 Food and Agriculture Organization, electronic files and web site.
#> 6016 Food and Agriculture Organization, electronic files and web site.
#> 6064 Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017.
#> 6065 Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017.
#> 6066 Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017.
#> 6067 Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017.
#> 6075 EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.
#> 6103 Food and Agriculture Organization, electronic files and web site.
#> 6104 Food and Agriculture Organization and World Bank population estimates.
#> 6105 Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.
#> 6106 Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.
#> 6107 Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.
#> 6108 United Nations Human Settlements Programme (UN-HABITAT)
#> 6113 Food and Agriculture Organization and World Bank population estimates.
#> 6114
#> 6120 United Nations, World Urbanization Prospects.
#> 6121 United Nations, World Urbanization Prospects.
#> 6122 United Nations, World Urbanization Prospects.
#> 6123 United Nations, World Urbanization Prospects.
#> 7474 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7475 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7476 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7477 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7478 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7479 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7480 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7481 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7482 Global Findex Database, World Bank (https://www.worldbank.org/en/publication/globalfindex).
#> 7945 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7946 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7947 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7948 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7949 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7950 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7951 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7952 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7953 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7954 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7955 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7956 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7993 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7994 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7995 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7996 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7997 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 7998 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8011 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8012 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8013 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8014 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8015 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8016 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8041 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8042 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8043 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8044 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8045 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8046 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8047
#> 8048
#> 8049
#> 8050
#> 8051
#> 8052
#> 8053 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8054 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8055 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8056 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8057 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8058 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8059
#> 8060
#> 8061
#> 8062
#> 8063
#> 8064
#> 8065 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8066 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8067 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8068 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8069 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8070 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8077
#> 8078 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8079 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8080 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8081 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8082 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8083 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8084 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8085 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8086 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8087 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8088 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8089 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8090 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8091
#> 8092 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8117 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8118 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8119 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8120 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8121 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8122 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8135 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8136 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8137 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8138 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8139 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8140 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8141 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8142 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8143 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8144 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8145 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8146 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8147 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8148 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8149 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8150 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8151 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8152 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8153 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8154 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8155 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8156 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8157 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8158 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8165 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8166 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8167 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8168 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8169 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8170 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8171 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8172 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8173 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8174 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8175 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8176 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8177 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8178 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8179 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8180 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8181 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8182 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8183 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8184 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8185 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8186 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8187 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8188 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8195 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8196 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8197 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8198 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8199 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8200 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8202 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8203 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8204 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8205 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8206 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8207 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8209 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8210 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8211 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8212 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8213 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8214 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8216
#> 8217
#> 8218
#> 8219
#> 8220
#> 8221
#> 8223
#> 8224
#> 8225
#> 8226
#> 8227
#> 8228
#> 8229
#> 8230
#> 8231
#> 8232
#> 8233
#> 8234
#> 8242 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8243 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8244 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8245 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8246 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8247 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8248 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8249 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8250 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8251 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8252 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8253 Health Equity and Financial Protection Indicators (HEFPI) database, World Bank
#> 8708 Source: Provisional Population Tables & Annexures, Census of India 2011
#> 8709
#> 8710
#> 8711
#> 8712
#> 8713 Source:\nCompiled from the statistics released by : Tenth Five Year Plan 2002-07, Volume-III, Planning Commission, Gov't of India
#> 8714
#> 8775 http://www.rbi.org.in/scripts/PublicationsView.aspx?id=12662
#> 8779 Ministry of Health and Family Welfare, India.\nhttp://mospi.nic.in/Mospi_New/Upload/SYB2014/CH-30-HEALTH AND FAMILY WELFARE/TABLE 30.1.xlsx
#> 8781 Ministry of Health and Family Welfare, India.\nhttp://mospi.nic.in/Mospi_New/Upload/SYB2014/CH-30-HEALTH AND FAMILY WELFARE/TABLE 30.1.xlsx
#> 8783 Ministry of Health and Family Welfare, India.\nhttp://mospi.nic.in/Mospi_New/Upload/SYB2014/CH-30-HEALTH AND FAMILY WELFARE/TABLE 30.1.xlsx
#> 8863
#> 8864
#> 8865
#> 8880 Source: Transport and Research Wing, Ministry of Road Transport and Highways, India\nRoad Transport : Table 1A.10 : Statewise Rural Road Density Per 1000 Population\nhttp://mospi.nic.in/Mospi_New/upload/Infra_stat_2010/1.ch_road.pdf
#> 8882 Source: Transport and Research Wing, Ministry of Road Transport and Highways, India\nRoad Transport : Table 1A.8 : Statewise Rural Road Density Per 1000 Population\nhttp://mospi.nic.in/Mospi_New/upload/Infra_stat_2010/1.ch_road.pdf
#> 8972 International Telecommunication Union, World Telecommunication/ICT Development Report and database.
#> 9027 International Telecommunication Union, World Telecommunication Development Report and database, and World Bank estimates.
#> 9052 International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database
#> 9176
#> 9177
#> 9178
#> 9179
#> 9180
#> 9181
#> 9182
#> 9183
#> 9184
#> 9185
#> 9186
#> 9187
#> 9188
#> 9189
#> 9190
#> 9191
#> 9192
#> 9193
#> 9194
#> 9195
#> 9196
#> 9197
#> 9198
#> 9199
#> 9200
#> 9201
#> 9202
#> 9203
#> 9204
#> 9205
#> 9206
#> 9207
#> 9208
#> 9209
#> 9210
#> 9211
#> 9212
#> 9213
#> 9214
#> 9215
#> 9216
#> 9217
#> 9218
#> 9219
#> 9220
#> 9221
#> 9222
#> 9223
#> 9224
#> 9225
#> 9226
#> 9227
#> 9228
#> 9229
#> 9230
#> 9231
#> 9232
#> 9233
#> 9234
#> 9235
#> 9236
#> 9237
#> 9238
#> 9239
#> 9240
#> 9241
#> 9242
#> 9243
#> 9244
#> 9245
#> 9246
#> 9247
#> 9248
#> 9249
#> 9250
#> 9251
#> 9252
#> 9253
#> 9254
#> 9255
#> 9256
#> 9257
#> 9258
#> 9259
#> 9260
#> 9261
#> 9262
#> 9263
#> 9264
#> 9265
#> 9266
#> 9267
#> 9268
#> 9269
#> 9270
#> 9271
#> 9272
#> 9273
#> 9274
#> 9275
#> 9276
#> 9277
#> 9278
#> 9279
#> 9280
#> 9281
#> 9282
#> 9283
#> 9284
#> 9285
#> 9286
#> 9287
#> 9288
#> 9289
#> 9290
#> 9291
#> 9292
#> 9293
#> 9294
#> 9295
#> 9296
#> 9297
#> 9298
#> 9299
#> 9300
#> 9301
#> 9302
#> 9303
#> 9304
#> 9305
#> 9306
#> 9307
#> 9308
#> 9309
#> 9310
#> 9311
#> 9312
#> 9313
#> 9314
#> 9315
#> 9316
#> 9317
#> 9318
#> 9319
#> 9320
#> 9321
#> 9322
#> 9323
#> 9324
#> 9325
#> 9326
#> 9327
#> 9328
#> 9329
#> 9330
#> 9331
#> 9332
#> 9333
#> 9334
#> 9335
#> 9336
#> 9337
#> 9338
#> 9339
#> 9340
#> 9341
#> 9342
#> 9343
#> 9344
#> 9352
#> 9353
#> 9354
#> 9355
#> 9356
#> 9357
#> 9358
#> 9359
#> 9360
#> 9361
#> 9362
#> 9363
#> 9364
#> 9365
#> 9366
#> 9367
#> 9368
#> 9369
#> 9370
#> 9371
#> 9372
#> 9373
#> 9374
#> 9375
#> 9376
#> 9377
#> 9378
#> 9379
#> 9380
#> 9381
#> 9382
#> 9383
#> 9384
#> 9385
#> 9386
#> 9387
#> 9388
#> 9389
#> 9390
#> 9391
#> 9392
#> 9393
#> 9394
#> 9395
#> 9396
#> 9397
#> 9398
#> 9399
#> 9400
#> 9401
#> 9402
#> 9403
#> 9404
#> 9405
#> 9406
#> 9407
#> 9408
#> 9409
#> 9410
#> 9411
#> 9412
#> 9413
#> 9414
#> 9415
#> 9416
#> 9417
#> 9418
#> 9419
#> 9420
#> 9421
#> 9422
#> 9423
#> 9424
#> 9425
#> 9426
#> 9427
#> 9428
#> 9429
#> 9430
#> 9431
#> 9432
#> 9433
#> 9434
#> 9435
#> 9436
#> 9437
#> 9438
#> 9439
#> 9440
#> 9441
#> 9442
#> 9443
#> 9444
#> 9445
#> 9446
#> 9447
#> 9448
#> 9449
#> 9450
#> 9451
#> 9452
#> 9453
#> 9454
#> 9455
#> 9456
#> 9457
#> 9458
#> 9459
#> 9460
#> 9461
#> 9462
#> 9463
#> 9464
#> 9465
#> 9466
#> 9467
#> 9468
#> 9469
#> 9470
#> 9471
#> 9472
#> 9473
#> 9474
#> 9475
#> 9476
#> 9477
#> 9478
#> 9479
#> 9480
#> 9481
#> 9482
#> 9483
#> 9484
#> 9485
#> 9486
#> 9487
#> 9488
#> 9489
#> 9490
#> 9491
#> 9492
#> 9493
#> 9494
#> 9495
#> 9496
#> 9497
#> 9498
#> 9499
#> 9500
#> 9501
#> 9502
#> 9503
#> 9504
#> 9512
#> 9513
#> 9514
#> 9515
#> 9516
#> 9517
#> 9518
#> 9519
#> 9520
#> 9521
#> 9522
#> 9523
#> 9524
#> 9525
#> 9526
#> 9527
#> 9528
#> 9529
#> 9530
#> 9531
#> 9532
#> 9533
#> 9534
#> 9535
#> 9536
#> 9537
#> 9565
#> 9566
#> 9567
#> 9568
#> 9569
#> 9570
#> 9571
#> 9572
#> 9573
#> 9574
#> 9575
#> 9576
#> 9577
#> 9578
#> 9579
#> 9580
#> 9581
#> 9582
#> 9583
#> 9584
#> 9585
#> 9586
#> 9587
#> 9588
#> 9589
#> 9590
#> 9591
#> 9592
#> 9593
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#> 9826
#> 11715 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 11993 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 11997 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12001 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12005 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12009 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12013 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12165 ASPIRE
#> 12166 ASPIRE
#> 12167 ASPIRE
#> 12168 ASPIRE
#> 12169 ASPIRE
#> 12170 ASPIRE
#> 12171 ASPIRE
#> 12172 ASPIRE
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#> 12206 ASPIRE
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#> 12211 ASPIRE
#> 12212 ASPIRE
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#> 12214 ASPIRE
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#> 12224 ASPIRE
#> 12748 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12752 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12756 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12760 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12764 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 12768 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 13893 ASPIRE
#> 13894 ASPIRE
#> 13895 ASPIRE
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#> 13909 ASPIRE
#> 13910 ASPIRE
#> 13911 ASPIRE
#> 13912 ASPIRE
#> 14019 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 14023 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 14027 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 14031 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 14035 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 14039 ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/)
#> 14330 ASPIRE
#> 14331 ASPIRE
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#> 14348 ASPIRE
#> 14349 ASPIRE
#> 14446 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14447 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14448 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14449 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14450 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14451 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14452 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14453 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14454 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14455 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14456 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14457 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14458 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14459 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14460 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14461 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14462 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14463 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14464 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14465 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14466 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14467 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14468 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14469 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14470 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14471 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14472 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14473 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14474 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14475 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14476 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14477 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14478 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14479 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14480 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14481 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14482 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14483 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14484 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14485 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14486 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14487 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14488 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14489 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14490 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14491 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14492 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14493 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14494 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14495 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14496 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14497 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14498 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14499 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14500 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14501 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14502 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14503 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14504 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14505 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14506 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14507 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14508 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14509 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14510 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14511 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14512 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14513 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14514 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14515 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14516 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14517 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14518 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14519 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14520 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14521 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14522 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14523 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14524 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14525 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14526 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14527 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14528 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14529 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14530 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14531 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14532 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14533 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14534 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14535 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14536 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
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#> 14545 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14546 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14547 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14548 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14549 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14550 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14551 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14552 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14553 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
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#> 14555 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
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#> 14560 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
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#> 14570 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
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#> 14615 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14616 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14617 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14618 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14619 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14620 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14621 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14622 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14623 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14624 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14625 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14626 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14627 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14628 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14629 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14630 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14631 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14632 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14633 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14634 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14635 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14636 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14637 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14638 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14639 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14640 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14641 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14642 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14643 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14682 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14683 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14684 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14685 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14686 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14687 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14688 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14689 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14690 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14691 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14692 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14693 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14694 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14695 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14696 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14697 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14698 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14699 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14700 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14701 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14702 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14703 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14704 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14705 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14706 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14707 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14708 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14709 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14710 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14711 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14712 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14713 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14714 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14715 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14716 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14717 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14718 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14719 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14720 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14721 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14722 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14723 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14724 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14725 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14726 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14727 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14728 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14729 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14730 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14731 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14732 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14733 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14734 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14735 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14736 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14737 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14738 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14739 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14740 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14741 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14742 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14743 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14744 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14745 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14746 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14747 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14748 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14749 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14750 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14751 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14752 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14753 Wittgenstein Centre for Demography and Global Human Capital: http://www.oeaw.ac.at/vid/dataexplorer/
#> 14841 Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)
#> 15186 BADAN PUSAT STATISTIK - Statistics Indonesia, National Social Economic Survey (SUSENAS)
#> 15278 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15279 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15280 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15501 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.
#> 15502 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.
#> 15503 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.
#> 15761 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15762 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15763 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15764 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15765 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15766 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15767 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15768 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15769 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15841 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15842 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15843 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15844 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15845 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15846 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15847 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15848 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15849 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15850 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15851 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 15852 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of June 2022.
#> 16251 WHO, OECD and supplemented by country data.
#> 16258 Demographic and Health Surveys, and UNAIDS.
#> 16259 Demographic and Health Surveys, and UNAIDS.
#> 16269 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16270 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16271 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16272 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16273 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16274 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16275 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16276 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16277 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16278 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16279 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16280 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16281 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16282 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16287 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16288 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16289 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16290 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16291 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16292 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16293 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16294 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16295 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16296 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16297 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16298 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16299 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16300 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16305 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16306 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16307 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16308 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16309 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16310 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16311 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16312 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16313 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16314 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16315 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16316 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16317 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16318 Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death
#> 16330 UNAIDS estimates.
#> 16333 UNAIDS estimates.
#> 16421 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16422 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16423 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16424 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16425 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16426 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16427 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16428 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16429 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16430 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16431 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16432 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16433 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16434 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16435 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16436 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16437 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16438 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16439 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).
#> 16440 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).
#> 16441 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).
#> 16442 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16443 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16444 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16462 UNAIDS estimates.
#> 16463 UNAIDS estimates.
#> 16464 UNAIDS estimates.
#> 16466 UNAIDS estimates.
#> 16468 UNAIDS estimates.
#> 16469 UNAIDS estimates.
#> 16470 UNAIDS estimates.
#> 16471 UNAIDS estimates.
#> 16505 BADAN PUSAT STATISTIK - Statistics Indonesia, National Social Economic Survey (SUSENAS)
#> 16537
#> 16539 Data collected by the Lancet Commission on Global Surgery (www.lancetglobalsurgery.org); Data collected by WHO Collaborating Centre for Surgery and Public Health at Lund University from various sources including Ministries of Health or equivalent national regulatory bodies, national official entities such as medical councils, Eurostat, OECD, WHO Euro Health For All Database, WHO EURO Technical resources for health Database; BMJ Glob Health.
#> 16544 World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/ghodata/).
#> 16567 UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.
#> 16618 Data from various sources compiled by the Lancet Commission on Global Surgery (www.lancetglobalsurgery.org) and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center.
#> 16619
#> 16620 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).
#> 16621 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).
#> 16622 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).
#> 16623 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16624 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16625 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16655 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16656 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16657 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16658 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16659 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16660 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16661 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16662 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16663 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16664 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16665 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16666 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16667 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16668 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16669 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16670 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16671 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16672 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16696 International Diabetes Federation, Diabetes Atlas.
#> 16714 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16715 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16716 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16717 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16718 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16719 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16720 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16721 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16722 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16723 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16724 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16725 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16726 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16727 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16728 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16729 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16730 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16731 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16754 World Health Organization (WHO):Global Health Observatory Data Repository
#> 16755 World Health Organization (WHO): Global Health Observatory Data Repository
#> 16756 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16757 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16758 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16759 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16760 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16761
#> 16762
#> 16763
#> 16764
#> 16765
#> 16766 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16767
#> 16768
#> 16769
#> 16770
#> 16771
#> 16772 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16773 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16799 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16800 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16801 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16802 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16803 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16804 WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).
#> 16819 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16820 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16821 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16822 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16823 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16824 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16825 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16826 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16827 World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).
#> 16851 World Health Organization, Global Tuberculosis Control Report.
#> 16852 World Health Organization, Global Tuberculosis Control Report.
#> 16853 World Health Organization, Global Tuberculosis Control Report.
#> 16854 World Health Organization, Global Tuberculosis Control Report.
#> 16855 World Health Organization, Global Tuberculosis Control Report.
#> 16857 Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017.
#> 16859 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16861 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16863 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16867 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16871 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16873 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16875 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16877 World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.
#> 16930 World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).
#> 16931 World Bank using Global Monitoring Database (GMD)
#> 16933 World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.
#> 16934 World Bank using Global Monitoring Database (GMD)
#> 16935 World Bank using Global Monitoring Database (GMD)
#> 16936 World Bank using Global Monitoring Database (GMD)
#> 16937 World Bank using Global Monitoring Database (GMD)
#> 16938 World Bank using Global Monitoring Database (GMD)
#> 16939 World Bank using Global Monitoring Database (GMD)
#> 16940 World Bank using Global Monitoring Database (GMD)
#> 16942 World Bank using Global Monitoring Database (GMD)
#> 16943
#> 16944 World Bank using Global Monitoring Database (GMD)
#> 16945 World Bank using Global Monitoring Database (GMD)
#> 16946 World Bank using Global Monitoring Database (GMD)
#> 16947
#> 16948 World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).
#> 16949
#> 16950 World Bank using Global Monitoring Database (GMD)
#> 16951 World Bank using Global Monitoring Database (GMD)
#> 16952 World Bank using Global Monitoring Database (GMD)
#> 16959 World Bank using Global Monitoring Database (GMD)
#> 16960 World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.
#> 16961
#> 16964
#> 16965
#> 16966 Government statistical agencies. Data for EU countires are from the EUROSTAT
#> 16967 Government statistical agencies. Data for EU countires are from the EUROSTAT
#> 16968 Government statistical agencies. Data for EU countires are from the EUROSTAT
#> 16969 Government statistical agencies. Data for EU countires are from the EUROSTAT
#> 16972 Government statistical agencies. Data for EU countires are from the EUROSTAT
#> 16976 World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.
#> 16977
#> 16979 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 16985 World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.
#> 16986 World Bank using Global Monitoring Database (GMD)
#> 16987 World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.
#> 16988
#> 16991
#> 16992
#> 16994 World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.
#> 16995 World Bank using Global Monitoring Database (GMD)
#> 17001
#> 17002 World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).
#> 17004 World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).
#> 17005 World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).
#> 17006 World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).
#> 17007 World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).
#> 17023 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17024 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17025 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17026 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17027 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17028 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17050 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17051 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17052 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17053 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17054 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17055 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17127 International Labour Organization, ILOSTAT database. Data as of June 2021.
#> 17128 International Labour Organization, ILOSTAT database. Data as of June 2021.
#> 17129 International Labour Organization, ILOSTAT database. Data as of June 2021.
#> 17130 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17131 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17132 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17133 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17134 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17135 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17136 International Labour Organization, Key Indicators of the Labour Market database.
#> 17137 International Labour Organization, Key Indicators of the Labour Market database.
#> 17138 International Labour Organization, Key Indicators of the Labour Market database.
#> 17139 International Labour Organization, Key Indicators of the Labour Market database.
#> 17140 International Labour Organization, Key Indicators of the Labour Market database.
#> 17141 International Labour Organization, Key Indicators of the Labour Market database.
#> 17142 International Labour Organization, Key Indicators of the Labour Market database.
#> 17143 International Labour Organization, Key Indicators of the Labour Market database.
#> 17144 International Labour Organization, Key Indicators of the Labour Market database.
#> 17145 International Labour Organization, Key Indicators of the Labour Market database.
#> 17146 International Labour Organization, Key Indicators of the Labour Market database.
#> 17147 International Labour Organization, Key Indicators of the Labour Market database.
#> 17148 International Labour Organization, Key Indicators of the Labour Market database.
#> 17149 International Labour Organization, Key Indicators of the Labour Market database.
#> 17150 International Labour Organization, Key Indicators of the Labour Market database.
#> 17151 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17152 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17155 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17156 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17157 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17158 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17161 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17162 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17163 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17204 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17205 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17206 International Labour Organization, ILOSTAT database. Data as of June 2022.
#> 17226 Frédéric Docquier, B. Lindsay Lowell, and Abdeslam Marfouk's , "A Gendered Assessment of Highly Skilled Emigration" (2009).
#> 17228
#> 17229
#> 17231
#> 17233 United Nations High Commissioner for Refugees (UNHCR) and UNRWA through UNHCR's Refugee Data Finder at https://www.unhcr.org/refugee-statistics/.
#> 17234 United Nations High Commissioner for Refugees (UNHCR), Refugee Data Finder at https://www.unhcr.org/refugee-statistics/.
#> 17236 United Nations Population Division, Trends in Total Migrant Stock: 2008 Revision.
#> 17240 Food and Agriculture Organization (http://www.fao.org/faostat/foodsecurity/index_en.htm).
#> 17241 Food and Agriculture Organization (http://www.fao.org/faostat/en/#home).
#> 17244 Food and Agriculture Organization of the United Nations (FAO)
#> 17246 Food and Agriculture Organization of the United Nations (FAO)
#> 17267
#> 17274
#> 17342 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17343 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17344 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17345 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17346 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17347 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17348 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17349 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17350 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17351 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17352 World Bank staff estimates from various sources including census reports, the United Nations Population Division's World Population Prospects, national statistical offices, household surveys conducted by national agencies, and Macro International.
#> 17353 UNESCO Institute for Statistics (Derived)
#> 17354 UNESCO Institute for Statistics (Derived)
#> 17355 UNESCO Institute for Statistics (Derived)
#> 17356 UNESCO Institute for Statistics (Derived)
#> 17357 UNESCO Institute for Statistics (Derived)
#> 17358 UNESCO Institute for Statistics (Derived)
#> 17359 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17360 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17361 UNESCO Institute for Statistics (Derived)
#> 17362 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17363 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17364 UNESCO Institute for Statistics (Derived)
#> 17365 UNESCO Institute for Statistics (Derived)
#> 17366 UNESCO Institute for Statistics (Derived)
#> 17367 UNESCO Institute for Statistics (Derived)
#> 17368 UNESCO Institute for Statistics (Derived)
#> 17369 UNESCO Institute for Statistics (Derived)
#> 17370 UNESCO Institute for Statistics (Derived)
#> 17371 UNESCO Institute for Statistics (Derived)
#> 17372 UNESCO Institute for Statistics (Derived)
#> 17373 UNESCO Institute for Statistics (Derived)
#> 17374 UNESCO Institute for Statistics (Derived)
#> 17375 UNESCO Institute for Statistics (Derived)
#> 17376 UNESCO Institute for Statistics (Derived)
#> 17377 UNESCO Institute for Statistics (Derived)
#> 17378 UNESCO Institute for Statistics (Derived)
#> 17379 UNESCO Institute for Statistics (Derived)
#> 17380 UNESCO Institute for Statistics (Derived)
#> 17381 UNESCO Institute for Statistics (Derived)
#> 17382 UNESCO Institute for Statistics (Derived)
#> 17383 UNESCO Institute for Statistics (Derived)
#> 17384 UNESCO Institute for Statistics (Derived)
#> 17385 UNESCO Institute for Statistics (Derived)
#> 17386 UNESCO Institute for Statistics (Derived)
#> 17387 UNESCO Institute for Statistics (Derived)
#> 17388 UNESCO Institute for Statistics (Derived)
#> 17389 UNESCO Institute for Statistics (Derived)
#> 17390 UNESCO Institute for Statistics (Derived)
#> 17391 UNESCO Institute for Statistics (Derived)
#> 17392 UNESCO Institute for Statistics (Derived)
#> 17393 UNESCO Institute for Statistics (Derived)
#> 17394 UNESCO Institute for Statistics (Derived)
#> 17395 UNESCO Institute for Statistics (Derived)
#> 17396 UNESCO Institute for Statistics (Derived)
#> 17397 UNESCO Institute for Statistics (Derived)
#> 17398 UNESCO Institute for Statistics (Derived)
#> 17399 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17400 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17401 UNESCO Institute for Statistics (Derived)
#> 17402 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17403 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17404 UNESCO Institute for Statistics (Derived)
#> 17405 UNESCO Institute for Statistics (Derived)
#> 17406 UNESCO Institute for Statistics (Derived)
#> 17407 UNESCO Institute for Statistics (Derived)
#> 17408 UNESCO Institute for Statistics (Derived)
#> 17409 UNESCO Institute for Statistics (Derived)
#> 17410 UNESCO Institute for Statistics (Derived)
#> 17411 UNESCO Institute for Statistics (Derived)
#> 17412 UNESCO Institute for Statistics (Derived)
#> 17413 UNESCO Institute for Statistics (Derived)
#> 17414 UNESCO Institute for Statistics (Derived)
#> 17415 UNESCO Institute for Statistics (Derived)
#> 17416 UNESCO Institute for Statistics (Derived)
#> 17417 UNESCO Institute for Statistics (Derived)
#> 17418 UNESCO Institute for Statistics (Derived)
#> 17419 UNESCO Institute for Statistics (Derived)
#> 17420 UNESCO Institute for Statistics (Derived)
#> 17421 UNESCO Institute for Statistics (Derived)
#> 17422 UNESCO Institute for Statistics (Derived)
#> 17423 UNESCO Institute for Statistics (Derived)
#> 17424 UNESCO Institute for Statistics (Derived)
#> 17425 UNESCO Institute for Statistics (Derived)
#> 17426 UNESCO Institute for Statistics (Derived)
#> 17427 UNESCO Institute for Statistics (Derived)
#> 17428 UNESCO Institute for Statistics (Derived)
#> 17429 UNESCO Institute for Statistics (Derived)
#> 17430 UNESCO Institute for Statistics (Derived)
#> 17431 UNESCO Institute for Statistics (Derived)
#> 17432 UNESCO Institute for Statistics (Derived)
#> 17433 UNESCO Institute for Statistics (Derived)
#> 17434 UNESCO Institute for Statistics (Derived)
#> 17435 UNESCO Institute for Statistics (Derived)
#> 17436 UNESCO Institute for Statistics (Derived)
#> 17437 UNESCO Institute for Statistics (Derived)
#> 17438 UNESCO Institute for Statistics (Derived)
#> 17439 UNESCO Institute for Statistics (Derived)
#> 17440 UNESCO Institute for Statistics (Derived)
#> 17441 UNESCO Institute for Statistics (Derived)
#> 17442 UNESCO Institute for Statistics (Derived)
#> 17443 UNESCO Institute for Statistics (Derived)
#> 17444 UNESCO Institute for Statistics (Derived)
#> 17445 UNESCO Institute for Statistics (Derived)
#> 17446 UNESCO Institute for Statistics (Derived)
#> 17447 UNESCO Institute for Statistics (Derived)
#> 17448 UNESCO Institute for Statistics (Derived)
#> 17449 UNESCO Institute for Statistics (Derived)
#> 17450 UNESCO Institute for Statistics (Derived)
#> 17451 UNESCO Institute for Statistics (Derived)
#> 17452 UNESCO Institute for Statistics (Derived)
#> 17453 UNESCO Institute for Statistics (Derived)
#> 17454 UNESCO Institute for Statistics (Derived)
#> 17455 UNESCO Institute for Statistics (Derived)
#> 17456 UNESCO Institute for Statistics (Derived)
#> 17457 UNESCO Institute for Statistics (Derived)
#> 17458 UNESCO Institute for Statistics (Derived)
#> 17459 UNESCO Institute for Statistics (Derived)
#> 17460 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17461 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17462 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17463 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17464 UNESCO Institute for Statistics (Derived)
#> 17465 UNESCO Institute for Statistics (Derived)
#> 17466 UNESCO Institute for Statistics (Derived)
#> 17467 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17468 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17469
#> 17470
#> 17471 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17472 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17473 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17474 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17475 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17476 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17477 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17478 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17479 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17480 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17481 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17482 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17483 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17484 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17485 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17486 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17487 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17488 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17489 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17490 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17491 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17492 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17493 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17494 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17495 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17496 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17497 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17498 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17499 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17500 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17501 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17502 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17503 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17504 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17505 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17506 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17507 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17508 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17509 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17510 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17511 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17512 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17513 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17514 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17515 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17516 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17517 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17518 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17520 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17521 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17522 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17523 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17524 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17525 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17526 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17527 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17528 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17529 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17530 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17531 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17532 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17533 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17534 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17535 UNESCO Institute for Statistics.
#> 17536 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17537 UNESCO Institute for Statistics
#> 17538 UNESCO Institute for Statistics.
#> 17539 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17540 UNESCO Institute for Statistics.
#> 17541 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17542 UNESCO Institute for Statistics
#> 17543 UNESCO Institute for Statistics.
#> 17544 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17545 UNESCO Institute for Statistics.
#> 17546 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17547 UNESCO Institute for Statistics
#> 17548 UNESCO Institute for Statistics.
#> 17549 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17550 UNESCO Institute for Statistics.
#> 17551 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17552 UNESCO Institute for Statistics
#> 17553 UNESCO Institute for Statistics.
#> 17554 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17555 UNESCO Institute for Statistics.
#> 17556 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17557 UNESCO Institute for Statistics
#> 17558 UNESCO Institute for Statistics.
#> 17559 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17560 UNESCO Institute for Statistics.
#> 17561 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17562 UNESCO Institute for Statistics
#> 17563 UNESCO Institute for Statistics.
#> 17564 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17565 UNESCO Institute for Statistics.
#> 17566 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17567 UNESCO Institute for Statistics
#> 17568 UNESCO Institute for Statistics.
#> 17569 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17570 UNESCO Institute for Statistics.
#> 17571 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17572 UNESCO Institute for Statistics
#> 17573 UNESCO Institute for Statistics.
#> 17574 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17575 UNESCO Institute for Statistics.
#> 17576 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17577 UNESCO Institute for Statistics
#> 17578 UNESCO Institute for Statistics.
#> 17579 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17580 UNESCO Institute for Statistics.
#> 17581 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17582 UNESCO Institute for Statistics
#> 17583 UNESCO Institute for Statistics.
#> 17584 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17585 UNESCO Institute for Statistics.
#> 17586 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17587 UNESCO Institute for Statistics
#> 17588 UNESCO Institute for Statistics.
#> 17589 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17590 UNESCO Institute for Statistics.
#> 17591 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17592 UNESCO Institute for Statistics
#> 17593 UNESCO Institute for Statistics.
#> 17594 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17595 UNESCO Institute for Statistics.
#> 17596 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17597 UNESCO Institute for Statistics
#> 17598 UNESCO Institute for Statistics.
#> 17599 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17600 UNESCO Institute for Statistics.
#> 17601 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17602 UNESCO Institute for Statistics
#> 17603 UNESCO Institute for Statistics.
#> 17604 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17605 UNESCO Institute for Statistics.
#> 17606 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17607 UNESCO Institute for Statistics
#> 17608 UNESCO Institute for Statistics.
#> 17609 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17610 UNESCO Institute for Statistics.
#> 17611 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17612 UNESCO Institute for Statistics
#> 17613 UNESCO Institute for Statistics.
#> 17614 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17615 UNESCO Institute for Statistics.
#> 17616 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17617 UNESCO Institute for Statistics
#> 17618 UNESCO Institute for Statistics.
#> 17619 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17620 UNESCO Institute for Statistics.
#> 17621 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17622 UNESCO Institute for Statistics
#> 17623 UNESCO Institute for Statistics.
#> 17624 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17625 UNESCO Institute for Statistics.
#> 17626 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17627 UNESCO Institute for Statistics
#> 17628 UNESCO Institute for Statistics.
#> 17629 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17630 UNESCO Institute for Statistics.
#> 17631 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17632 UNESCO Institute for Statistics
#> 17633 UNESCO Institute for Statistics.
#> 17634 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17635 UNESCO Institute for Statistics.
#> 17636 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17637 UNESCO Institute for Statistics
#> 17638 UNESCO Institute for Statistics.
#> 17639 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17640 UNESCO Institute for Statistics.
#> 17641 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17642 UNESCO Institute for Statistics
#> 17643 UNESCO Institute for Statistics.
#> 17644 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17645 UNESCO Institute for Statistics.
#> 17646 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17647 UNESCO Institute for Statistics
#> 17648 UNESCO Institute for Statistics.
#> 17649 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17650 UNESCO Institute for Statistics.
#> 17651 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17652 UNESCO Institute for Statistics
#> 17653 UNESCO Institute for Statistics.
#> 17654 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17655 UNESCO Institute for Statistics.
#> 17656 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17657 UNESCO Institute for Statistics
#> 17658 UNESCO Institute for Statistics.
#> 17659 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17660 UNESCO Institute for Statistics.
#> 17661 United Nations Population Division. World Population Prospects: 2019 Revision.
#> 17662 UNESCO Institute for Statistics
#> 17663 UNESCO Institute for Statistics.
#> 17665 World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17666 World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17667 World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17668 Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2019 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.
#> 17669
#> 17674 (1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.
#> 17675 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17676 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17677
#> 17678
#> 17679 World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17680 World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2019 Revision.
#> 17681 1. Census reports and statistical databases from national statistical offices 2. Estimates from the Center for International Earth Science Information Network (CIESIN), The Earth Institute at Columbia University
#> 17682 UNESCO Institute for Statistics
#> 17683 UNESCO Institute for Statistics
#> 17684 UNESCO Institute for Statistics
#> 17685 UNESCO Institute for Statistics
#> 17686 UNESCO Institute for Statistics
#> 17687 UNESCO Institute for Statistics
#> 17688 UNESCO Institute for Statistics
#> 17689 UNESCO Institute for Statistics
#> 17690 UNESCO Institute for Statistics
#> 17702 World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.
#> 17703 The United Nations Population Division's World Urbanization Prospects.
#> 17704 The United Nations Population Division's World Urbanization Prospects.
#> 17705 World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.
#> 17706 World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.
#> 17707 UNESCO Institute for Statistics
#> 17708 UNESCO Institute for Statistics
#> 17709 UNESCO Institute for Statistics
#> 17710 UNESCO Institute for Statistics
#> 17711 UNESCO Institute for Statistics
#> 17712 UNESCO Institute for Statistics
#> 17713 UNESCO Institute for Statistics
#> 17714 UNESCO Institute for Statistics
#> 17715 UNESCO Institute for Statistics
#> 17716 UNESCO Institute for Statistics
#> 17717 UNESCO Institute for Statistics
#> 17718 UNESCO Institute for Statistics
#> 17719 World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.
#> 17720
#> 17721
#> 17722
#> 17723
#> 17724 World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.
#> 17725 The United Nations Population Division's World Urbanization Prospects.
#> 17726 United Nations Population Division. World Urbanization Prospects: 2018 Revision.
#> 17727 The United Nations Population Division's World Urbanization Prospects.
#> 17728 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 17771 Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators)
#> 18453 UNESCO Institute for Statistics
#> 18454 UNESCO Institute for Statistics
#> 18455 UNESCO Institute for Statistics
#> 18456 UNESCO Institute for Statistics
#> 18457 UNESCO Institute for Statistics
#> 18458 UNESCO Institute for Statistics
#> 18459
#> 18460 UNESCO Institute for Statistics
#> 18461 UNESCO Institute for Statistics
#> 18462 UNESCO Institute for Statistics
#> 18463 UNESCO Institute for Statistics
#> 18464 UNESCO Institute for Statistics
#> 18465 UNESCO Institute for Statistics
#> 18466
#> 18467 UNESCO Institute for Statistics
#> 18468 UNESCO Institute for Statistics
#> 18469 UNESCO Institute for Statistics
#> 18470 UNESCO Institute for Statistics
#> 18471 UNESCO Institute for Statistics
#> 18472 UNESCO Institute for Statistics
#> 18473
#> 18474 UNESCO Institute for Statistics
#> 18475 UNESCO Institute for Statistics
#> 18476 UNESCO Institute for Statistics
#> 18477 UNESCO Institute for Statistics
#> 18478 UNESCO Institute for Statistics
#> 18479 UNESCO Institute for Statistics
#> 18480
#> 18481 UNESCO Institute for Statistics
#> 18482 UNESCO Institute for Statistics
#> 18483 UNESCO Institute for Statistics
#> 18484 UNESCO Institute for Statistics
#> 18485 UNESCO Institute for Statistics
#> 18486
#> 18487 UNESCO Institute for Statistics
#> 18488 UNESCO Institute for Statistics
#> 18489 UNESCO Institute for Statistics
#> 18490 UNESCO Institute for Statistics
#> 18491 UNESCO Institute for Statistics
#> 18492 UNESCO Institute for Statistics
#> 18493
#> 18494 UNESCO Institute for Statistics
#> 18495 UNESCO Institute for Statistics
#> 18496 UNESCO Institute for Statistics
#> 18497 UNESCO Institute for Statistics
#> 18498 UNESCO Institute for Statistics
#> 18499 UNESCO Institute for Statistics
#> 18500
#> 18501 UNESCO Institute for Statistics
#> 18502 UNESCO Institute for Statistics
#> 18503 UNESCO Institute for Statistics
#> 18504
#> 18505 UNESCO Institute for Statistics
#> 18506 UNESCO Institute for Statistics
#> 18507 UNESCO Institute for Statistics
#> 18508 UNESCO Institute for Statistics
#> 18509 UNESCO Institute for Statistics
#> 18510 UNESCO Institute for Statistics
#> 18511 UNESCO Institute for Statistics
#> 18512 UNESCO Institute for Statistics
#> 18513 UNESCO Institute for Statistics
#> 18514 UNESCO Institute for Statistics
#> 18515
#> 18516
#> 18517
#> 18518
#> 18519 UNESCO Institute for Statistics
#> 18520 UNESCO Institute for Statistics
#> 18521 UNESCO Institute for Statistics
#> 18776 UNESCO Institute for Statistics
#> 18777 UNESCO Institute for Statistics
#> 18778 UNESCO Institute for Statistics
#> 18779 UNESCO Institute for Statistics
#> 18780 UNESCO Institute for Statistics
#> 18781 UNESCO Institute for Statistics
#> 18782 UNESCO Institute for Statistics
#> 18783 UNESCO Institute for Statistics
#> 18784 UNESCO Institute for Statistics
#> 18785 UNESCO Institute for Statistics
#> 18786 UNESCO Institute for Statistics
#> 18787 UNESCO Institute for Statistics
#> 18788 UNESCO Institute for Statistics
#> 18789 UNESCO Institute for Statistics
#> 18790 UNESCO Institute for Statistics
#> 18791 UNESCO Institute for Statistics
#> 18792
#> 18793
#> 18794
#> 18795
#> 18796
#> 18797
#> 18798
#> 18799
#> 18800
#> 18801
#> 18802
#> 18803
#> 18804
#> 18805
#> 18806
#> 18807
#> 18808
#> 18809
#> 18810
#> 18811
#> 18812
#> 18813
#> 18814
#> 18815
#> 18816 UNESCO Institute for Statistics
#> 18817 UNESCO Institute for Statistics
#> 18818
#> 18819
#> 18820
#> 18821 UNESCO Institute for Statistics
#> 18822
#> 18823
#> 18824
#> 18825
#> 18826
#> 18827
#> 18828
#> 18829
#> 18830
#> 18831 UNESCO Institute for Statistics
#> 18832 UNESCO Institute for Statistics
#> 18833 UNESCO Institute for Statistics
#> 18834
#> 18835
#> 18836
#> 18837
#> 18838
#> 18839
#> 18840
#> 18841
#> 18842
#> 18843
#> 18844
#> 18845
#> 19358
#> 19359
#> 19360
#> 19876
#> 19877
#> 19878
#> 19879
#> 19880
#> 19881
#> 19882
#> 19883
#> 19884
#> 19885 UNESCO Institute for Statistics
#> 19886 UNESCO Institute for Statistics
#> 19887 UNESCO Institute for Statistics
#> 19888 UNESCO Institute for Statistics
#> 19889 UNESCO Institute for Statistics
#> 19890 UNESCO Institute for Statistics
#> 19891 UNESCO Institute for Statistics
#> 19892 UNESCO Institute for Statistics
#> 19893 UNESCO Institute for Statistics
#> 19894 UNESCO Institute for Statistics
#> 19895 UNESCO Institute for Statistics
#> 19896 UNESCO Institute for Statistics
#> 20141
#> 20142
#> 20143
#> 20144
#> 20145
#> 20146
#> 20147
#> 20148
#> 20149
#> 20150
#> 20151
#> 20152
#> 20153
#> 20154
#> 20155
#> 20156
#> 20157
#> 20158
#> 20159
#> 20160WDIsearch(string = "NY.GDP.PCAP.KD",
field = "indicator", short = FALSE, cache = NULL)WDIsearch(string = "gdp",
field = "name", short = TRUE, cache = NULL) 7.8 WDIcache
Download an updated list of available WDI indicators from the World Bank website. Returns a list for use in the WDIsearch function.
wdi_cache <- WDIcache()Downloading all series information from the World Bank website can take time. The WDI package ships with a local data object with information on all the series available on 2012-06-18. You can update this database by retrieving a new list using WDIcache, and then feeding the resulting object to WDIsearch via the cache argument.
glimpse(wdi_cache)
#> List of 2
#> $ series :'data.frame': 21034 obs. of 5 variables:
#> ..$ indicator : chr [1:21034] "1.0.HCount.1.90usd" "1.0.HCount.2.5usd" "1.0.HCount.Mid10to50" "1.0.HCount.Ofcl" ...
#> ..$ name : chr [1:21034] "Poverty Headcount ($1.90 a day)" "Poverty Headcount ($2.50 a day)" "Middle Class ($10-50 a day) Headcount" "Official Moderate Poverty Rate-National" ...
#> ..$ description : chr [1:21034] "The poverty headcount index measures the proportion of the population with daily per capita income (in 2011 PPP"| __truncated__ "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP"| __truncated__ "The poverty headcount index measures the proportion of the population with daily per capita income (in 2005 PPP"| __truncated__ "The poverty headcount index measures the proportion of the population with daily per capita income below the of"| __truncated__ ...
#> ..$ sourceDatabase : chr [1:21034] "LAC Equity Lab" "LAC Equity Lab" "LAC Equity Lab" "LAC Equity Lab" ...
#> ..$ sourceOrganization: chr [1:21034] "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." "LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)." "LAC Equity Lab tabulations of data from National Statistical Offices." ...
#> $ country:'data.frame': 299 obs. of 9 variables:
#> ..$ iso3c : chr [1:299] "ABW" "AFE" "AFG" "AFR" ...
#> ..$ iso2c : chr [1:299] "AW" "ZH" "AF" "A9" ...
#> ..$ country : chr [1:299] "Aruba" "Africa Eastern and Southern" "Afghanistan" "Africa" ...
#> ..$ region : chr [1:299] "Latin America & Caribbean" "Aggregates" "South Asia" "Aggregates" ...
#> ..$ capital : chr [1:299] "Oranjestad" "" "Kabul" "" ...
#> ..$ longitude: chr [1:299] "-70.0167" "" "69.1761" "" ...
#> ..$ latitude : chr [1:299] "12.5167" "" "34.5228" "" ...
#> ..$ income : chr [1:299] "High income" "Aggregates" "Low income" "Aggregates" ...
#> ..$ lending : chr [1:299] "Not classified" "Aggregates" "IDA" "Aggregates" ...
WDIsearch(string = "gdp",
field = "name", short = FALSE, cache = wdi_cache)
#> indicator
#> 712 5.51.01.10.gdp
#> 714 6.0.GDP_current
#> 715 6.0.GDP_growth
#> 716 6.0.GDP_usd
#> 717 6.0.GDPpc_constant
#> 1558 BG.GSR.NFSV.GD.ZS
#> 1559 BG.KAC.FNEI.GD.PP.ZS
#> 1560 BG.KAC.FNEI.GD.ZS
#> 1561 BG.KLT.DINV.GD.PP.ZS
#> 1562 BG.KLT.DINV.GD.ZS
#> 1863 BI.WAG.TOTL.GD.ZS
#> 1883 BM.GSR.MRCH.ZS
#> 1895 BM.KLT.DINV.GD.ZS
#> 1896 BM.KLT.DINV.WD.GD.ZS
#> 1909 BN.CAB.XOKA.GD.ZS
#> 1910 BN.CAB.XOKA.GDP.ZS
#> 1913 BN.CAB.XOTR.ZS
#> 1916 BN.CUR.GDPM.ZS
#> 1922 BN.GSR.FCTY.CD.ZS
#> 1931 BN.KLT.DINV.CD.ZS
#> 1933 BN.KLT.DINV.DRS.GDP.ZS
#> 1939 BN.KLT.PRVT.GD.ZS
#> 1950 BN.TRF.CURR.CD.ZS
#> 1999 BX.GSR.MRCH.ZS
#> 2011 BX.KLT.DINV.DT.GD.ZS
#> 2013 BX.KLT.DINV.WD.GD.ZS
#> 2022 BX.TRF.MGR.DT.GD.ZS
#> 2028 BX.TRF.PWKR.DT.GD.ZS
#> 2029 BX.TRF.PWKR.GD.ZS
#> 2326 CC.ENTX.ENE.ZS
#> 2327 CC.ENTX.ENV.ZS
#> 2402 CC.GHG.MEMG.EI
#> 2403 CC.GHG.MEMG.GC
#> 2426 CC.INCP.ALRS
#> 2427 CC.INCP.KRGC
#> 2428 CC.INCP.SPMC
#> 2491 CC.RISK.AST.ZS
#> 2492 CC.RISK.WELL.ZS
#> 2501 CC.SP.EXP.ZS
#> 2543 CM.FIN.INTL.GD.ZS
#> 2546 CM.MKT.LCAP.GD.ZS
#> 2549 CM.MKT.TRAD.GD.ZS
#> 2701 DP.DOD.DECD.CR.BC.Z1
#> 2704 DP.DOD.DECD.CR.CG.Z1
#> 2707 DP.DOD.DECD.CR.FC.Z1
#> 2710 DP.DOD.DECD.CR.GG.Z1
#> 2713 DP.DOD.DECD.CR.NF.Z1
#> 2718 DP.DOD.DECF.CR.BC.Z1
#> 2721 DP.DOD.DECF.CR.CG.Z1
#> 2724 DP.DOD.DECF.CR.FC.Z1
#> 2727 DP.DOD.DECF.CR.GG.Z1
#> 2730 DP.DOD.DECF.CR.NF.Z1
#> 2735 DP.DOD.DECN.CR.BC.Z1
#> 2738 DP.DOD.DECN.CR.CG.Z1
#> 2741 DP.DOD.DECN.CR.FC.Z1
#> 2744 DP.DOD.DECN.CR.GG.Z1
#> 2747 DP.DOD.DECN.CR.NF.Z1
#> 2752 DP.DOD.DECT.CR.BC.Z1
#> 2755 DP.DOD.DECT.CR.CG.Z1
#> 2758 DP.DOD.DECT.CR.FC.Z1
#> 2761 DP.DOD.DECT.CR.GG.Z1
#> 2764 DP.DOD.DECT.CR.NF.Z1
#> 2769 DP.DOD.DECX.CR.BC.Z1
#> 2772 DP.DOD.DECX.CR.CG.Z1
#> 2775 DP.DOD.DECX.CR.FC.Z1
#> 2778 DP.DOD.DECX.CR.GG.Z1
#> 2781 DP.DOD.DECX.CR.NF.Z1
#> 2786 DP.DOD.DLCD.CR.BC.Z1
#> 2789 DP.DOD.DLCD.CR.CG.Z1
#> 2792 DP.DOD.DLCD.CR.FC.Z1
#> 2795 DP.DOD.DLCD.CR.GG.Z1
#> 2798 DP.DOD.DLCD.CR.L1.BC.Z1
#> 2801 DP.DOD.DLCD.CR.L1.CG.Z1
#> 2804 DP.DOD.DLCD.CR.L1.FC.Z1
#> 2807 DP.DOD.DLCD.CR.L1.GG.Z1
#> 2810 DP.DOD.DLCD.CR.L1.NF.Z1
#> 2815 DP.DOD.DLCD.CR.M1.BC.Z1
#> 2818 DP.DOD.DLCD.CR.M1.CG.Z1
#> 2821 DP.DOD.DLCD.CR.M1.FC.Z1
#> 2824 DP.DOD.DLCD.CR.M1.GG.Z1
#> 2827 DP.DOD.DLCD.CR.M1.NF.Z1
#> 2832 DP.DOD.DLCD.CR.NF.Z1
#> 2836 DP.DOD.DLD1.CR.CG.Z1
#> 2838 DP.DOD.DLD1.CR.GG.Z1
#> 2840 DP.DOD.DLD2.CR.CG.Z1
#> 2842 DP.DOD.DLD2.CR.GG.Z1
#> 2844 DP.DOD.DLD2A.CR.CG.Z1
#> 2846 DP.DOD.DLD2A.CR.GG.Z1
#> 2848 DP.DOD.DLD3.CR.CG.Z1
#> 2850 DP.DOD.DLD3.CR.GG.Z1
#> 2852 DP.DOD.DLD4.CR.CG.Z1
#> 2854 DP.DOD.DLD4.CR.GG.Z1
#> 2857 DP.DOD.DLDS.CR.BC.Z1
#> 2860 DP.DOD.DLDS.CR.CG.Z1
#> 2863 DP.DOD.DLDS.CR.FC.Z1
#> 2866 DP.DOD.DLDS.CR.GG.Z1
#> 2869 DP.DOD.DLDS.CR.L1.BC.Z1
#> 2872 DP.DOD.DLDS.CR.L1.CG.Z1
#> 2875 DP.DOD.DLDS.CR.L1.FC.Z1
#> 2878 DP.DOD.DLDS.CR.L1.GG.Z1
#> 2881 DP.DOD.DLDS.CR.L1.NF.Z1
#> 2886 DP.DOD.DLDS.CR.M1.BC.Z1
#> 2889 DP.DOD.DLDS.CR.M1.CG.Z1
#> 2892 DP.DOD.DLDS.CR.M1.FC.Z1
#> 2895 DP.DOD.DLDS.CR.M1.GG.Z1
#> 2898 DP.DOD.DLDS.CR.M1.NF.Z1
#> 2903 DP.DOD.DLDS.CR.MV.BC.Z1
#> 2906 DP.DOD.DLDS.CR.MV.CG.Z1
#> 2909 DP.DOD.DLDS.CR.MV.FC.Z1
#> 2912 DP.DOD.DLDS.CR.MV.GG.Z1
#> 2915 DP.DOD.DLDS.CR.MV.NF.Z1
#> 2920 DP.DOD.DLDS.CR.NF.Z1
#> 2925 DP.DOD.DLIN.CR.BC.Z1
#> 2928 DP.DOD.DLIN.CR.CG.Z1
#> 2931 DP.DOD.DLIN.CR.FC.Z1
#> 2934 DP.DOD.DLIN.CR.GG.Z1
#> 2937 DP.DOD.DLIN.CR.L1.BC.Z1
#> 2940 DP.DOD.DLIN.CR.L1.CG.Z1
#> 2943 DP.DOD.DLIN.CR.L1.FC.Z1
#> 2946 DP.DOD.DLIN.CR.L1.GG.Z1
#> 2949 DP.DOD.DLIN.CR.L1.NF.Z1
#> 2954 DP.DOD.DLIN.CR.M1.BC.Z1
#> 2957 DP.DOD.DLIN.CR.M1.CG.Z1
#> 2960 DP.DOD.DLIN.CR.M1.FC.Z1
#> 2963 DP.DOD.DLIN.CR.M1.GG.Z1
#> 2966 DP.DOD.DLIN.CR.M1.NF.Z1
#> 2971 DP.DOD.DLIN.CR.NF.Z1
#> 2976 DP.DOD.DLLO.CR.BC.Z1
#> 2979 DP.DOD.DLLO.CR.CG.Z1
#> 2982 DP.DOD.DLLO.CR.FC.Z1
#> 2985 DP.DOD.DLLO.CR.GG.Z1
#> 2988 DP.DOD.DLLO.CR.L1.BC.Z1
#> 2991 DP.DOD.DLLO.CR.L1.CG.Z1
#> 2994 DP.DOD.DLLO.CR.L1.FC.Z1
#> 2997 DP.DOD.DLLO.CR.L1.GG.Z1
#> 3000 DP.DOD.DLLO.CR.L1.NF.Z1
#> 3005 DP.DOD.DLLO.CR.M1.BC.Z1
#> 3008 DP.DOD.DLLO.CR.M1.CG.Z1
#> 3011 DP.DOD.DLLO.CR.M1.FC.Z1
#> 3014 DP.DOD.DLLO.CR.M1.GG.Z1
#> 3017 DP.DOD.DLLO.CR.M1.NF.Z1
#> 3022 DP.DOD.DLLO.CR.NF.Z1
#> 3027 DP.DOD.DLOA.CR.BC.Z1
#> 3030 DP.DOD.DLOA.CR.CG.Z1
#> 3033 DP.DOD.DLOA.CR.FC.Z1
#> 3036 DP.DOD.DLOA.CR.GG.Z1
#> 3039 DP.DOD.DLOA.CR.L1.BC.Z1
#> 3042 DP.DOD.DLOA.CR.L1.CG.Z1
#> 3045 DP.DOD.DLOA.CR.L1.FC.Z1
#> 3048 DP.DOD.DLOA.CR.L1.GG.Z1
#> 3051 DP.DOD.DLOA.CR.L1.NF.Z1
#> 3056 DP.DOD.DLOA.CR.M1.BC.Z1
#> 3059 DP.DOD.DLOA.CR.M1.CG.Z1
#> 3062 DP.DOD.DLOA.CR.M1.FC.Z1
#> 3065 DP.DOD.DLOA.CR.M1.GG.Z1
#> 3068 DP.DOD.DLOA.CR.M1.NF.Z1
#> 3073 DP.DOD.DLOA.CR.NF.Z1
#> 3078 DP.DOD.DLSD.CR.BC.Z1
#> 3081 DP.DOD.DLSD.CR.CG.Z1
#> 3084 DP.DOD.DLSD.CR.FC.Z1
#> 3087 DP.DOD.DLSD.CR.GG.Z1
#> 3090 DP.DOD.DLSD.CR.M1.BC.Z1
#> 3093 DP.DOD.DLSD.CR.M1.CG.Z1
#> 3096 DP.DOD.DLSD.CR.M1.FC.Z1
#> 3099 DP.DOD.DLSD.CR.M1.GG.Z1
#> 3102 DP.DOD.DLSD.CR.M1.NF.Z1
#> 3107 DP.DOD.DLSD.CR.NF.Z1
#> 3112 DP.DOD.DLTC.CR.BC.Z1
#> 3115 DP.DOD.DLTC.CR.CG.Z1
#> 3118 DP.DOD.DLTC.CR.FC.Z1
#> 3121 DP.DOD.DLTC.CR.GG.Z1
#> 3124 DP.DOD.DLTC.CR.L1.BC.Z1
#> 3127 DP.DOD.DLTC.CR.L1.CG.Z1
#> 3130 DP.DOD.DLTC.CR.L1.FC.Z1
#> 3133 DP.DOD.DLTC.CR.L1.GG.Z1
#> 3136 DP.DOD.DLTC.CR.L1.NF.Z1
#> 3141 DP.DOD.DLTC.CR.M1.BC.Z1
#> 3144 DP.DOD.DLTC.CR.M1.CG.Z1
#> 3147 DP.DOD.DLTC.CR.M1.FC.Z1
#> 3150 DP.DOD.DLTC.CR.M1.GG.Z1
#> 3153 DP.DOD.DLTC.CR.M1.NF.Z1
#> 3158 DP.DOD.DLTC.CR.NF.Z1
#> 3164 DP.DOD.DSCD.CR.BC.Z1
#> 3167 DP.DOD.DSCD.CR.CG.Z1
#> 3170 DP.DOD.DSCD.CR.FC.Z1
#> 3173 DP.DOD.DSCD.CR.GG.Z1
#> 3176 DP.DOD.DSCD.CR.NF.Z1
#> 3181 DP.DOD.DSDS.CR.BC.Z1
#> 3184 DP.DOD.DSDS.CR.CG.Z1
#> 3187 DP.DOD.DSDS.CR.FC.Z1
#> 3190 DP.DOD.DSDS.CR.GG.Z1
#> 3193 DP.DOD.DSDS.CR.NF.Z1
#> 3198 DP.DOD.DSIN.CR.BC.Z1
#> 3201 DP.DOD.DSIN.CR.CG.Z1
#> 3204 DP.DOD.DSIN.CR.FC.Z1
#> 3207 DP.DOD.DSIN.CR.GG.Z1
#> 3210 DP.DOD.DSIN.CR.NF.Z1
#> 3215 DP.DOD.DSLO.CR.BC.Z1
#> 3218 DP.DOD.DSLO.CR.CG.Z1
#> 3221 DP.DOD.DSLO.CR.FC.Z1
#> 3224 DP.DOD.DSLO.CR.GG.Z1
#> 3227 DP.DOD.DSLO.CR.NF.Z1
#> 3232 DP.DOD.DSOA.CR.BC.Z1
#> 3235 DP.DOD.DSOA.CR.CG.Z1
#> 3238 DP.DOD.DSOA.CR.FC.Z1
#> 3241 DP.DOD.DSOA.CR.GG.Z1
#> 3244 DP.DOD.DSOA.CR.NF.Z1
#> 3249 DP.DOD.DSTC.CR.BC.Z1
#> 3252 DP.DOD.DSTC.CR.CG.Z1
#> 3255 DP.DOD.DSTC.CR.FC.Z1
#> 3258 DP.DOD.DSTC.CR.GG.Z1
#> 3261 DP.DOD.DSTC.CR.NF.Z1
#> 3757 DT.DOD.ALLC.ZSG
#> 3760 DT.DOD.ALLN.ZSG
#> 3915 DT.DOD.DECT.CD.ZSG
#> 5518 DT.ODA.ALLD.GD.ZS
#> 5589 DT.ODA.DACD.ZSG
#> 5594 DT.ODA.MULT.ZSG
#> 5602 DT.ODA.NDAC.ZSG
#> 5608 DT.ODA.ODAT.GD.ZS
#> 5758 DT.TDS.DECT.GD.ZS
#> 6110 EG.EGY.PRIM.PP.KD
#> 6134 EG.GDP.PUSE.KO.87
#> 6135 EG.GDP.PUSE.KO.KD
#> 6136 EG.GDP.PUSE.KO.PP
#> 6137 EG.GDP.PUSE.KO.PP.KD
#> 6145 EG.USE.COMM.GD.PP.KD
#> 6164 EN.ATM.CO2E.GDP
#> 6168 EN.ATM.CO2E.KD.87.GD
#> 6169 EN.ATM.CO2E.KD.GD
#> 6174 EN.ATM.CO2E.PP.GD
#> 6175 EN.ATM.CO2E.PP.GD.KD
#> 6305 ER.GDP.FWTL.M3.KD
#> 6323 EU.EGY.USES.GDP
#> 6377 FB.DPT.INSU.PC.ZS
#> 6730 FD.AST.PRVT.GD.ZS
#> 6736 FI.RES.TOTL.CD.ZS
#> 8052 FM.AST.GOVT.CN.ZS
#> 8061 FM.AST.PRVT.GD.ZS
#> 8070 FM.LBL.BMNY.GD.ZS
#> 8077 FM.LBL.MQMY.GD.ZS
#> 8078 FM.LBL.MQMY.GDP.ZS
#> 8080 FM.LBL.MQMY.XD
#> 8085 FM.LBL.QMNY.GDP.ZS
#> 8086 FM.LBL.SEIG.GDP.ZS
#> 8125 FS.AST.CGOV.GD.ZS
#> 8126 FS.AST.DOMO.GD.ZS
#> 8127 FS.AST.DOMS.GD.ZS
#> 8128 FS.AST.DTOT.ZS
#> 8130 FS.AST.PRVT.GD.ZS
#> 8131 FS.AST.PRVT.GDP.ZS
#> 8132 FS.LBL.LIQU.GD.ZS
#> 8133 FS.LBL.LIQU.GDP.ZS
#> 8134 FS.LBL.QLIQ.GD.ZS
#> 8205 GB.BAL.OVRL.GD.ZS
#> 8206 GB.BAL.OVRL.GDP.ZS
#> 8215 GB.DOD.TOTL.GD.ZS
#> 8216 GB.DOD.TOTL.GDP.ZS
#> 8220 GB.FIN.ABRD.GD.ZS
#> 8221 GB.FIN.ABRD.GDP.ZS
#> 8225 GB.FIN.DOMS.GD.ZS
#> 8226 GB.FIN.DOMS.GDP.ZS
#> 8236 GB.REV.CTOT.GD.ZS
#> 8239 GB.REV.TOTL.GDP.ZS
#> 8241 GB.REV.XAGT.CN.ZS
#> 8244 GB.RVC.TOTL.GD.ZS
#> 8246 GB.SOE.DECT.ZS
#> 8248 GB.SOE.ECON.GD.ZS
#> 8249 GB.SOE.ECON.GDP.ZS
#> 8252 GB.SOE.NFLW.GD.ZS
#> 8253 GB.SOE.NFLW.GDP.ZS
#> 8254 GB.SOE.OVRL.GD.ZS
#> 8280 GB.TAX.TOTL.GD.ZS
#> 8281 GB.TAX.TOTL.GDP.ZS
#> 8299 GB.XPD.DEFN.GDP.ZS
#> 8302 GB.XPD.RSDV.GD.ZS
#> 8305 GB.XPD.TOTL.GD.ZS
#> 8306 GB.XPD.TOTL.GDP.ZS
#> 8313 GC.AST.TOTL.GD.ZS
#> 8316 GC.BAL.CASH.GD.ZS
#> 8321 GC.DOD.TOTL.GD.ZS
#> 8325 GC.FIN.DOMS.GD.ZS
#> 8327 GC.FIN.FRGN.GD.ZS
#> 8329 GC.LBL.TOTL.GD.ZS
#> 8331 GC.NFN.TOTL.GD.ZS
#> 8333 GC.NLD.TOTL.GD.ZS
#> 8344 GC.REV.XGRT.GD.ZS
#> 8359 GC.TAX.TOTL.GD.ZS
#> 8376 GC.XPN.TOTL.GD.ZS
#> 8466 GFDD.DI.01
#> 8467 GFDD.DI.02
#> 8468 GFDD.DI.03
#> 8470 GFDD.DI.05
#> 8471 GFDD.DI.06
#> 8472 GFDD.DI.07
#> 8473 GFDD.DI.08
#> 8474 GFDD.DI.09
#> 8475 GFDD.DI.10
#> 8476 GFDD.DI.11
#> 8477 GFDD.DI.12
#> 8478 GFDD.DI.13
#> 8479 GFDD.DI.14
#> 8480 GFDD.DM.01
#> 8481 GFDD.DM.02
#> 8482 GFDD.DM.03
#> 8483 GFDD.DM.04
#> 8484 GFDD.DM.05
#> 8485 GFDD.DM.06
#> 8486 GFDD.DM.07
#> 8487 GFDD.DM.08
#> 8488 GFDD.DM.09
#> 8489 GFDD.DM.10
#> 8490 GFDD.DM.11
#> 8491 GFDD.DM.12
#> 8492 GFDD.DM.13
#> 8495 GFDD.DM.16
#> 8503 GFDD.EI.08
#> 8508 GFDD.OI.02
#> 8511 GFDD.OI.08
#> 8512 GFDD.OI.09
#> 8516 GFDD.OI.13
#> 8517 GFDD.OI.14
#> 8521 GFDD.OI.17
#> 8522 GFDD.OI.18
#> 9365 IE.ICT.TOTL.GD.ZS
#> 9639 IS.RRS.GOOD.KM.PP.ZS
#> 9641 IS.RRS.PASG.K2.PP.ZS
#> 9748 IT.TEL.REVN.GD.ZS
#> 11639 MS.MIL.XPND.GD.ZS
#> 11644 NA.GDP.ACC.FB.SNA08.CR
#> 11645 NA.GDP.ACC.FB.SNA08.KR
#> 11646 NA.GDP.AGR.CR
#> 11647 NA.GDP.AGR.KR
#> 11648 NA.GDP.AGR.SNA08.CR
#> 11649 NA.GDP.AGR.SNA08.KR
#> 11650 NA.GDP.BUSS.SNA08.CR
#> 11651 NA.GDP.BUSS.SNA08.KR
#> 11652 NA.GDP.CNST.CR
#> 11653 NA.GDP.CNST.KR
#> 11654 NA.GDP.CNST.SNA08.CR
#> 11655 NA.GDP.CNST.SNA08.KR
#> 11656 NA.GDP.EDUS.SNA08.CR
#> 11657 NA.GDP.EDUS.SNA08.KR
#> 11658 NA.GDP.ELEC.GAS.SNA08.CR
#> 11659 NA.GDP.ELEC.GAS.SNA08.KR
#> 11660 NA.GDP.EXC.OG.CR
#> 11661 NA.GDP.EXC.OG.KR
#> 11662 NA.GDP.FINS.CR
#> 11663 NA.GDP.FINS.KR
#> 11664 NA.GDP.FINS.SNA08.CR
#> 11665 NA.GDP.FINS.SNA08.KR
#> 11666 NA.GDP.HLTH.SOCW.SNA08.CR
#> 11667 NA.GDP.HLTH.SOCW.SNA08.KR
#> 11668 NA.GDP.INC.OG.CR
#> 11669 NA.GDP.INC.OG.KR
#> 11670 NA.GDP.INC.OG.SNA08.CR
#> 11671 NA.GDP.INC.OG.SNA08.KR
#> 11672 NA.GDP.INF.COMM.SNA08.CR
#> 11673 NA.GDP.INF.COMM.SNA08.KR
#> 11674 NA.GDP.MINQ.CR
#> 11675 NA.GDP.MINQ.KR
#> 11676 NA.GDP.MINQ.SNA08.CR
#> 11677 NA.GDP.MINQ.SNA08.KR
#> 11678 NA.GDP.MNF.CR
#> 11679 NA.GDP.MNF.KR
#> 11680 NA.GDP.MNF.SNA08.CR
#> 11681 NA.GDP.MNF.SNA08.KR
#> 11682 NA.GDP.PADM.DEF.SNA08.CR
#> 11683 NA.GDP.PADM.DEF.SNA08.KR
#> 11684 NA.GDP.REST.SNA08.CR
#> 11685 NA.GDP.REST.SNA08.KR
#> 11686 NA.GDP.SRV.OTHR.CR
#> 11687 NA.GDP.SRV.OTHR.KR
#> 11688 NA.GDP.SRV.OTHR.SNA08.CR
#> 11689 NA.GDP.SRV.OTHR.SNA08.KR
#> 11690 NA.GDP.TRAN.COMM.CR
#> 11691 NA.GDP.TRAN.COMM.KR
#> 11692 NA.GDP.TRAN.STOR.SNA08.CR
#> 11693 NA.GDP.TRAN.STOR.SNA08.KR
#> 11694 NA.GDP.TRD.HTL.CR
#> 11695 NA.GDP.TRD.HTL.KR
#> 11696 NA.GDP.TRD.SNA08.CR
#> 11697 NA.GDP.TRD.SNA08.KR
#> 11698 NA.GDP.UTL.CR
#> 11699 NA.GDP.UTL.KR
#> 11700 NA.GDP.WTR.WST.SNA08.CR
#> 11701 NA.GDP.WTR.WST.SNA08.KR
#> 11710 NE.CON.GOVT.ZS
#> 11721 NE.CON.PETC.ZS
#> 11738 NE.CON.PRVT.ZS
#> 11748 NE.CON.TETC.ZS
#> 11756 NE.CON.TOTL.ZG
#> 11757 NE.CON.TOTL.ZS
#> 11767 NE.DAB.TOTL.ZS
#> 11779 NE.EXP.GNFS.ZS
#> 11781 NE.GDI.CON.GOVT.CR
#> 11782 NE.GDI.CON.GOVT.SNA08.CR
#> 11783 NE.GDI.CON.NPI.CR
#> 11784 NE.GDI.CON.NPI.SNA08.CR
#> 11785 NE.GDI.CON.PRVT.CR
#> 11786 NE.GDI.CON.PRVT.SNA08.CR
#> 11787 NE.GDI.EXPT.CR
#> 11788 NE.GDI.EXPT.SNA08.CR
#> 11813 NE.GDI.FPRV.ZS
#> 11818 NE.GDI.FPUB.ZS
#> 11821 NE.GDI.FTOT.CR
#> 11828 NE.GDI.FTOT.SNA08.CR
#> 11829 NE.GDI.FTOT.ZS
#> 11830 NE.GDI.IMPT.CR
#> 11831 NE.GDI.IMPT.SNA08.CR
#> 11832 NE.GDI.INEX.SNA08.CR
#> 11837 NE.GDI.STKB.CR
#> 11841 NE.GDI.STKB.SNA08.CR
#> 11850 NE.GDI.TOTL.CR
#> 11857 NE.GDI.TOTL.SNA08.CR
#> 11858 NE.GDI.TOTL.ZG
#> 11859 NE.GDI.TOTL.ZS
#> 11869 NE.IMP.GNFS.ZS
#> 11870 NE.MRCH.GDP.ZS
#> 11876 NE.RSB.GNFS.ZG
#> 11877 NE.RSB.GNFS.ZS
#> 11880 NE.TRD.GNFS.ZS
#> 11887 NP.AGR.TOTL.ZG
#> 11891 NP.IND.TOTL.ZG
#> 11897 NP.SRV.TOTL.ZG
#> 11905 NV.AGR.PCAP.KD.ZG
#> 11915 NV.AGR.TOTL.ZG
#> 11916 NV.AGR.TOTL.ZS
#> 11936 NV.IND.MANF.ZS
#> 11950 NV.IND.TOTL.ZG
#> 11951 NV.IND.TOTL.ZS
#> 11969 NV.SRV.DISC.CD
#> 11970 NV.SRV.DISC.CN
#> 11971 NV.SRV.DISC.KN
#> 11988 NV.SRV.TETC.ZG
#> 11989 NV.SRV.TETC.ZS
#> 11995 NV.SRV.TOTL.ZS
#> 12084 NY.AGR.SUBS.GD.ZS
#> 12088 NY.GDP.COAL.RT.ZS
#> 12089 NY.GDP.DEFL.87.ZG
#> 12090 NY.GDP.DEFL.KD.ZG
#> 12091 NY.GDP.DEFL.KD.ZG.AD
#> 12092 NY.GDP.DEFL.ZS
#> 12093 NY.GDP.DEFL.ZS.87
#> 12094 NY.GDP.DEFL.ZS.AD
#> 12095 NY.GDP.DISC.CD
#> 12096 NY.GDP.DISC.CN
#> 12097 NY.GDP.DISC.KN
#> 12101 NY.GDP.FCST.KD.87
#> 12103 NY.GDP.FCST.KN.87
#> 12104 NY.GDP.FRST.RT.ZS
#> 12105 NY.GDP.MINR.RT.ZS
#> 12106 NY.GDP.MKTP.CD
#> 12107 NY.GDP.MKTP.CD.XD
#> 12108 NY.GDP.MKTP.CN
#> 12109 NY.GDP.MKTP.CN.AD
#> 12110 NY.GDP.MKTP.CN.XD
#> 12111 NY.GDP.MKTP.IN
#> 12112 NY.GDP.MKTP.KD
#> 12113 NY.GDP.MKTP.KD.87
#> 12114 NY.GDP.MKTP.KD.ZG
#> 12115 NY.GDP.MKTP.KN
#> 12116 NY.GDP.MKTP.KN.87
#> 12117 NY.GDP.MKTP.KN.87.ZG
#> 12118 NY.GDP.MKTP.PP.CD
#> 12119 NY.GDP.MKTP.PP.KD
#> 12120 NY.GDP.MKTP.PP.KD.87
#> 12121 NY.GDP.MKTP.XD
#> 12122 NY.GDP.MKTP.XU.E
#> 12124 NY.GDP.NGAS.RT.ZS
#> 12125 NY.GDP.PCAP.CD
#> 12126 NY.GDP.PCAP.CN
#> 12127 NY.GDP.PCAP.KD
#> 12128 NY.GDP.PCAP.KD.ZG
#> 12129 NY.GDP.PCAP.KN
#> 12130 NY.GDP.PCAP.PP.CD
#> 12131 NY.GDP.PCAP.PP.KD
#> 12132 NY.GDP.PCAP.PP.KD.87
#> 12133 NY.GDP.PCAP.PP.KD.ZG
#> 12134 NY.GDP.PETR.RT.ZS
#> 12135 NY.GDP.TOTL.RT.ZS
#> 12148 NY.GDS.TOTL.ZS
#> 12153 NY.GEN.AEDU.GD.ZS
#> 12154 NY.GEN.DCO2.GD.ZS
#> 12155 NY.GEN.DFOR.GD.ZS
#> 12156 NY.GEN.DKAP.GD.ZS
#> 12157 NY.GEN.DMIN.GD.ZS
#> 12158 NY.GEN.DNGY.GD.ZS
#> 12159 NY.GEN.NDOM.GD.ZS
#> 12160 NY.GEN.SVNG.GD.ZS
#> 12193 NY.GNS.ICTR.ZS
#> 12231 NYGDPMKTPKDZ
#> 12232 NYGDPMKTPSACD
#> 12233 NYGDPMKTPSACN
#> 12234 NYGDPMKTPSAKD
#> 12235 NYGDPMKTPSAKN
#> 12261 PA.NUS.PPP
#> 12262 PA.NUS.PPP.05
#> 12263 PA.NUS.PPPC.RF
#> 16405 SE.PRM.SATT.2
#> 16469 SE.PRM.TATT.1
#> 16696 SE.XPD.EDUC.ZS
#> 16701 SE.XPD.PRIM.GDP.ZS
#> 16702 SE.XPD.PRIM.PC.ZS
#> 16705 SE.XPD.SECO.GDP.ZS
#> 16706 SE.XPD.SECO.PC.ZS
#> 16710 SE.XPD.TERT.GDP.ZS
#> 16711 SE.XPD.TERT.PC.ZS
#> 16714 SE.XPD.TOTL.GD.ZS
#> 16734 SF.TRN.RAIL.KM.ZS
#> 17722 SH.XPD.CHEX.GD.ZS
#> 17731 SH.XPD.GHED.GD.ZS
#> 17735 SH.XPD.HLTH.ZS
#> 17736 SH.XPD.KHEX.GD.ZS
#> 17748 SH.XPD.PRIV.ZS
#> 17751 SH.XPD.PUBL.ZS
#> 17757 SH.XPD.TOTL.ZS
#> 17867 SL.GDP.PCAP.EM.KD
#> 17868 SL.GDP.PCAP.EM.KD.ZG
#> 17869 SL.GDP.PCAP.EM.XD
#> 18629 TG.VAL.TOTL.GD.PP.ZS
#> 18630 TG.VAL.TOTL.GD.ZS
#> 18631 TG.VAL.TOTL.GG.ZS
#> 20862 UIS.XGDP.0.FSGOV
#> 20863 UIS.XGDP.1.FSGOV
#> 20864 UIS.XGDP.2.FSGOV
#> 20865 UIS.XGDP.23.FSGOV
#> 20866 UIS.XGDP.2T4.V.FSGOV
#> 20867 UIS.XGDP.3.FSGOV
#> 20868 UIS.XGDP.4.FSGOV
#> 20869 UIS.XGDP.56.FSGOV
#> 20919 UIS.XUNIT.GDPCAP.02.FSGOV
#> 20920 UIS.XUNIT.GDPCAP.1.FSGOV
#> 20921 UIS.XUNIT.GDPCAP.1.FSHH
#> 20922 UIS.XUNIT.GDPCAP.2.FSGOV
#> 20923 UIS.XUNIT.GDPCAP.23.FSGOV
#> 20924 UIS.XUNIT.GDPCAP.23.FSHH
#> 20925 UIS.XUNIT.GDPCAP.3.FSGOV
#> 20926 UIS.XUNIT.GDPCAP.5T8.FSGOV
#> 20927 UIS.XUNIT.GDPCAP.5T8.FSHH
#> name
#> 712 Per capita GDP growth
#> 714 GDP (current $)
#> 715 GDP growth (annual %)
#> 716 GDP (constant 2005 $)
#> 717 GDP per capita, PPP (constant 2011 international $)
#> 1558 Trade in services (% of GDP)
#> 1559 Gross private capital flows (% of GDP, PPP)
#> 1560 Gross private capital flows (% of GDP)
#> 1561 Gross foreign direct investment (% of GDP, PPP)
#> 1562 Gross foreign direct investment (% of GDP)
#> 1863 Wage bill as a percentage of GDP
#> 1883 Merchandise imports (BOP): percentage of GDP (%)
#> 1895 Foreign direct investment, net outflows (% of GDP)
#> 1896 Foreign direct investment, net outflows (% of GDP)
#> 1909 Current account balance (% of GDP)
#> 1910 Current account balance (% of GDP)
#> 1913 Curr. acc. bal. before official transf. (% of GDP)
#> 1916 Current account balance excluding net official capital grants (% of GDP)
#> 1922 Net income (% of GDP)
#> 1931 Foreign direct investment (% of GDP)
#> 1933 Foreign direct investment, net inflows (% of GDP)
#> 1939 Private capital flows, total (% of GDP)
#> 1950 Net current transfers (% of GDP)
#> 1999 Merchandise exports (BOP): percentage of GDP (%)
#> 2011 Foreign direct investment, net inflows (% of GDP)
#> 2013 Foreign direct investment, net inflows (% of GDP)
#> 2022 Migrant remittance inflows (% of GDP)
#> 2028 Personal remittances, received (% of GDP)
#> 2029 Workers' remittances, receipts (% of GDP)
#> 2326 Total energy tax revenue (% of GDP)
#> 2327 Total environmental tax revenue (% of GDP)
#> 2402 Macro drivers of GHG emissions growth in the period 2012-2018 - Emission Intensity of GDP
#> 2403 Macro drivers of GHG emissions growth in the period 2012-2018 - GDP per capita
#> 2426 Annual investment needs for coastal protection, by risk strategy (% of GDP) - low risk tolerance
#> 2427 Annual investment needs for coastal protection, by risk strategy (% of GDP) - constant relative flood risk
#> 2428 Annual investment needs for coastal protection, by risk strategy (% of GDP) - optimal protection
#> 2491 Risk to asset (average annual losses as % of GDP)
#> 2492 Risk to wellbeing (average annual losses as % of GDP)
#> 2501 Public social protection expenditure (%of GDP)
#> 2543 Financing via international capital markets (gross inflows, % of GDP)
#> 2546 Market capitalization of listed domestic companies (% of GDP)
#> 2549 Stocks traded, total value (% of GDP)
#> 2701 Gross PSD, Budgetary Central Gov., All maturities, All instruments, Domestic creditors, Nominal Value, % of GDP
#> 2704 Gross PSD, Central Gov., All maturities, All instruments, Domestic creditors, Nominal Value, % of GDP
#> 2707 Gross PSD, Financial Public Corp., All maturities, All instruments, Domestic creditors, Nominal Value, % of GDP
#> 2710 Gross PSD, General Gov., All maturities, All instruments, Domestic creditors, Nominal Value, % of GDP
#> 2713 Gross PSD, Nonfinancial Public Corp., All maturities, All instruments, Domestic creditors, Nominal Value, % of GDP
#> 2718 Gross PSD, Budgetary Central Gov., All maturities, All instruments, Foreign currency, Nominal Value, % of GDP
#> 2721 Gross PSD, Central Gov., All maturities, All instruments, Foreign currency, Nominal Value, % of GDP
#> 2724 Gross PSD, Financial Public Corp., All maturities, All instruments, Foreign currency, Nominal Value, % of GDP
#> 2727 Gross PSD, General Gov., All maturities, All instruments, Foreign currency, Nominal Value, % of GDP
#> 2730 Gross PSD, Nonfinancial Public Corp., All maturities, All instruments, Foreign currency, Nominal Value, % of GDP
#> 2735 Gross PSD, Budgetary Central Gov., All maturities, All instruments, Domestic currency, Nominal Value, % of GDP
#> 2738 Gross PSD, Central Gov., All maturities, All instruments, Domestic currency, Nominal Value, % of GDP
#> 2741 Gross PSD, Financial Public Corp., All maturities, All instruments, Domestic currency, Nominal Value, % of GDP
#> 2744 Gross PSD, General Gov., All maturities, All instruments, Domestic currency, Nominal Value, % of GDP
#> 2747 Gross PSD, Nonfinancial Public Corp., All maturities, All instruments, Domestic currency, Nominal Value, % of GDP
#> 2752 Gross PSD, Budgetary Central Gov., All maturities, All instruments, Nominal Value, % of GDP
#> 2755 Gross PSD, Central Gov., All maturities, All instruments, Nominal Value, % of GDP
#> 2758 Gross PSD, Financial Public Corp., All maturities, All instruments, Nominal Value, % of GDP
#> 2761 Gross PSD, General Gov., All maturities, All instruments, Nominal Value, % of GDP
#> 2764 Gross PSD, Nonfinancial Public Corp., All maturities, All instruments, Nominal Value, % of GDP
#> 2769 Gross PSD, Budgetary Central Gov., All maturities, All instruments, External creditors, Nominal Value, % of GDP
#> 2772 Gross PSD, Central Gov., All maturities, All instruments, External creditors, Nominal Value, % of GDP
#> 2775 Gross PSD, Financial Public Corp., All maturities, All instruments, External creditors, Nominal Value, % of GDP
#> 2778 Gross PSD, General Gov., All maturities, All instruments, External creditors, Nominal Value, % of GDP
#> 2781 Gross PSD, Nonfinancial Public Corp., All maturities, All instruments, External creditors, Nominal Value, % of GDP
#> 2786 Gross PSD, Budgetary Central Gov., All maturities, Currency and deposits, Nominal Value, % of GDP
#> 2789 Gross PSD, Central Gov., All maturities, Currency and deposits, Nominal Value, % of GDP
#> 2792 Gross PSD, Financial Public Corp., All maturities, Currency and deposits, Nominal Value, % of GDP
#> 2795 Gross PSD, General Gov., All maturities, Currency and deposits, Nominal Value, % of GDP
#> 2798 Gross PSD, Budgetary Central Gov., Long-term, With payment due in one year or less, Currency and deposits, Nominal Value, % of GDP
#> 2801 Gross PSD, Central Gov., Long-term, With payment due in one year or less, Currency and deposits, Nominal Value, % of GDP
#> 2804 Gross PSD, Financial Public Corp., Long-term, With payment due in one year or less, Currency and deposits, Nominal Value, % of GDP
#> 2807 Gross PSD, General Gov., Long-term, With payment due in one year or less, Currency and deposits, Nominal Value, % of GDP
#> 2810 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in one year or less, Currency and deposits, Nominal Value, % of GDP
#> 2815 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, Currency and deposits, Nominal Value, % of GDP
#> 2818 Gross PSD, Central Gov., Long-term, With payment due in more than one year, Currency and deposits, Nominal Value, % of GDP
#> 2821 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, Currency and deposits, Nominal Value, % of GDP
#> 2824 Gross PSD, General Gov., Long-term, With payment due in more than one year, Currency and deposits, Nominal Value, % of GDP
#> 2827 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, Currency and deposits, Nominal Value, % of GDP
#> 2832 Gross PSD, Nonfinancial Public Corp., All maturities, Currency and deposits, Nominal Value, % of GDP
#> 2836 Gross PSD, Central Gov.-D1, All maturities, Debt securities + loans, Nominal Value, % of GDP
#> 2838 Gross PSD, General Gov.-D1, All maturities, Debt securities + loans, Nominal Value, % of GDP
#> 2840 Gross PSD, Central Gov.-D2, All maturities, D1+ SDRs + currency and deposits, Nominal Value, % of GDP
#> 2842 Gross PSD, General Gov.-D2, All maturities, D1+ SDRs + currency and deposits, Nominal Value, % of GDP
#> 2844 Gross PSD, Central Gov.-D2A, All maturities, D1+ currency and deposits, Maastricht debt, % of GDP
#> 2846 Gross PSD, General Gov.-D2A, All maturities, D1+ currency and deposits, Maastricht debt, % of GDP
#> 2848 Gross PSD, Central Gov.-D3, All maturities, D2+other accounts payable, Nominal Value, % of GDP
#> 2850 Gross PSD, General Gov.-D3, All maturities, D2+other accounts payable, Nominal Value, % of GDP
#> 2852 Gross PSD, Central Gov.-D4, All maturities, D3+insurance, pensions, and standardized guarantees, Nominal Value, % of GDP
#> 2854 Gross PSD, General Gov.-D4, All maturities, D3+insurance, pensions, and standardized guarantees, Nominal Value, % of GDP
#> 2857 Gross PSD, Budgetary Central Gov., All maturities, Debt securities, Nominal Value, % of GDP
#> 2860 Gross PSD, Central Gov., All maturities, Debt securities, Nominal Value, % of GDP
#> 2863 Gross PSD, Financial Public Corp., All maturities, Debt securities, Nominal Value, % of GDP
#> 2866 Gross PSD, General Gov., All maturities, Debt securities, Nominal Value, % of GDP
#> 2869 Gross PSD, Budgetary Central Gov., Long-term, With payment due in one year or less, Debt securities, Nominal Value, % of GDP
#> 2872 Gross PSD, Central Gov., Long-term, With payment due in one year or less, Debt securities, Nominal Value, % of GDP
#> 2875 Gross PSD, Financial Public Corp., Long-term, With payment due in one year or less, Debt securities, Nominal Value, % of GDP
#> 2878 Gross PSD, General Gov., Long-term, With payment due in one year or less, Debt securities, Nominal Value, % of GDP
#> 2881 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in one year or less, Debt securities, Nominal Value, % of GDP
#> 2886 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, Debt securities, Nominal Value, % of GDP
#> 2889 Gross PSD, Central Gov., Long-term, With payment due in more than one year, Debt securities, Nominal Value, % of GDP
#> 2892 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, Debt securities, Nominal Value, % of GDP
#> 2895 Gross PSD, General Gov., Long-term, With payment due in more than one year, Debt securities, Nominal Value, % of GDP
#> 2898 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, Debt securities, Nominal Value, % of GDP
#> 2903 Gross PSD, Budgetary Central Gov., All maturities, Debt Securities, Market value, % of GDP
#> 2906 Gross PSD, Central Gov., All maturities, Debt Securities, Market value, % of GDP
#> 2909 Gross PSD, Financial Public Corp., All maturities, Debt Securities, Market value, % of GDP
#> 2912 Gross PSD, General Gov., All maturities, Debt Securities, Market value, % of GDP
#> 2915 Gross PSD, Nonfinancial Public Corp., All maturities, Debt Securities, Market value, % of GDP
#> 2920 Gross PSD, Nonfinancial Public Corp., All maturities, Debt securities, Nominal Value, % of GDP
#> 2925 Gross PSD, Budgetary Central Gov., All maturities, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2928 Gross PSD, Central Gov., All maturities, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2931 Gross PSD, Financial Public Corp., All maturities, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2934 Gross PSD, General Gov., All maturities, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2937 Gross PSD, Budgetary Central Gov., Long-term, With payment due in one year or less, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2940 Gross PSD, Central Gov., Long-term, With payment due in one year or less, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2943 Gross PSD, Financial Public Corp., Long-term, With payment due in one year or less, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2946 Gross PSD, General Gov., Long-term, With payment due in one year or less, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2949 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in one year or less, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2954 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2957 Gross PSD, Central Gov., Long-term, With payment due in more than one year, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2960 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2963 Gross PSD, General Gov., Long-term, With payment due in more than one year, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2966 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2971 Gross PSD, Nonfinancial Public Corp., All maturities, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 2976 Gross PSD, Budgetary Central Gov., All maturities, Loans, Nominal Value, % of GDP
#> 2979 Gross PSD, Central Gov., All maturities, Loans, Nominal Value, % of GDP
#> 2982 Gross PSD, Financial Public Corp., All maturities, Loans, Nominal Value, % of GDP
#> 2985 Gross PSD, General Gov., All maturities, Loans, Nominal Value, % of GDP
#> 2988 Gross PSD, Budgetary Central Gov., Long-term, With payment due in one year or less, Loans, Nominal Value, % of GDP
#> 2991 Gross PSD, Central Gov., Long-term, With payment due in one year or less, Loans, Nominal Value, % of GDP
#> 2994 Gross PSD, Financial Public Corp., Long-term, With payment due in one year or less, Loans, Nominal Value, % of GDP
#> 2997 Gross PSD, General Gov., Long-term, With payment due in one year or less, Loans, Nominal Value, % of GDP
#> 3000 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in one year or less, Loans, Nominal Value, % of GDP
#> 3005 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, Loans, Nominal Value, % of GDP
#> 3008 Gross PSD, Central Gov., Long-term, With payment due in more than one year, Loans, Nominal Value, % of GDP
#> 3011 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, Loans, Nominal Value, % of GDP
#> 3014 Gross PSD, General Gov., Long-term, With payment due in more than one year, Loans, Nominal Value, % of GDP
#> 3017 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, Loans, Nominal Value, % of GDP
#> 3022 Gross PSD, Nonfinancial Public Corp., All maturities, Loans, Nominal Value, % of GDP
#> 3027 Gross PSD, Budgetary Central Gov., All maturities, Other accounts payable, Nominal Value, % of GDP
#> 3030 Gross PSD, Central Gov., All maturities, Other accounts payable, Nominal Value, % of GDP
#> 3033 Gross PSD, Financial Public Corp., All maturities, Other accounts payable, Nominal Value, % of GDP
#> 3036 Gross PSD, General Gov., All maturities, Other accounts payable, Nominal Value, % of GDP
#> 3039 Gross PSD, Budgetary Central Gov., Long-term, With payment due in one year or less, Other accounts payable, Nominal Value, % of GDP
#> 3042 Gross PSD, Central Gov., Long-term, With payment due in one year or less, Other accounts payable, Nominal Value, % of GDP
#> 3045 Gross PSD, Financial Public Corp., Long-term, With payment due in one year or less, Other accounts payable, Nominal Value, % of GDP
#> 3048 Gross PSD, General Gov., Long-term, With payment due in one year or less, Other accounts payable, Nominal Value, % of GDP
#> 3051 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in one year or less, Other accounts payable, Nominal Value, % of GDP
#> 3056 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, Other accounts payable, Nominal Value, % of GDP
#> 3059 Gross PSD, Central Gov., Long-term, With payment due in more than one year, Other accounts payable, Nominal Value, % of GDP
#> 3062 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, Other accounts payable, Nominal Value, % of GDP
#> 3065 Gross PSD, General Gov., Long-term, With payment due in more than one year, Other accounts payable, Nominal Value, % of GDP
#> 3068 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, Other accounts payable, Nominal Value, % of GDP
#> 3073 Gross PSD, Nonfinancial Public Corp., All maturities, Other accounts payable, Nominal Value, % of GDP
#> 3078 Gross PSD, Budgetary Central Gov., All maturities, Special Drawing Rights, Nominal Value, % of GDP
#> 3081 Gross PSD, Central Gov., All maturities, Special Drawing Rights, Nominal Value, % of GDP
#> 3084 Gross PSD, Financial Public Corp., All maturities, Special Drawing Rights, Nominal Value, % of GDP
#> 3087 Gross PSD, General Gov., All maturities, Special Drawing Rights, Nominal Value, % of GDP
#> 3090 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, Special Drawing Rights, Nominal Value, % of GDP
#> 3093 Gross PSD, Central Gov., Long-term, With payment due in more than one year, Special Drawing Rights, Nominal Value, % of GDP
#> 3096 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, Special Drawing Rights, Nominal Value, % of GDP
#> 3099 Gross PSD, General Gov., Long-term, With payment due in more than one year, Special Drawing Rights, Nominal Value, % of GDP
#> 3102 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, Special Drawing Rights, Nominal Value, % of GDP
#> 3107 Gross PSD, Nonfinancial Public Corp., All maturities, Special Drawing Rights, Nominal Value, % of GDP
#> 3112 Gross PSD, Budgetary Central Gov., Long-term, All instruments, Nominal Value, % of GDP
#> 3115 Gross PSD, Central Gov., Long-term, All instruments, Nominal Value, % of GDP
#> 3118 Gross PSD, Financial Public Corp., Long-term, All instruments, Nominal Value, % of GDP
#> 3121 Gross PSD, General Gov., Long-term, All instruments, Nominal Value, % of GDP
#> 3124 Gross PSD, Budgetary Central Gov., Long-term, With payment due in one year or less, All instruments, Nominal Value, % of GDP
#> 3127 Gross PSD, Central Gov., Long-term, With payment due in one year or less, All instruments, Nominal Value, % of GDP
#> 3130 Gross PSD, Financial Public Corp., Long-term, With payment due in one year or less, All instruments, Nominal Value, % of GDP
#> 3133 Gross PSD, General Gov., Long-term, With payment due in one year or less, All instruments, Nominal Value, % of GDP
#> 3136 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in one year or less, All instruments, Nominal Value, % of GDP
#> 3141 Gross PSD, Budgetary Central Gov., Long-term, With payment due in more than one year, All instruments, Nominal Value, % of GDP
#> 3144 Gross PSD, Central Gov., Long-term, With payment due in more than one year, All instruments, Nominal Value, % of GDP
#> 3147 Gross PSD, Financial Public Corp., Long-term, With payment due in more than one year, All instruments, Nominal Value, % of GDP
#> 3150 Gross PSD, General Gov., Long-term, With payment due in more than one year, All instruments, Nominal Value, % of GDP
#> 3153 Gross PSD, Nonfinancial Public Corp., Long-term, With payment due in more than one year, All instruments, Nominal Value, % of GDP
#> 3158 Gross PSD, Nonfinancial Public Corp., Long-term, All instruments, Nominal Value, % of GDP
#> 3164 Gross PSD, Budgetary Central Gov., Short-term, Currency and deposits, Nominal Value, % of GDP
#> 3167 Gross PSD, Central Gov., Short-term, Currency and deposits, Nominal Value, % of GDP
#> 3170 Gross PSD, Financial Public Corp., Short-term, Currency and deposits, Nominal Value, % of GDP
#> 3173 Gross PSD, General Gov., Short-term, Currency and deposits, Nominal Value, % of GDP
#> 3176 Gross PSD, Nonfinancial Public Corp., Short-term, Currency and deposits, Nominal Value, % of GDP
#> 3181 Gross PSD, Budgetary Central Gov., Short-term, Debt securities, Nominal Value, % of GDP
#> 3184 Gross PSD, Central Gov., Short-term, Debt securities, Nominal Value, % of GDP
#> 3187 Gross PSD, Financial Public Corp., Short-term, Debt securities, Nominal Value, % of GDP
#> 3190 Gross PSD, General Gov., Short-term, Debt securities, Nominal Value, % of GDP
#> 3193 Gross PSD, Nonfinancial Public Corp., Short-term, Debt securities, Nominal Value, % of GDP
#> 3198 Gross PSD, Budgetary Central Gov., Short-term, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 3201 Gross PSD, Central Gov., Short-term, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 3204 Gross PSD, Financial Public Corp., Short-term, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 3207 Gross PSD, General Gov., Short-term, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 3210 Gross PSD, Nonfinancial Public Corp., Short-term, Insurance, pensions, and standardized guarantee schemes, Nominal Value, % of GDP
#> 3215 Gross PSD, Budgetary Central Gov., Short-term, Loans, Nominal Value, % of GDP
#> 3218 Gross PSD, Central Gov., Short-term, Loans, Nominal Value, % of GDP
#> 3221 Gross PSD, Financial Public Corp., Short-term, Loans, Nominal Value, % of GDP
#> 3224 Gross PSD, General Gov., Short-term, Loans, Nominal Value, % of GDP
#> 3227 Gross PSD, Nonfinancial Public Corp., Short-term, Loans, Nominal Value, % of GDP
#> 3232 Gross PSD, Budgetary Central Gov., Short-term, Other accounts payable, Nominal Value, % of GDP
#> 3235 Gross PSD, Central Gov., Short-term, Other accounts payable, Nominal Value, % of GDP
#> 3238 Gross PSD, Financial Public Corp., Short-term, Other accounts payable, Nominal Value, % of GDP
#> 3241 Gross PSD, General Gov., Short-term, Other accounts payable, Nominal Value, % of GDP
#> 3244 Gross PSD, Nonfinancial Public Corp., Short-term, Other accounts payable, Nominal Value, % of GDP
#> 3249 Gross PSD, Budgetary Central Gov., Short-term, All instruments, Nominal Value, % of GDP
#> 3252 Gross PSD, Central Gov., Short-term, All instruments, Nominal Value, % of GDP
#> 3255 Gross PSD, Financial Public Corp., Short-term, All instruments, Nominal Value, % of GDP
#> 3258 Gross PSD, General Gov., Short-term, All instruments, Nominal Value, % of GDP
#> 3261 Gross PSD, Nonfinancial Public Corp., Short-term, All instruments, Nominal Value, % of GDP
#> 3757 Debt on Concessional terms to GDP (% of GDP)
#> 3760 Debt on Non-concessional terms to GDP (% of GDP)
#> 3915 Debt outstanding and disbursed, Total to GDP (% of GDP)
#> 5518 Net ODA received (% of GDP)
#> 5589 Net ODA received from DAC donors (% of recipient's GDP)
#> 5594 Net ODA received from multilateral donors (% of GDP)
#> 5602 Net ODA received from non-DAC bilateral donors (% of GDP)
#> 5608 Net ODA received (% of GDP)
#> 5758 Total debt service (% of GDP)
#> 6110 Energy intensity level of primary energy (MJ/$2017 PPP GDP)
#> 6134 GDP per unit of energy use (1987 US$ per kg of oil equivalent)
#> 6135 GDP per unit of energy use (2000 US$ per kg of oil equivalent)
#> 6136 GDP per unit of energy use (PPP $ per kg of oil equivalent)
#> 6137 GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)
#> 6145 Energy use (kg of oil equivalent) per $1,000 GDP (constant 2017 PPP)
#> 6164 CO2 emissions, industrial (kg per 1987 US$ of GDP)
#> 6168 CO2 emissions, industrial (kg per 1987 US$ of GDP)
#> 6169 CO2 emissions (kg per 2015 US$ of GDP)
#> 6174 CO2 emissions (kg per PPP $ of GDP)
#> 6175 CO2 emissions (kg per 2017 PPP $ of GDP)
#> 6305 Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)
#> 6323 GDP per unit of energy use (1987 US$ per kg of oil equivalent)
#> 6377 Deposit insurance coverage (% of GDP per capita)
#> 6730 Domestic credit to private sector by banks (% of GDP)
#> 6736 Total reserves includes gold (% of GDP)
#> 8052 Claims on governments and other public entities (% of GDP)
#> 8061 Monetary Sector credit to private sector (% GDP)
#> 8070 Broad money (% of GDP)
#> 8077 Money and quasi money (M2) as % of GDP
#> 8078 Money and quasi money (M2) as % of GDP
#> 8080 Income velocity of money (GDP/M2)
#> 8085 Quasi-liquid liabilities (% of GDP)
#> 8086 Seignorage (% of GDP)
#> 8125 Claims on central government, etc. (% GDP)
#> 8126 Claims on other sectors of the domestic economy (% of GDP)
#> 8127 Domestic credit provided by financial sector (% of GDP)
#> 8128 Domestic credit provided by banking sector (% of GDP)
#> 8130 Domestic credit to private sector (% of GDP)
#> 8131 Credit to private sector (% of GDP)
#> 8132 Liquid liabilities (M3) as % of GDP
#> 8133 Liquid liabilities (M3) as % of GDP
#> 8134 Quasi-liquid liabilities (% of GDP)
#> 8205 Overall budget balance, including grants (% of GDP)
#> 8206 Overall budget deficit, including grants (% of GDP)
#> 8215 Central government debt, total (% of GDP)
#> 8216 Central government debt, total (% of GDP)
#> 8220 Financing from abroad (% of GDP)
#> 8221 Financing from abroad (% of GDP)
#> 8225 Domestic financing, total (% of GDP)
#> 8226 Domestic finanacing (% of GDP)
#> 8236 Current revenue, excluding grants (% of GDP)
#> 8239 Current revenue (% of GDP)
#> 8241 Central government revenues, excluding all grants (% of GDP)
#> 8244 Current revenue, excluding grants (% of GDP)
#> 8246 SOE external debt (% of GDP)
#> 8248 State-owned enterprises, economic activity (% of GDP)
#> 8249 SOE economic activity (% of GDP)
#> 8252 State-owned enterprises, net financial flows from government (% of GDP)
#> 8253 SOE net financial flows from government (% of GDP)
#> 8254 State-owned enterprises, overall balance before transfers (% of GDP)
#> 8280 Tax revenue (% of GDP)
#> 8281 Tax revenue (% of GDP)
#> 8299 Defense expenditure (% of GDP)
#> 8302 Research and development expenditure (% of GDP)
#> 8305 Expenditure, total (% of GDP)
#> 8306 Total expenditure (% of GDP)
#> 8313 Net acquisition of financial assets (% of GDP)
#> 8316 Cash surplus/deficit (% of GDP)
#> 8321 Central government debt, total (% of GDP)
#> 8325 Net incurrence of liabilities, domestic (% of GDP)
#> 8327 Net incurrence of liabilities, foreign (% of GDP)
#> 8329 Net incurrence of liabilities, total (% of GDP)
#> 8331 Net investment in nonfinancial assets (% of GDP)
#> 8333 Net lending (+) / net borrowing (-) (% of GDP)
#> 8344 Revenue, excluding grants (% of GDP)
#> 8359 Tax revenue (% of GDP)
#> 8376 Expense (% of GDP)
#> 8466 Private credit by deposit money banks to GDP (%)
#> 8467 Deposit money banks'' assets to GDP (%)
#> 8468 Nonbank financial institutions’ assets to GDP (%)
#> 8470 Liquid liabilities to GDP (%)
#> 8471 Central bank assets to GDP (%)
#> 8472 Mutual fund assets to GDP (%)
#> 8473 Financial system deposits to GDP (%)
#> 8474 Life insurance premium volume to GDP (%)
#> 8475 Non-life insurance premium volume to GDP (%)
#> 8476 Insurance company assets to GDP (%)
#> 8477 Private credit by deposit money banks and other financial institutions to GDP (%)
#> 8478 Pension fund assets to GDP (%)
#> 8479 Domestic credit to private sector (% of GDP)
#> 8480 Stock market capitalization to GDP (%)
#> 8481 Stock market total value traded to GDP (%)
#> 8482 Outstanding domestic private debt securities to GDP (%)
#> 8483 Outstanding domestic public debt securities to GDP (%)
#> 8484 Outstanding international private debt securities to GDP (%)
#> 8485 Outstanding international public debt securities to GDP (%)
#> 8486 International debt issues to GDP (%)
#> 8487 Gross portfolio equity liabilities to GDP (%)
#> 8488 Gross portfolio equity assets to GDP (%)
#> 8489 Gross portfolio debt liabilities to GDP (%)
#> 8490 Gross portfolio debt assets to GDP (%)
#> 8491 Syndicated loan issuance volume to GDP (%)
#> 8492 Corporate bond issuance volume to GDP (%)
#> 8495 Credit flows by fintech and bigtech companies to GDP (%)
#> 8503 Credit to government and state-owned enterprises to GDP (%)
#> 8508 Bank deposits to GDP (%)
#> 8511 Loans from nonresident banks (net) to GDP (%)
#> 8512 Loans from nonresident banks (amounts outstanding) to GDP (%)
#> 8516 Remittance inflows to GDP (%)
#> 8517 Consolidated foreign claims of BIS reporting banks to GDP (%)
#> 8521 Global leasing volume to GDP (%)
#> 8522 Total factoring volume to GDP (%)
#> 9365 Information and communication technology expenditure (% of GDP)
#> 9639 Railways, goods transported (ton-km per PPP $ million of GDP)
#> 9641 Railways, passenger-km (per PPP $ million of GDP)
#> 9748 Telecommunications revenue (% GDP)
#> 11639 Military expenditure (% of GDP)
#> 11644 GDP on Accommodation & Food Beverages Activity Sector (in IDR Million), SNA 2008, Current Price
#> 11645 GDP on Accommodation & Food Beverages Activity Sector (in IDR Million), SNA 2008, Constant Price
#> 11646 GDP on Agriculture Sector (in IDR Million), Current Price
#> 11647 GDP on Agriculture Sector (in IDR Million), Constant Price
#> 11648 GDP on Agriculture, Forestry & Fisheries Sector (in IDR Million), SNA 2008, Current Price
#> 11649 GDP on Agriculture, Forestry & Fisheries Sector (in IDR Million), SNA 2008, Constant Price
#> 11650 GDP on Business Services Sector (in IDR Million), SNA 2008, Current Price
#> 11651 GDP on Business Services Sector (in IDR Million), SNA 2008, Constant Price
#> 11652 GDP on Construction Sector (in IDR Million), Current Price
#> 11653 GDP on Construction Sector (in IDR Million), Constant Price
#> 11654 GDP on Construction Sector (in IDR Million), SNA 2008, Current Price
#> 11655 GDP on Construction Sector (in IDR Million), SNA 2008, Constant Price
#> 11656 GDP on Education Services Sector (in IDR Million), SNA 2008, Current Price
#> 11657 GDP on Education Services Sector (in IDR Million), SNA 2008, Constant Price
#> 11658 GDP on Electricity & Gas Supply Sector (in IDR Million), SNA 2008, Current Price
#> 11659 GDP on Electricity & Gas Supply Sector (in IDR Million), SNA 2008, Constant Price
#> 11660 Total GDP excluding Oil and Gas (in IDR Million), Current Price
#> 11661 Total GDP excluding Oil and Gas (in IDR Million), Constant Price
#> 11662 GDP on Financial Service Sector (in IDR Million), Current Price
#> 11663 GDP on Financial Service Sector (in IDR Million), Constant Price
#> 11664 GDP on Financial & Insurance Activity Sector (in IDR Million), SNA 2008, Current Price
#> 11665 GDP on Financial & Insurance Activity Sector (in IDR Million), SNA 2008, Constant Price
#> 11666 GDP on Human Health & Social Work Activity Sector (in IDR Million), SNA 2008, Current Price
#> 11667 GDP on Human Health & Social Work Activity Sector (in IDR Million), SNA 2008, Constant Price
#> 11668 Total GDP including Oil and Gas (in IDR Million), Current Price
#> 11669 Total GDP including Oil and Gas (in IDR Million), Constant Price
#> 11670 Total GDP including Oil and Gas (in IDR Million), SNA 2008, Current Price
#> 11671 Total GDP including Oil and Gas (in IDR Million), SNA 2008, Constant Price
#> 11672 GDP on Information & Communication Sector (in IDR Million), SNA 2008, Current Price
#> 11673 GDP on Information & Communication Sector (in IDR Million), SNA 2008, Constant Price
#> 11674 GDP on Mining and Quarrying Sector (in IDR Million), Current Price
#> 11675 GDP on Mining and Quarrying Sector (in IDR Million), Constant Price
#> 11676 GDP on Mining & Quarrying Sector (in IDR Million), SNA 2008, Current Price
#> 11677 GDP on Mining & Quarrying Sector (in IDR Million), SNA 2008, Constant Price
#> 11678 GDP on Manufacturing Sector (in IDR Million), Current Price
#> 11679 GDP on Manufacturing Sector (in IDR Million), Constant Price
#> 11680 GDP on Manufacturing Industry Sector (in IDR Million), SNA 2008, Current Price
#> 11681 GDP on Manufacturing Industry Sector (in IDR Million), SNA 2008, Constant Price
#> 11682 GDP on Public Administration, Defense & Compulsory Social Security Sector (in IDR Million), SNA 2008, Current Price
#> 11683 GDP on Public Administration, Defense & Compulsory Social Security Sector (in IDR Million), SNA 2008, Constant Price
#> 11684 GDP on Real Estate Sector (in IDR Million), SNA 2008, Current Price
#> 11685 GDP on Real Estate Sector (in IDR Million), SNA 2008, Constant Price
#> 11686 GDP on Other Service Sector (in IDR Million), Current Price
#> 11687 GDP on Other Service Sector (in IDR Million), Constant Price
#> 11688 GDP on Other Services Sector (in IDR Million), SNA 2008, Current Price
#> 11689 GDP on Other Services Sector (in IDR Million), SNA 2008, Constant Price
#> 11690 GDP on Transportation and Telecommunication Sector (in IDR Million), Current Price
#> 11691 GDP on Transportation and Telecommunication Sector (in IDR Million), Constant Price
#> 11692 GDP on Transportation & Storage Sector (in IDR Million), SNA 2008, Current Price
#> 11693 GDP on Transportation & Storage Sector (in IDR Million), SNA 2008, Constant Price
#> 11694 GDP on Trade, Hotel and Restaurant Sector (in IDR Million), Current Price
#> 11695 GDP on Trade, Hotel and Restaurant Sector (in IDR Million), Constant Price
#> 11696 GDP on Wholesales & Retail Trade, Repair of Motor Vehicles & Motorcycles Sector (in IDR Million), SNA 2008, Current Price
#> 11697 GDP on Wholesales & Retail Trade, Repair of Motor Vehicles & Motorcycles Sector (in IDR Million), SNA 2008, Constant Price
#> 11698 GDP on Utilities Sector (in IDR Million), Current Price
#> 11699 GDP on Utilities Sector (in IDR Million), Constant Price
#> 11700 GDP on Water Supply, Sewerage, Waste & Recycling Management Sector (in IDR Million), SNA 2008, Current Price
#> 11701 GDP on Water Supply, Sewerage, Waste & Recycling Management Sector (in IDR Million), SNA 2008, Constant Price
#> 11710 General government final consumption expenditure (% of GDP)
#> 11721 Household final consumption expenditure, etc. (% of GDP)
#> 11738 Households and NPISHs final consumption expenditure (% of GDP)
#> 11748 Final consumption expenditure, etc. (% of GDP)
#> 11756 Total consumption: contribution to growth of GDP (%)
#> 11757 Final consumption expenditure (% of GDP)
#> 11767 Gross national expenditure (% of GDP)
#> 11779 Exports of goods and services (% of GDP)
#> 11781 GDP expenditure on general government consumption (in IDR Million)
#> 11782 GDP expenditure on general government consumption (in IDR Million), SNA 2008, Current Price
#> 11783 GDP expenditure on non profit private institution consumption (in IDR Million)
#> 11784 GDP expenditure on non profit private institution consumption (in IDR Million), SNA 2008, Current Price
#> 11785 GDP expenditure on private consumption (in IDR Million)
#> 11786 GDP expenditure on private consumption (in IDR Million), SNA 2008, Current Price
#> 11787 GDP expenditure on exports (in IDR Million)
#> 11788 GDP expenditure on exports (in IDR Million), SNA 2008, Current Price
#> 11813 Gross fixed capital formation, private sector (% of GDP)
#> 11818 Gross public investment (% of GDP)
#> 11821 GDP expenditure on gross fixed capital formation (in IDR Million)
#> 11828 GDP expenditure on gross fixed capital formation (in IDR Million), SNA 2008, Current Price
#> 11829 Gross fixed capital formation (% of GDP)
#> 11830 GDP expenditure on imports (in IDR Million)
#> 11831 GDP expenditure on imports (in IDR Million), SNA 2008, Current Price
#> 11832 GDP expenditure on inter-region net exports (in IDR Million), SNA 2008, Current Price
#> 11837 GDP expenditure on changes in stock (in IDR Million)
#> 11841 GDP expenditure on changes in stock (in IDR Million), SNA 2008, Current Price
#> 11850 Total GDP based on expenditure (in IDR Million)
#> 11857 Total GDP based on expenditure (in IDR Million), SNA 2008, Current Price
#> 11858 Gross domestic investment: contr. to growth of GDP(%)
#> 11859 Gross capital formation (% of GDP)
#> 11869 Imports of goods and services (% of GDP)
#> 11870 Merchandise trade to GDP ratio (%)
#> 11876 Resource balance: contribution to growth of GDP (%)
#> 11877 External balance on goods and services (% of GDP)
#> 11880 Trade (% of GDP)
#> 11887 Agriculture: contribution to growth of GDP (%)
#> 11891 Industry: contribution to growth of GDP (%)
#> 11897 Services: contribution to growth of GDP (%)
#> 11905 Real agricultural GDP per capita growth rate (%)
#> 11915 Real agricultural GDP growth rates (%)
#> 11916 Agriculture, forestry, and fishing, value added (% of GDP)
#> 11936 Manufacturing, value added (% of GDP)
#> 11950 Industry: contribution to growth of GDP (%)
#> 11951 Industry (including construction), value added (% of GDP)
#> 11969 Discrepancy in GDP, value added (current US$)
#> 11970 Discrepancy in GDP, value added (current LCU)
#> 11971 Discrepancy in GDP, value added (constant LCU)
#> 11988 Services: contribution to growth of GDP (%)
#> 11989 Services, etc., value added (% of GDP)
#> 11995 Services, value added (% of GDP)
#> 12084 Agricultural support estimate (% of GDP)
#> 12088 Coal rents (% of GDP)
#> 12089 Inflation, GDP deflator (annual %)
#> 12090 Inflation, GDP deflator (annual %)
#> 12091 Inflation, GDP deflator: linked series (annual %)
#> 12092 GDP deflator (base year varies by country)
#> 12093 GDP deflator (1987 = 100)
#> 12094 GDP deflator: linked series (base year varies by country)
#> 12095 Discrepancy in expenditure estimate of GDP (current US$)
#> 12096 Discrepancy in expenditure estimate of GDP (current LCU)
#> 12097 Discrepancy in expenditure estimate of GDP (constant LCU)
#> 12101 GDP at factor cost (constant 1987 US$)
#> 12103 GDP at factor cost (constant 1987 LCU)
#> 12104 Forest rents (% of GDP)
#> 12105 Mineral rents (% of GDP)
#> 12106 GDP (current US$)
#> 12107 GDP deflator, index (2000=100; US$ series)
#> 12108 GDP (current LCU)
#> 12109 GDP: linked series (current LCU)
#> 12110 GDP deflator, period average (LCU index 2000=100)
#> 12111 GDP Deflator
#> 12112 GDP (constant 2015 US$)
#> 12113 GDP at market prices (constant 1987 US$)
#> 12114 GDP growth (annual %)
#> 12115 GDP (constant LCU)
#> 12116 GDP at market prices (constant 1987 LCU)
#> 12117 GDP growth (annual %)
#> 12118 GDP, PPP (current international $)
#> 12119 GDP, PPP (constant 2017 international $)
#> 12120 GDP, PPP (constant 1987 international $)
#> 12121 GDP deflator (1987=100,Index)
#> 12122 GDP deflator, end period (base year varies by country)
#> 12124 Natural gas rents (% of GDP)
#> 12125 GDP per capita (current US$)
#> 12126 GDP per capita (current LCU)
#> 12127 GDP per capita (constant 2015 US$)
#> 12128 GDP per capita growth (annual %)
#> 12129 GDP per capita (constant LCU)
#> 12130 GDP per capita, PPP (current international $)
#> 12131 GDP per capita, PPP (constant 2017 international $)
#> 12132 GDP per capita, PPP (constant 1987 international $)
#> 12133 GDP per capita, PPP annual growth (%)
#> 12134 Oil rents (% of GDP)
#> 12135 Total natural resources rents (% of GDP)
#> 12148 Gross domestic savings (% of GDP)
#> 12153 Genuine savings: education expenditure (% of GDP)
#> 12154 Genuine savings: carbon dioxide damage (% of GDP)
#> 12155 Genuine savings: net forest depletion (% of GDP)
#> 12156 Genuine savings: consumption of fixed capital (% of GDP)
#> 12157 Genuine savings: mineral depletion (% of GDP)
#> 12158 Genuine savings: energy depletion (% of GDP)
#> 12159 Genuine savings: net domestic savings (% of GDP)
#> 12160 Genuine domestic savings (% of GDP)
#> 12193 Gross savings (% of GDP)
#> 12231 Annual percentage growth rate of GDP at market prices based on constant 2010 US Dollars.
#> 12232 GDP,current US$,millions,seas. adj.,
#> 12233 GDP,current LCU,millions,seas. adj.,
#> 12234 GDP,constant 2010 US$,millions,seas. adj.,
#> 12235 GDP,constant 2010 LCU,millions,seas. adj.,
#> 12261 PPP conversion factor, GDP (LCU per international $)
#> 12262 2005 PPP conversion factor, GDP (LCU per international $)
#> 12263 Price level ratio of PPP conversion factor (GDP) to market exchange rate
#> 16405 (De Facto) Average principal salary as percent of GDP per capita
#> 16469 (De Jure) Average starting public-school teacher salary as percent of GDP per capita
#> 16696 Public Expenditure on Education (% GDP)
#> 16701 Public spending on education, primary (% of GDP)
#> 16702 Government expenditure per student, primary (% of GDP per capita)
#> 16705 Public spending on education, secondary (% of GDP)
#> 16706 Government expenditure per student, secondary (% of GDP per capita)
#> 16710 Public spending on education, tertiary (% of GDP)
#> 16711 Government expenditure per student, tertiary (% of GDP per capita)
#> 16714 Government expenditure on education, total (% of GDP)
#> 16734 Rail traffic (km per million US$ GDP)
#> 17722 Current health expenditure (% of GDP)
#> 17731 Domestic general government health expenditure (% of GDP)
#> 17735 Public Expenditure on Health (% GDP)
#> 17736 Capital health expenditure (% of GDP)
#> 17748 Health expenditure, private (% of GDP)
#> 17751 Health expenditure, public (% of GDP)
#> 17757 Health expenditure, total (% of GDP)
#> 17867 GDP per person employed (constant 2017 PPP $)
#> 17868 GDP per person employed (annual % growth)
#> 17869 GDP per person employed, index (1980 = 100)
#> 18629 Trade (% of GDP, PPP)
#> 18630 Merchandise trade (% of GDP)
#> 18631 Trade in goods (% of goods GDP)
#> 20862 Government expenditure on pre-primary education as % of GDP (%)
#> 20863 Government expenditure on primary education as % of GDP (%)
#> 20864 Government expenditure on lower secondary education as a percentage of GDP (%)
#> 20865 Government expenditure on secondary education as % of GDP (%)
#> 20866 Government expenditure on secondary and post-secondary non-tertiary vocational education as % of GDP (%)
#> 20867 Government expenditure on upper secondary education as a percentage of GDP (%)
#> 20868 Government expenditure on post-secondary non-tertiary education as % of GDP (%)
#> 20869 Government expenditure on tertiary education as % of GDP (%)
#> 20919 Initial government funding per pre-primary student as a percentage of GDP per capita
#> 20920 Initial government funding per primary student as a percentage of GDP per capita
#> 20921 Initial household funding per primary student as a percentage of GDP per capita
#> 20922 Initial government funding per lower secondary student as a percentage of GDP per capita
#> 20923 Initial government funding per secondary student as a percentage of GDP per capita
#> 20924 Initial household funding per secondary student as a percentage of GDP per capita
#> 20925 Initial government funding per upper secondary student as a percentage of GDP per capita
#> 20926 Initial government funding per tertiary student as a percentage of GDP per capita
#> 20927 Initial household funding per tertiary student as a percentage of GDP per capita
#> description
#> 712 GDP per capita is the sum of gross value added by all resident producers in the economy plus any product taxes (less subsidies) not included in the valuation of output, divided by mid-year population. Growth is calculated from constant price GDP data in local currency. Sustained economic growth increases average incomes and is strongly linked to poverty reduction. GDP per capita provides a basic measure of the value of output per person, which is an indirect indicator of per capita income. Growth in GDP and GDP per capita are considered broad measures of economic growth.
#> 714 GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.
#> 715 Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2011 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
#> 716 GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2000 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.
#> 717 GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2011 international dollars.
#> 1558 Trade in services is the sum of service exports and imports divided by the value of GDP, all in current U.S. dollars.
#> 1559
#> 1560
#> 1561
#> 1562
#> 1863
#> 1883
#> 1895 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net outflows of investment from the reporting economy to the rest of the world and is divided by GDP.
#> 1896 Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world, and is divided by GDP.
#> 1909 Current account balance is the sum of net exports of goods and services, net primary income, and net secondary income.
#> 1910
#> 1913
#> 1916 Current account balance is the sum of net exports of goods, services, net income, and net current transfers. This is divided by GDP at market prices, with both series expressed in current U.S. dollars.
#> 1922 Net income refers to receipts and payments of employee compensation paid to nonresident workers and investment income (receipts and payments on direct investment, portfolio investment, other investments, and receipts on reserve assets). Income derived from the use of intangible assets is recorded under business services. Data are in current U.S. dollars.
#> 1931 Foreign direct investment is net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows total net, that is, net FDI in the reporting economy from foreign sources less net FDI by the reporting economy to the rest of the world. Data are in current U.S. dollars.
#> 1933
#> 1939 Private capital flows consist of net foreign direct investment and portfolio investment. Foreign direct investment is net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. The FDI included here is total net, that is, net FDI in the reporting economy from foreign sources less net FDI by the reporting economy to the rest of the world. Portfolio investment covers transactions in equity securities and debt securities.
#> 1950 Net current transfers are recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. Data are in current U.S. dollars.
#> 1999
#> 2011
#> 2013 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP.
#> 2022 Migrants’ remittances are defined as the sum of worker’s remittances,
#> 2028 Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees.
#> 2029 Workers' remittances are current transfers by migrants who are employed or intend to remain employed for more than a year in another economy in which they are considered residents. Some developing countries classify workers' remittances as a factor income receipt (and thus as a component of GNI). The World Bank adheres to international guidelines in defining GNI, and its classification of workers' remittances may therefore differ from national practices. This item shows receipts by the reporting country. Data are in current U.S. dollars.
#> 2326 Revenues from taxes raised on energy products (fossil fuels and electricity) including those used in transportation (petrol and diesel). This includes all CO2-related taxes.
#> 2327
#> 2402 Data reflects the impact of the emissions intensity of GDP (as a macro-driver) to total emission growth (excluding LUCF) across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020.
#> 2403 Data reflects the impact of GDP-per capita (as a macro-driver) to total emission growth (excluding LUCF) across the period 2012-2018. This data has been calculated by World Bank staff using greenhouse gas emissions data from the World Resource Institute's Climate Watch. Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. Climate Watch Historical GHG Emissions data (previously published through CAIT Climate Data Explorer) are derived from several sources. Any use of the Land-Use Change and Forestry or Agriculture indicator should be cited as FAO 2020, FAOSTAT Emissions Database. Any use of CO2 emissions from fuel combustion data should be cited as CO2 Emissions from Fuel Combustion, OECD/IEA, 2020.
#> 2426 Data reflects the cost of a risk taking strategy that adopts an ambititious low-risk strategy. This is discussed in Policy Note 5 of the Beyond the Gap report.
#> 2427 Data reflects the cost of a risk taking strategy that keeps the risk relative to GDP constant. This is discussed in Policy Note 5 of the Beyond the Gap report.
#> 2428 Data reflects the level of spending in protection that minimizes the cost of floods, including the prevention of expenditure and damages. This is discussed in Policy Note 5 of the Beyond the Gap report.
#> 2491 Risk to asset indicator is intended to be viewed with risk to wellbeing indicator (both reported as average annual losses as % of GDP). The Unbreakable report which the indicators are taken from moves beyond asset and production losses and shifts its attention to how natural disasters affect people’s well-being. Disasters are far greater threats to well-being than traditional estimates suggest. This approach provides a more nuanced view of natural disasters than usual reporting, and a perspective that takes fuller account of poor people’s vulnerabilities.
#> 2492 Risk to wellbeing indicator is intended to be viewed with risk to asset indicator (both reported as average annual losses as % of GDP). The Unbreakable report which the indicators are taken from moves beyond asset and production losses and shifts its attention to how natural disasters affect people’s well-being. Disasters are far greater threats to well-being than traditional estimates suggest. This approach provides a more nuanced view of natural disasters than usual reporting, and a perspective that takes fuller account of poor people’s vulnerabilities.
#> 2501
#> 2543
#> 2546 Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.
#> 2549 The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.
#> 2701 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2704 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2707 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2710 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2713 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2718 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2721 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2724 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2727 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2730 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2735 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2738 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2741 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2744 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2747 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2752 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2755 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2758 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2761 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2764 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2769 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2772 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2775 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2778 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2781 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2786 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2789 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2792 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2795 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2798 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2801 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2804 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2807 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2810 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2815 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2818 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2821 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2824 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2827 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2832 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2836 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2838 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2840 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2842 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2844 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2846 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2848 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2850 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2852 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2854 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2857 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2860 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2863 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2866 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2869 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2872 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2875 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2878 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2881 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2886 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2889 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2892 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2895 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2898 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2903 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2906 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2909 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2912 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2915 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2920 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2925 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2928 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2931 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2934 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2937 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2940 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2943 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2946 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2949 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2954 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2957 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2960 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2963 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2966 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2971 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2976 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2979 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2982 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2985 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2988 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2991 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2994 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 2997 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3000 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3005 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3008 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3011 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3014 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3017 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3022 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3027 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3030 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3033 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3036 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3039 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3042 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3045 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3048 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3051 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3056 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3059 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3062 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3065 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3068 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3073 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3078 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3081 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3084 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3087 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3090 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3093 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3096 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3099 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3102 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3107 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3112 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3115 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3118 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3121 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3124 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3127 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3130 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3133 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3136 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3141 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3144 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3147 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3150 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3153 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3158 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3164 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3167 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3170 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3173 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3176 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3181 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3184 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3187 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3190 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3193 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3198 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3201 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3204 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3207 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3210 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3215 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3218 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3221 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3224 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3227 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3232 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3235 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3238 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3241 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3244 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3249 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3252 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3255 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3258 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3261 The source of non-seasonally adjusted Gross Domestic Product (GDP) data in national currency, at current prices, is the International Finance Statistics quarterly database of the IMF, annualized by the World Bank, unless otherwise specified.
#> 3757 Concessional Long-term Debt Outstanding and Disbursed (LDOD) conveys information about the borrower's receipt of aid from official lenders at concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. LDOD is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services.
#> 3760 Non-concessional LDOD conveys information about the borrower's receipt of aid from official lenders on non-concessional terms as defined by the Development Assistance Committee (DAC) of the OECD. Concessional debt is defined as loans with an original grant element of 25 percent or more. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. Loans from major regional development banks--African Development Bank, Asian Development Bank, and the Inter-American Development Bank--and from the World Bank are classified as concessional according to each institution's classification and not according to the DAC definition, as was the practice in earlier reports. Long-term debt outstanding and disbursed (LDOD) is the total outstanding long-term debt at year end. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents and repayable in currency, goods, or services. Data are in current U.S. dollars.
#> 3915 Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data expressed as a percentage of GDP at market prices.
#> 5518 Official development assistance and net official aid record the actual international transfer by the donor of financial resources or of goods or services valued at the cost to the donor, less any repayments of loan principal during the same period.
#> 5589 Official development assistance and net official aid record the actual international transfer by the donor of financial resources or of goods or services valued at the cost to the donor, less any repayments of loan principal during the same period.
#> 5594 Official development assistance and net official aid record the actual international transfer by the donor of financial resources or of goods or services valued at the cost to the donor, less any repayments of loan principal during the same period.
#> 5602 Official development assistance and net official aid record the actual international transfer by the donor of financial resources or of goods or services valued at the cost to the donor, less any repayments of loan principal during the same period.
#> 5608 Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).
#> 5758 Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF.
#> 6110 Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output.
#> 6134
#> 6135
#> 6136 GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to current international dollars using purchasing power parity rates based on the 2017 ICP round. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.
#> 6137 GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to 2017 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.
#> 6145 Energy use per PPP GDP is the kilogram of oil equivalent of energy use per constant PPP GDP. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. PPP GDP is gross domestic product converted to 2017 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.
#> 6164
#> 6168
#> 6169 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.
#> 6174 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.
#> 6175 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.
#> 6305 Water productivity is calculated as GDP in constant prices divided by annual total water withdrawal.
#> 6323
#> 6377
#> 6730 Domestic credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.
#> 6736 Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices.
#> 8052 Claims on governments and other public entities (IFS line 32an + 32b + 32bx + 32c) usually comprise direct credit for specific purposes such as financing of the government budget deficit or loans to state enterprises, advances against future credit authorizations, and purchases of treasury bills and bonds, net of deposits by the public sector. Public sector deposits with the banking system also include sinking funds for the service of debt and temporary deposits of government revenues. Data are in current local currency.
#> 8061 Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.
#> 8070 Broad money (IFS line 35L..ZK) is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler’s checks; and other securities such as certificates of deposit and commercial paper.
#> 8077 Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition of money supply is frequently called M2; it corresponds to lines 34 and 35 in the International Monetary Fund's (IMF) International Financial Statistics (IFS).
#> 8078
#> 8080
#> 8085
#> 8086
#> 8125 Claims on central government (IFS line 52AN or 32AN) include loans to central government institutions net of deposits.
#> 8126 Claims on other sectors of the domestic economy (IFS line 52S or 32S) include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds.
#> 8127 Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.
#> 8128
#> 8130 Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.
#> 8131
#> 8132 Liquid liabilities are also known as M3. They are the sum of currency and deposits in the central bank (M0), plus transferable deposits and electronic currency (M1), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents.
#> 8133
#> 8134 Quasi-liquid liabilities are the sum of currency and deposits in the central bank (M0), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements, plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents. They equal the M3 money supply less transferable deposits and electronic currency (M1).
#> 8205
#> 8206
#> 8215
#> 8216
#> 8220
#> 8221
#> 8225
#> 8226
#> 8236
#> 8239
#> 8241 Central government revenues, excluding all grants.
#> 8244
#> 8246
#> 8248
#> 8249
#> 8252
#> 8253
#> 8254
#> 8280
#> 8281
#> 8299
#> 8302 Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development.
#> 8305
#> 8306
#> 8313 Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities.
#> 8316 Cash surplus or deficit is revenue (including grants) minus expense, minus net acquisition of nonfinancial assets. In the 1986 GFS manual nonfinancial assets were included under revenue and expenditure in gross terms. This cash surplus or deficit is closest to the earlier overall budget balance (still missing is lending minus repayments, which are now a financing item under net acquisition of financial assets).
#> 8321 Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year.
#> 8325 Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets (a third financing item). The difference between the cash surplus or deficit and the three financing items is the net change in the stock of cash.
#> 8327 Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets (a third financing item). The difference between the cash surplus or deficit and the three financing items is the net change in the stock of cash.
#> 8329 Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets.
#> 8331 Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital.
#> 8333 Net lending (+) / net borrowing (–) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad.
#> 8344 Revenue is cash receipts from taxes, social contributions, and other revenues such as fines, fees, rent, and income from property or sales. Grants are also considered as revenue but are excluded here.
#> 8359 Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue.
#> 8376 Expense is cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends.
#> 8466 Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF’s International Financial Statistics. Private credit by deposit money banks (IFS line 22d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8467 Claims on domestic real nonfinancial sector by deposit money banks as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is deposit money bank claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF’s International Financial Statistics. Deposit money bank assets (IFS lines 22, a-d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8468 Claims on domestic real nonfinancial sector by other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is other financial institutions' claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF’s International Financial Statistics. Nonbank financial institutions assets (IFS lines 42, a-d, h, and s); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8470 Ratio of liquid liabilities to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is liquid liabilities, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Liquid liabilities (IFS lines 55L or, if not available, line 35L); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF) For Eurocurrency area countries liquid liabilities are estimated by summing IFS items 34A, 34B and 35.
#> 8471 Claims on domestic real nonfinancial sector by the Central Bank as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is Central Bank claims, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF's International Financial Statistics. Central Bank claims (IFS lines 12, a-d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8472 Data taken from a variety of sources such as Investment Company Institute and national sources.
#> 8473 Demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is demand and time and saving deposits, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF’s International Financial Statistics. Financial system deposits (IFS lines 24, 25, 44, and 45); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8474 Premium data is taken from various issues of Sigma reports (Swiss Re). Data on GDP in US dollars is from the electronic version of the World Development Indicators.
#> 8475 Premium data is taken from various issues of Sigma reports (Swiss Re). Data on GDP in US dollars is from the electronic version of the World Development Indicators.
#> 8476 Data taken from a variety of sources such as AXCO and national sources.
#> 8477 Private credit by deposit money banks and other financial institutions to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is credit to the private sector, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF’s International Financial Statistics. Private credit by deposit money banks and other financial institutions (IFS lines 22d and 42d); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF)
#> 8478 Ratio of assets of pension funds to GDP. A pension fund is any plan, fund, or scheme that provides retirement income. Data taken from a variety of sources such as OECD, AIOS, FIAP and national sources.
#> 8479 Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.
#> 8480 Value of listed shares to GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is stock market capitalization, P_e is end-of period CPI, and P_a is average annual CPI. End-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF) and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8481 Total value of all traded shares in a stock market exchange as a percentage of GDP. Following deflation method is use: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is stock market capitalization, P_e is end-of period CPI, and P_a is average annual CPI. End-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF) and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8482 Total amount of domestic private debt securities (amounts outstanding) issued in domestic markets as a share of GDP. It covers data on long-term bonds and notes, commercial paper and other short-term notes. Table 16A (domestic debt amount): all issuers minus governments / GDP. End of year data (i.e. December data) are considered for debt securities. The figures are deflated using the following methodology: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is the level domestic private debt, P_e is end-of period CPI, and P_a is average annual CPI. GDP is from World Development Indicators. End-of period CPI is taken from IFS line 64M..ZF month of December (or if not available Q4). Average annual CPI is constructed from the monthly CPI figure taken from IFS line 64..ZF.
#> 8483 Total amount of domestic public debt securities (amounts outstanding) issued in domestic markets as a share of GDP. It covers long-term bonds and notes, treasury bills, commercial paper and other short-term notes. Table 16A (domestic debt amount): governments / GDP. End of year data (i.e. December data) are considered for debt securities. The figures are deflated using the following methodology: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is the level domestic public debt, P_e is end-of period CPI, and P_a is average annual CPI. GDP is from World Development Indicators. End-of period CPI is taken from IFS line 64M..ZF month of December (or if not available Q4). Average annual CPI is constructed from the monthly CPI figure taken from IFS line 64..ZF.
#> 8484 Amount of private international debt securities (amounts outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets. (Table 12A (international debt amount: all issuers) - Table 12D (international debt amount: governments)) / GDP. End of year data (i.e. December data) are considered for debt securities. The figures are deflated using the following methodology: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is the level intenational private debt, P_e is end-of period CPI, and P_a is average annual CPI. GDP is from World Development Indicators.End-of period CPI is taken from IFS line 64M..ZF month of December (or if not available Q4). Average annual CPI is constructed from the monthly CPI figure taken from IFS line 64..ZF.
#> 8485 Amount of public international debt securities (amounts outstanding), as a share of GDP. It covers long-term bonds and notes and money market instruments placed on international markets. Table 12D (international debt amount): governments / GDP. End of year data (i.e. December data) are considered for debt securities. The figures are deflated using the following methodology: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is the level international public debt, P_e is end-of period CPI, and P_a is average annual CPI. GDP is from World Development Indicators. End-of period CPI is taken from IFS line 64M..ZF month of December (or if not available Q4). Average annual CPI is constructed from the monthly CPI figure taken from IFS line 64..ZF.
#> 8486 Total value of outstanding international debt issues both public and private, as a share of GDP. Offshore bank loan data from BIS Statistical Appendix Table 12A (Amount Outstanding): International debt securities - all issuers.
#> 8487 Ratio of gross portfolio equity liabilities to GDP. Equity liabilities include shares, stocks, participation, and similar documents (such as American depository receipts) that usually denote ownership of equity. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 79LDDZF/ GDP. Local currency GDP is from IFS (line 99B..ZF or, if not available, line 99B.CZF).
#> 8488 Ratio of gross portfolio equity assets to GDP. Equity assets include shares, stocks, participation, and similar documents (such as American depository receipts) that usually denote ownership of equity. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 79ADDZF / GDP. Local currency GDP is from IFS (line 99B..ZF or, if not available, line 99B.CZF).
#> 8489 Ratio of gross portfolio debt liabilities to GDP. Debt liabilities cover (1) bonds, debentures, notes, etc., and (2) money market or negotiable debt instruments. Raw data are from the electronic version of the IMF's International Financial Statistics. IFS line 79LEDZF / GDP. Local currency GDP is from IFS (line 99B..ZF or, if not available, line 99B.CZF).
#> 8490
#> 8491
#> 8492
#> 8495
#> 8503 Raw data are from the electronic version of the IMF’s International Financial Statistics. (IFS line 22A + line 22B + line 22C) / GDP. Local currency GDP is from IFS (line 99B..ZF or, if not available, line 99B.CZF).
#> 8508 Demand, time and saving deposits in deposit money banks as a share of GDP, calculated using the following deflation method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is demand and time and saving deposits, P_e is end-of period CPI, and P_a is average annual CPI. Raw data are from the electronic version of the IMF’s International Financial Statistics. Bank deposits (IFS lines 24 and 25); GDP in local currency (IFS line 99B..ZF or, if not available, line 99B.CZF); end-of period CPI (IFS line 64M..ZF or, if not available, 64Q..ZF); and average annual CPI is calculated using the monthly CPI values (IFS line 64M..ZF).
#> 8511 Ratio of net offshore bank loans to GDP. An offshore bank is a bank located outside the country of residence of the depositor, typically in a low tax jurisdiction (or tax haven) that provides financial and legal advantages. Offshore bank loan data from BIS Statistical Appendix Table 12A (Net Issues): International debt securities - all issuers.
#> 8512 Ratio of outstanding offshore bank loans to GDP. An offshore bank is a bank located outside the country of residence of the depositor, typically in a low tax jurisdiction (or tax haven) that provides financial and legal advantages. Offshore bank loan data from BIS Statistical Appendix Table 7A: External loans and deposits of reporting banks vis-à-vis all sectors.
#> 8516 Workers' remittances and compensation of employees comprise current transfers by migrant workers and wages and salaries earned by nonresident workers. Data are the sum of three items defined in the fifth edition of the IMF's Balance of Payments Manual: workers' remittances, compensation of employees, and migrants' transfers. Remittances are classified as current private transfers from migrant workers resident in the host country for more than a year, irrespective of their immigration status, to recipients in their country of origin. Migrants' transfers are defined as the net worth of migrants who are expected to remain in the host country for more than one year that is transferred from one country to another at the time of migration. Compensation of employees is the income of migrants who have lived in the host country for less than a year.
#> 8517 The ratio of consolidated foreign claims to GDP of the banks that are reporting to BIS. Foreign claims are defined as the sum of cross-border claims plus foreign offices’ local claims in all currencies. In the consolidated banking statistics claims that are granted or extended to nonresidents are referred to as either cross-border claims. In the context of the consolidated banking statistics, local claims refer to claims of domestic banks’ foreign affiliates (branches/subsidiaries) on the residents of the host country (i.e. country of residence of affiliates). Items (A+L from BIS Table 9A). End-of-year data (i.e. December data) are considered for banks claims. GDP is from World Development Indicators.
#> 8521 Ratios calculated by source.
#> 8522 GDP data provided by IFS and converted into USD using IFS exchange rates.
#> 9365 Information and communications technology expenditures include computer hardware (computers, storage devices, printers, and other peripherals); computer software (operating systems, programming tools, utilities, applications, and internal software development); computer services (information technology consulting, computer and network systems integration, Web hosting, data processing services, and other services); and communications services (voice and data communications services) and wired and wireless communications equipment.
#> 9639
#> 9641
#> 9748 Telecommunications revenue is the revenue from the provision of telecommunications services such as fixed-line, mobile, and data.
#> 11639 Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.)
#> 11644
#> 11645
#> 11646
#> 11647
#> 11648
#> 11649
#> 11650
#> 11651
#> 11652 Construction is an economic activity which produces physical structures. Physical structures include: buildings; roads; bridges; railways and bridgeways; tunnel subways; viaducts and drainage; sanitary construction; airport terminals and airfield construction; dams; electricity power plants; distribution, transmission and communication networks. Construction activities include: planning; preparation; execution; demolition; repairs and maintenance; and construction.
#> 11653 Construction is an economic activity which produces physical structures. Physical structures include: buildings; roads; bridges; railways and bridgeways; tunnel subways; viaducts and drainage; sanitary construction; airport terminals and airfield construction; dams; electricity power plants; distribution, transmission and communication networks. Construction activities include: planning; preparation; execution; demolition; repairs and maintenance; and construction.
#> 11654
#> 11655
#> 11656
#> 11657
#> 11658
#> 11659
#> 11660
#> 11661
#> 11662
#> 11663
#> 11664
#> 11665
#> 11666
#> 11667
#> 11668
#> 11669
#> 11670
#> 11671
#> 11672
#> 11673
#> 11674 Mining is an economic activity which extracts minerals (in solid, liquid or gas form) and prepares these items for further processing. Quarrying is an economic activity that covers all extraction of quarried commodities. Quarried commodities are chemical elements, mineral and recess rock sediments below the earth's surface. Quarried commodities are usually used in manufacturing and construction (excluding metal, coal, petroleum, natural gas and radio active elements).
#> 11675 Mining is an economic activity which extracts minerals (in solid, liquid or gas form) and prepares these items for further processing. Quarrying is an economic activity that covers all extraction of quarried commodities. Quarried commodities are chemical elements, mineral and recess rock sediments below the earth's surface. Quarried commodities are usually used in manufacturing and construction (excluding metal, coal, petroleum, natural gas and radio active elements).
#> 11676
#> 11677
#> 11678 Manufacturing is an economic activity involving processing materials and transforming them mechanically, chemically, or manually into finished or semi finished products and/or converting them into other goods having higher value and closer to the final user.
#> 11679 Manufacturing is an economic activity involving processing materials and transforming them mechanically, chemically, or manually into finished or semi finished products and/or converting them into other goods having higher value and closer to the final user.
#> 11680
#> 11681
#> 11682
#> 11683
#> 11684
#> 11685
#> 11686
#> 11687
#> 11688
#> 11689
#> 11690
#> 11691
#> 11692
#> 11693
#> 11694
#> 11695
#> 11696
#> 11697
#> 11698
#> 11699
#> 11700
#> 11701
#> 11710 General government final consumption expenditure (formerly general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation.
#> 11721 Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. This item also includes any statistical discrepancy in the use of resources relative to the supply of resources.
#> 11738 Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. This item also includes any statistical discrepancy in the use of resources relative to the supply of resources.
#> 11748 Final consumption expenditure (formerly total consumption) is the sum of household final consumption expenditure (private consumption) and general government final consumption expenditure (general government consumption). This estimate includes any statistical discrepancy in the use of resources relative to the supply of resources.
#> 11756
#> 11757 Final consumption expenditure (formerly total consumption) is the sum of household final consumption expenditure (private consumption) and general government final consumption expenditure (general government consumption). This estimate includes any statistical discrepancy in the use of resources relative to the supply of resources.
#> 11767 Gross national expenditure (formerly domestic absorption) is the sum of household final consumption expenditure (formerly private consumption), general government final consumption expenditure (formerly general government consumption), and gross capital formation (formerly gross domestic investment).
#> 11779 Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.
#> 11781 The Indonesian general government sector is classified into two categories: central government; and local government. Central government covers all government bodies affiliated to central government, including all vertical regional boundaries. Local government covers provincial governments, regency governments and village governments. \n\nGovernment final consumption expenditure refers to the value of goods and services produced for own use in the current account. It is the value of gross output less sales of commodities and non-commodities produced.
#> 11782
#> 11783
#> 11784
#> 11785
#> 11786
#> 11787 Export is a trade of goods and services provided by citizens of a country to citizens of another country in exchange for foreign currency. Export creates influx of foreign currency.
#> 11788
#> 11813 Private investment covers gross outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets.
#> 11818 Gross public investment (see definition below) as a percentage of GDP (%) . Public sectors’ gross domestic fixed investment (gross fixed capital formation) comprises all additions to the stocks of fixed assets (purchases and own-account capital formation), less any sales of second-hand and scrapped fixed assets measured at constant prices, done by government units and non-financial public enterprises. Most outlays by government on military equipment are excluded. According to 1993 SNA are outlays on weapons and equipment with no alternative civil use treated as intermediate consumption, and part of governments consumption expenditure.
#> 11821 Gross fixed capital formation is expenditure for capital goods which have an effective life of more than one year and which do not represent commodities for consumption. Gross fixed capital formation includes residential and non residential buildings and other constructions such as roads and airports as well as machinery. Expenditure on capital goods and buildings for military requirements is not included in this breakdown, but rather is classified as government consumption. \n\nGross fixed capital formation, in the general government sector, is defined as the difference between government expenditure on additions to its fixed assets and net sales of similar second-hand and scrapped goods. Items classified as fixed capital formation include: dwelling and non-dwelling buildings; roads, bridges and similar constructions; machinery and equipment; motor vehicles; major repairs and alterations to the above mentioned durable goods which significantly extend their lifetime or productivity; and outlays on the reclamation and improvement of land and the development of plantations.
#> 11828
#> 11829 Gross fixed capital formation (formerly gross domestic fixed investment) includes land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. According to the 1993 SNA, net acquisitions of valuables are also considered capital formation.
#> 11830 Import is a trade of goods and services required by citizens of a country from citizens of other country in exchange for foreign currency. Import creates outflux of foreign currency.
#> 11831
#> 11832
#> 11837 Stock change in a particular year is defined as the difference between the year's final stock and the initial stock. Stock may consist of intermediate goods to be used in the production process, semi-finished goods and unsold finished goods. Those who hold stock include business enterprises, state enterprises and government. Goods categorised as government stock are those held for strategic purposes such as food products.
#> 11841
#> 11850
#> 11857
#> 11858
#> 11859 Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and "work in progress." According to the 1993 SNA, net acquisitions of valuables are also considered capital formation.
#> 11869 Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.
#> 11870 Merchandise trade as a share of GDP is the sum of merchandise exports and imports divided by the value of GDP, all in current US dollars and from the national accounts.
#> 11876
#> 11877 External balance on goods and services (formerly resource balance) equals exports of goods and services minus imports of goods and services (previously nonfactor services).
#> 11880 Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product.
#> 11887
#> 11891
#> 11897
#> 11905 The growth rate of real per capita GDP in agriculture, expressed at an annual rate.
#> 11915 This is the annual rate of growth of agricultural GDP. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1-5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 2. Data are in current local currency.
#> 11916 Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Note: For VAB countries, gross value added at factor cost is used as the denominator.
#> 11936 Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.
#> 11950
#> 11951 Industry (including construction) corresponds to ISIC divisions 05-43 and includes manufacturing (ISIC divisions 10-33). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Note: For VAB countries, gross value added at factor cost is used as the denominator.
#> 11969 This is the discrepancy included in the value added of services, etc. Covered here are any discrepancies noted by national compilers as well as discrepancies arising from linking new and old series in the World Bank data base. Data are in current U.S. dollars.
#> 11970 This is the discrepancy included in the value added of services, etc. Covered here are any discrepancies noted by national compilers as well as discrepancies arising from linking new and old series in the World Bank data base. Data are in current local currency.
#> 11971 This is the discrepancy included in the value added of services, etc. Covered here are any discrepancies noted by national compilers as well as discrepancies arising from linking new and old series in the World Bank data base. Data are in constant local currency.
#> 11988
#> 11989 Services correspond to ISIC divisions 50-99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.
#> 11995 Services correspond to ISIC divisions 50-99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.
#> 12084 Agriculture support is the annual monetary value of all gross transfers from taxpayers and consumers, both domestic and foreign (in the form of subsidies arising from policy measures that support agriculture), net of the associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or consumption of farm products.
#> 12088 Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production.
#> 12089
#> 12090 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.
#> 12091 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.
#> 12092 The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country.
#> 12093
#> 12094 The GDP implicit deflator is calculated as the ratio of GDP in current local currency to GDP in constant local currency. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The base year varies by country.
#> 12095 This is the discrepancy included in the ‘total consumption etc.' This discrepancy is included to ensures that GDP from the expenditure side equals GDP measured by the income or output approach. Data are in current U.S. dollars.
#> 12096 Discrepancy in expenditure estimate of GDP is the discrepancy included in final consumption expenditure, etc. (total consumption, etc.). This discrepancy is included to ensure that GDP from the expenditure side equals GDP measured by the income or output approach. Data are in current local currency.
#> 12097 A statistical discrepancy usually arises when the GDP components are estimated independently by industrial origin and by expenditure categories. This item represents the discrepancy in the use of resources (i.e., the estimate of GDP by expenditure categories). Data are in constant local currency.
#> 12101
#> 12103
#> 12104 Forest rents are roundwood harvest times the product of regional prices and a regional rental rate.
#> 12105 Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.
#> 12106 GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.
#> 12107 The GDP deflator series based upon the U.S. dollar series is defined as the ratio of the GDP at market prices in current U.S. dollars) to the GDP at market prices in constant (2000) U.S. dollars.
#> 12108 GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current local currency.
#> 12109 GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. Data are in current local currency.
#> 12110 The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.
#> 12111
#> 12112 GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.
#> 12113
#> 12114 Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
#> 12115 GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant local currency.
#> 12116
#> 12117
#> 12118 This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries. From April 2020, “GDP: linked series (current LCU)” [NY.GDP.MKTP.CN.AD] is used as underlying GDP in local currency unit so that it’s in line with time series of PPP conversion factors for GDP, which are extrapolated with linked GDP deflators.
#> 12119 PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2017 international dollars.
#> 12120
#> 12121
#> 12122 The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country.
#> 12124 Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production.
#> 12125 GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars.
#> 12126 GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current local currency.
#> 12127 GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.
#> 12128 Annual percentage growth rate of GDP per capita based on constant local currency. GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
#> 12129 GDP per capita is gross domestic product divided by midyear population. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant local currency.
#> 12130 This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
#> 12131 GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2017 international dollars.
#> 12132
#> 12133 Annual percentage growth rate of GDP per capita based on purchasing power parity (PPP). GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2000 international dollars.
#> 12134 Oil rents are the difference between the value of crude oil production at regional prices and total costs of production.
#> 12135 Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents.
#> 12148 Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption).
#> 12153
#> 12154
#> 12155
#> 12156
#> 12157
#> 12158
#> 12159
#> 12160
#> 12193 Gross savings are calculated as gross national income less total consumption, plus net transfers.
#> 12231 GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
#> 12232
#> 12233
#> 12234
#> 12235
#> 12261 Purchasing power parity (PPP) conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries, thereby allowing volume comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for GDP.
#> 12262 Purchasing power parity conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States. This conversion factor is for GDP. Historical estimates are provided for the 2005 benchmark year only. A separate series is available for extrapolated estimates based on the latest ICP round.
#> 12263 Price level ratio is the ratio of a purchasing power parity (PPP) conversion factor to an exchange rate. It provides a measure of the differences in price levels between countries by indicating the number of units of the common currency needed to buy the same volume of the aggregation level in each country. At the level of GDP, they provide a measure of the differences in the general price levels of countries.
#> 16405
#> 16469
#> 16696
#> 16701
#> 16702 Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.
#> 16705
#> 16706 Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.
#> 16710
#> 16711 Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.
#> 16714 General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.
#> 16734
#> 17722 Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.
#> 17731 Public expenditure on health from domestic sources as a share of the economy as measured by GDP.
#> 17735
#> 17736 Level of capital investments on health expressed as a percentage of GDP. Capital health investments include health infrastructure (buildings, machinery, IT) and stocks of vaccines for emergency or outbreaks.
#> 17748 Private health expenditure includes direct household (out-of-pocket) spending, private insurance, charitable donations, and direct service payments by private corporations.
#> 17751 Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds.
#> 17757 Total health expenditure is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation.
#> 17867 GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2017 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States.
#> 17868 GDP per person employed is gross domestic product (GDP) divided by total employment in the economy.
#> 17869 GDP per person employed is presented as an index with base year 2000 = 100. GDP per person employed is gross domestic product (GDP) divided by total employment.
#> 18629
#> 18630 Merchandise trade as a share of GDP is the sum of merchandise exports and imports divided by the value of GDP, all in current U.S. dollars.
#> 18631
#> 20862 Total general (local, regional and central) government expenditure on pre-primary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20863 Total general (local, regional and central) government expenditure on primary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20864 Total general (local, regional and central) government expenditure on lower secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20865 Total general (local, regional and central) government expenditure on secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20866 Total general (local, regional and central) government expenditure on secondary and post-secondary non-tertiary vocational education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20867 Total general (local, regional and central) government expenditure on upper secondary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20868 Total general (local, regional and central) government expenditure on post-secondary non-tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20869 Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20919 Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20920 Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20921 Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20922 Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20923 Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20924 Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20925 Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20926 Total general (local, regional and central, current and capital) initial government funding of education per student, which includes transfers paid (such as scholarships to students), but excludes transfers received, in this case international transfers to government for education (when foreign donors provide education sector budget support or other support integrated in the government budget). Calculation Method: Total general (local, regional and central) government expenditure (current and capital) on a given level of education (primary, secondary, etc) minus international transfers to government for education, divided by the number of student enrolled at that level of education. This is then expressed as a share of GDP per capita. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. There are also cases where it may not be possible to separate international transfers to government from general government expenditure on education, in which cases they have not been subtracted in the formula. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> 20927 Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport) and purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes). 'Initial funding' means that government transfers to households, such as scholarships and other financial aid for education, are subtracted from what is spent by households. Note that in some countries for some education levels, the value of this indicator may be 0, since on average households may be receiving as much, or more, in financial aid from the government than what they are spending on education. Calculation: Total payments of households (pupils, students and their families) for educational institutions (such as for tuition fees, exam and registration fees, contribution to Parent-Teacher associations or other school funds, and fees for canteen, boarding and transport), plus purchases outside of educational institutions (such as for uniforms, textbooks, teaching materials, or private classes), minus government education transfers to households (such as scholarships or other education-specific financial aid). When expressed as a share of GDP, this is then divided by the country's Gross Domestic Product (GDP). Limitations: Indicators for household expenditure on education should be interpreted with caution since data comes from household surveys which may not all follow the same definitions and concepts. These types of surveys are also not carried out in all countries with regularity, and for some categories (such as pupils in pre-primary education), the sample sizes may be low. In some cases where data on government transfers to households (scholarships and other financial aid) was not available, they could not be subtracted from amounts paid by households. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/
#> sourceDatabase
#> 712 Statistical Capacity Indicators
#> 714 LAC Equity Lab
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#> 1558 World Development Indicators
#> 1559 WDI Database Archives
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#> 1863 Worldwide Bureaucracy Indicators
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#> 1916 Africa Development Indicators
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#> 1933 WDI Database Archives
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#> 1950 Africa Development Indicators
#> 1999 WDI Database Archives
#> 2011 WDI Database Archives
#> 2013 World Development Indicators
#> 2022 Africa Development Indicators
#> 2028 World Development Indicators
#> 2029 Africa Development Indicators
#> 2326 Country Climate and Development Report (CCDR)
#> 2327 Country Climate and Development Report (CCDR)
#> 2402 Country Climate and Development Report (CCDR)
#> 2403 Country Climate and Development Report (CCDR)
#> 2426 Country Climate and Development Report (CCDR)
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#> 2491 Country Climate and Development Report (CCDR)
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#> 2543 WDI Database Archives
#> 2546 World Development Indicators
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#> 3258 Quarterly Public Sector Debt
#> 3261 Quarterly Public Sector Debt
#> 3757 Africa Development Indicators
#> 3760 Africa Development Indicators
#> 3915 Africa Development Indicators
#> 5518 Africa Development Indicators
#> 5589 Africa Development Indicators
#> 5594 Africa Development Indicators
#> 5602 Africa Development Indicators
#> 5608 Africa Development Indicators
#> 5758 Africa Development Indicators
#> 6110 World Development Indicators
#> 6134 WDI Database Archives
#> 6135 WDI Database Archives
#> 6136 World Development Indicators
#> 6137 World Development Indicators
#> 6145 World Development Indicators
#> 6164 WDI Database Archives
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#> 6169 World Development Indicators
#> 6174 World Development Indicators
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#> 6305 World Development Indicators
#> 6323 WDI Database Archives
#> 6377 WDI Database Archives
#> 6730 World Development Indicators
#> 6736 Africa Development Indicators
#> 8052 Africa Development Indicators
#> 8061 World Development Indicators
#> 8070 World Development Indicators
#> 8077 WDI Database Archives
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#> 8125 World Development Indicators
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#> 8241 Africa Development Indicators
#> 8244 WDI Database Archives
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#> 8281 WDI Database Archives
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#> 8302 World Development Indicators
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#> 8313 World Development Indicators
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#> 8329 World Development Indicators
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#> 8376 World Development Indicators
#> 8466 Global Financial Development
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#> 9365 Africa Development Indicators
#> 9639 WDI Database Archives
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#> 9748 Africa Development Indicators
#> 11639 World Development Indicators
#> 11644 Indonesia Database for Policy and Economic Research
#> 11645 Indonesia Database for Policy and Economic Research
#> 11646 Indonesia Database for Policy and Economic Research
#> 11647 Indonesia Database for Policy and Economic Research
#> 11648 Indonesia Database for Policy and Economic Research
#> 11649 Indonesia Database for Policy and Economic Research
#> 11650 Indonesia Database for Policy and Economic Research
#> 11651 Indonesia Database for Policy and Economic Research
#> 11652 Indonesia Database for Policy and Economic Research
#> 11653 Indonesia Database for Policy and Economic Research
#> 11654 Indonesia Database for Policy and Economic Research
#> 11655 Indonesia Database for Policy and Economic Research
#> 11656 Indonesia Database for Policy and Economic Research
#> 11657 Indonesia Database for Policy and Economic Research
#> 11658 Indonesia Database for Policy and Economic Research
#> 11659 Indonesia Database for Policy and Economic Research
#> 11660 Indonesia Database for Policy and Economic Research
#> 11661 Indonesia Database for Policy and Economic Research
#> 11662 Indonesia Database for Policy and Economic Research
#> 11663 Indonesia Database for Policy and Economic Research
#> 11664 Indonesia Database for Policy and Economic Research
#> 11665 Indonesia Database for Policy and Economic Research
#> 11666 Indonesia Database for Policy and Economic Research
#> 11667 Indonesia Database for Policy and Economic Research
#> 11668 Indonesia Database for Policy and Economic Research
#> 11669 Indonesia Database for Policy and Economic Research
#> 11670 Indonesia Database for Policy and Economic Research
#> 11671 Indonesia Database for Policy and Economic Research
#> 11672 Indonesia Database for Policy and Economic Research
#> 11673 Indonesia Database for Policy and Economic Research
#> 11674 Indonesia Database for Policy and Economic Research
#> 11675 Indonesia Database for Policy and Economic Research
#> 11676 Indonesia Database for Policy and Economic Research
#> 11677 Indonesia Database for Policy and Economic Research
#> 11678 Indonesia Database for Policy and Economic Research
#> 11679 Indonesia Database for Policy and Economic Research
#> 11680 Indonesia Database for Policy and Economic Research
#> 11681 Indonesia Database for Policy and Economic Research
#> 11682 Indonesia Database for Policy and Economic Research
#> 11683 Indonesia Database for Policy and Economic Research
#> 11684 Indonesia Database for Policy and Economic Research
#> 11685 Indonesia Database for Policy and Economic Research
#> 11686 Indonesia Database for Policy and Economic Research
#> 11687 Indonesia Database for Policy and Economic Research
#> 11688 Indonesia Database for Policy and Economic Research
#> 11689 Indonesia Database for Policy and Economic Research
#> 11690 Indonesia Database for Policy and Economic Research
#> 11691 Indonesia Database for Policy and Economic Research
#> 11692 Indonesia Database for Policy and Economic Research
#> 11693 Indonesia Database for Policy and Economic Research
#> 11694 Indonesia Database for Policy and Economic Research
#> 11695 Indonesia Database for Policy and Economic Research
#> 11696 Indonesia Database for Policy and Economic Research
#> 11697 Indonesia Database for Policy and Economic Research
#> 11698 Indonesia Database for Policy and Economic Research
#> 11699 Indonesia Database for Policy and Economic Research
#> 11700 Indonesia Database for Policy and Economic Research
#> 11701 Indonesia Database for Policy and Economic Research
#> 11710 World Development Indicators
#> 11721 WDI Database Archives
#> 11738 World Development Indicators
#> 11748 WDI Database Archives
#> 11756 WDI Database Archives
#> 11757 World Development Indicators
#> 11767 World Development Indicators
#> 11779 World Development Indicators
#> 11781 Indonesia Database for Policy and Economic Research
#> 11782 Indonesia Database for Policy and Economic Research
#> 11783 Indonesia Database for Policy and Economic Research
#> 11784 Indonesia Database for Policy and Economic Research
#> 11785 Indonesia Database for Policy and Economic Research
#> 11786 Indonesia Database for Policy and Economic Research
#> 11787 Indonesia Database for Policy and Economic Research
#> 11788 Indonesia Database for Policy and Economic Research
#> 11813 World Development Indicators
#> 11818 Africa Development Indicators
#> 11821 Indonesia Database for Policy and Economic Research
#> 11828 Indonesia Database for Policy and Economic Research
#> 11829 World Development Indicators
#> 11830 Indonesia Database for Policy and Economic Research
#> 11831 Indonesia Database for Policy and Economic Research
#> 11832 Indonesia Database for Policy and Economic Research
#> 11837 Indonesia Database for Policy and Economic Research
#> 11841 Indonesia Database for Policy and Economic Research
#> 11850 Indonesia Database for Policy and Economic Research
#> 11857 Indonesia Database for Policy and Economic Research
#> 11858 WDI Database Archives
#> 11859 World Development Indicators
#> 11869 World Development Indicators
#> 11870 Africa Development Indicators
#> 11876 WDI Database Archives
#> 11877 World Development Indicators
#> 11880 World Development Indicators
#> 11887 WDI Database Archives
#> 11891 WDI Database Archives
#> 11897 WDI Database Archives
#> 11905 Africa Development Indicators
#> 11915 Africa Development Indicators
#> 11916 World Development Indicators
#> 11936 World Development Indicators
#> 11950 WDI Database Archives
#> 11951 World Development Indicators
#> 11969 Africa Development Indicators
#> 11970 Africa Development Indicators
#> 11971 Africa Development Indicators
#> 11988 WDI Database Archives
#> 11989 WDI Database Archives
#> 11995 World Development Indicators
#> 12084 Millennium Development Goals
#> 12088 World Development Indicators
#> 12089 WDI Database Archives
#> 12090 World Development Indicators
#> 12091 World Development Indicators
#> 12092 World Development Indicators
#> 12093 WDI Database Archives
#> 12094 World Development Indicators
#> 12095 Africa Development Indicators
#> 12096 World Development Indicators
#> 12097 World Development Indicators
#> 12101 WDI Database Archives
#> 12103 WDI Database Archives
#> 12104 World Development Indicators
#> 12105 World Development Indicators
#> 12106 World Development Indicators
#> 12107 Africa Development Indicators
#> 12108 World Development Indicators
#> 12109 World Development Indicators
#> 12110 Africa Development Indicators
#> 12111 WDI Database Archives
#> 12112 World Development Indicators
#> 12113 WDI Database Archives
#> 12114 World Development Indicators
#> 12115 World Development Indicators
#> 12116 WDI Database Archives
#> 12117 WDI Database Archives
#> 12118 World Development Indicators
#> 12119 World Development Indicators
#> 12120 WDI Database Archives
#> 12121 WDI Database Archives
#> 12122 Africa Development Indicators
#> 12124 World Development Indicators
#> 12125 World Development Indicators
#> 12126 World Development Indicators
#> 12127 World Development Indicators
#> 12128 World Development Indicators
#> 12129 World Development Indicators
#> 12130 World Development Indicators
#> 12131 World Development Indicators
#> 12132 WDI Database Archives
#> 12133 Africa Development Indicators
#> 12134 World Development Indicators
#> 12135 World Development Indicators
#> 12148 World Development Indicators
#> 12153 WDI Database Archives
#> 12154 WDI Database Archives
#> 12155 WDI Database Archives
#> 12156 WDI Database Archives
#> 12157 WDI Database Archives
#> 12158 WDI Database Archives
#> 12159 WDI Database Archives
#> 12160 WDI Database Archives
#> 12193 World Development Indicators
#> 12231 Global Economic Prospects
#> 12232 Global Economic Monitor
#> 12233 Global Economic Monitor
#> 12234 Global Economic Monitor
#> 12235 Global Economic Monitor
#> 12261 World Development Indicators
#> 12262 WDI Database Archives
#> 12263 World Development Indicators
#> 16405 Education Policy
#> 16469 Education Policy
#> 16696 WDI Database Archives
#> 16701 WDI Database Archives
#> 16702 World Development Indicators
#> 16705 WDI Database Archives
#> 16706 World Development Indicators
#> 16710 WDI Database Archives
#> 16711 World Development Indicators
#> 16714 World Development Indicators
#> 16734 WDI Database Archives
#> 17722 World Development Indicators
#> 17731 World Development Indicators
#> 17735 WDI Database Archives
#> 17736 Health Nutrition and Population Statistics
#> 17748 WDI Database Archives
#> 17751 WDI Database Archives
#> 17757 WDI Database Archives
#> 17867 World Development Indicators
#> 17868 Africa Development Indicators
#> 17869 WDI Database Archives
#> 18629 WDI Database Archives
#> 18630 World Development Indicators
#> 18631 WDI Database Archives
#> 20862 Education Statistics
#> 20863 Education Statistics
#> 20864 Education Statistics
#> 20865 Education Statistics
#> 20866 Education Statistics
#> 20867 Education Statistics
#> 20868 Education Statistics
#> 20869 Education Statistics
#> 20919 Education Statistics
#> 20920 Education Statistics
#> 20921 Education Statistics
#> 20922 Education Statistics
#> 20923 Education Statistics
#> 20924 Education Statistics
#> 20925 Education Statistics
#> 20926 Education Statistics
#> 20927 Education Statistics
#> sourceOrganization
#> 712 World Development Indicator (WDI) databank. Original source: World Bank national accounts data, and OECD National Accounts data files
#> 714 World Development Indicators (World Bank)
#> 715 World Development Indicators (World Bank)
#> 716 World Development Indicators (World Bank)
#> 717 World Development Indicators (World Bank)
#> 1558 International Monetary Fund, Balance of Payments Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 1559
#> 1560
#> 1561
#> 1562
#> 1863
#> 1883
#> 1895 International Monetary Fund, International Financial Statistics and Balance of Payments databases, World Bank, International Debt Statistics, and World Bank and OECD GDP estimates.
#> 1896 International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.
#> 1909 International Monetary Fund, Balance of Payments Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 1910
#> 1913
#> 1916 World Bank country economists.
#> 1922 International Monetary Fund, Balance of Payments Statistics Yearbook and data files. World Bank GDP estimates are used for the denominator.
#> 1931 World Bank country economists.
#> 1933
#> 1939 International Monetary Fund, Balance of Payments Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 1950 International Monetary Fund, Balance of Payments Statistics Yearbook and data files. World Bank GDP estimates are used for the denominator.
#> 1999
#> 2011
#> 2013 International Monetary Fund, International Financial Statistics and Balance of Payments databases, World Bank, International Debt Statistics, and World Bank and OECD GDP estimates.
#> 2022 World Bank staff estimates based on the International Monetary Fund's Balance of Payments Statistics Yearbook 2008, and World Bank and OECD GDP estimates.
#> 2028 World Bank staff estimates based on IMF balance of payments data, and World Bank and OECD GDP estimates.
#> 2029 International Monetary Fund, Balance of Payments Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 2326 Policy Instrument for the Environment Data Base, OECD. Available at: https://www.oecd.org/env/indicators-modelling-outlooks/policy-instrument-database/
#> 2327
#> 2402 Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org.
#> 2403 Climate Watch. 2020. Washington, DC: World Resources Institute. Available online at: https://www.climatewatchdata.org.
#> 2426 Rozenberg, Julie; Fay, Marianne. 2019. Beyond the Gap : How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31291 License: CC BY 3.0 IGO
#> 2427 Rozenberg, Julie; Fay, Marianne. 2019. Beyond the Gap : How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31291 License: CC BY 3.0 IGO
#> 2428 Rozenberg, Julie; Fay, Marianne. 2019. Beyond the Gap : How Countries Can Afford the Infrastructure They Need while Protecting the Planet. Sustainable Infrastructure;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/31291 License: CC BY 3.0 IGO
#> 2491 Hallegatte, Stephane; Vogt-Schilb, Adrien; Bangalore, Mook; Rozenberg, Julie. 2017. Unbreakable : Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/25335 License: CC BY 3.0 IGO.
#> 2492 Hallegatte, Stephane; Vogt-Schilb, Adrien; Bangalore, Mook; Rozenberg, Julie. 2017. Unbreakable : Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/25335 License: CC BY 3.0 IGO.
#> 2501
#> 2543
#> 2546 World Federation of Exchanges database.
#> 2549 World Federation of Exchanges database.
#> 2701
#> 2704
#> 2707
#> 2710
#> 2713
#> 2718
#> 2721
#> 2724
#> 2727
#> 2730
#> 2735
#> 2738
#> 2741
#> 2744
#> 2747
#> 2752
#> 2755
#> 2758
#> 2761
#> 2764
#> 2769
#> 2772
#> 2775
#> 2778
#> 2781
#> 2786
#> 2789
#> 2792
#> 2795
#> 2798
#> 2801
#> 2804
#> 2807
#> 2810
#> 2815
#> 2818
#> 2821
#> 2824
#> 2827
#> 2832
#> 2836
#> 2838
#> 2840
#> 2842
#> 2844
#> 2846
#> 2848
#> 2850
#> 2852
#> 2854
#> 2857
#> 2860
#> 2863
#> 2866
#> 2869
#> 2872
#> 2875
#> 2878
#> 2881
#> 2886
#> 2889
#> 2892
#> 2895
#> 2898
#> 2903
#> 2906
#> 2909
#> 2912
#> 2915
#> 2920
#> 2925
#> 2928
#> 2931
#> 2934
#> 2937
#> 2940
#> 2943
#> 2946
#> 2949
#> 2954
#> 2957
#> 2960
#> 2963
#> 2966
#> 2971
#> 2976
#> 2979
#> 2982
#> 2985
#> 2988
#> 2991
#> 2994
#> 2997
#> 3000
#> 3005
#> 3008
#> 3011
#> 3014
#> 3017
#> 3022
#> 3027
#> 3030
#> 3033
#> 3036
#> 3039
#> 3042
#> 3045
#> 3048
#> 3051
#> 3056
#> 3059
#> 3062
#> 3065
#> 3068
#> 3073
#> 3078
#> 3081
#> 3084
#> 3087
#> 3090
#> 3093
#> 3096
#> 3099
#> 3102
#> 3107
#> 3112
#> 3115
#> 3118
#> 3121
#> 3124
#> 3127
#> 3130
#> 3133
#> 3136
#> 3141
#> 3144
#> 3147
#> 3150
#> 3153
#> 3158
#> 3164
#> 3167
#> 3170
#> 3173
#> 3176
#> 3181
#> 3184
#> 3187
#> 3190
#> 3193
#> 3198
#> 3201
#> 3204
#> 3207
#> 3210
#> 3215
#> 3218
#> 3221
#> 3224
#> 3227
#> 3232
#> 3235
#> 3238
#> 3241
#> 3244
#> 3249
#> 3252
#> 3255
#> 3258
#> 3261
#> 3757 World Bank, Global Development Finance.
#> 3760 World Bank, Global Development Finance.
#> 3915 World Bank, Global Development Finance.
#> 5518 Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline. World Bank GDP estimates are used for the denominator.
#> 5589 Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline. World Bank gross domestic product estimates are used for the denominator.
#> 5594 Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline. World Bank gross domestic product estimates are used for the denominator.
#> 5602 Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline. World Bank gross domestic product estimates are used for the denominator.
#> 5608 Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline. World Bank GDP estimates are used for the denominator.
#> 5758 World Bank, Global Development Finance.
#> 6110 World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.
#> 6134
#> 6135
#> 6136 IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/
#> 6137 IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/
#> 6145 IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/
#> 6164
#> 6168
#> 6169 Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.KD for the denominator's source.
#> 6174 Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.CD for the denominator's source.
#> 6175 Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See NY.GDP.MKTP.PP.KD for the denominator's source.
#> 6305 Food and Agriculture Organization, AQUASTAT data, and World Bank and OECD GDP estimates.
#> 6323
#> 6377
#> 6730 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 6736 International Monetary Fund, International Financial Statistics and data files.
#> 8052 "International Monetary Fund.\r"
#> 8061 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8070 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8077 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8078
#> 8080
#> 8085
#> 8086
#> 8125 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8126 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8127 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8128
#> 8130 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8131
#> 8132 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8133
#> 8134 International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.
#> 8205
#> 8206
#> 8215
#> 8216
#> 8220
#> 8221
#> 8225
#> 8226
#> 8236
#> 8239
#> 8241 World Bank country economists.
#> 8244
#> 8246
#> 8248
#> 8249
#> 8252
#> 8253
#> 8254
#> 8280
#> 8281
#> 8299
#> 8302 UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed October 24, 2022. https://apiportal.uis.unesco.org/bdds.
#> 8305
#> 8306
#> 8313 International Monetary Fund, Government Finance Statistics Yearbook and data files.
#> 8316 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8321 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8325 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8327 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8329 International Monetary Fund, Government Finance Statistics Yearbook and data files.
#> 8331 International Monetary Fund, Government Finance Statistics Yearbook and data files.
#> 8333 International Monetary Fund, Government Finance Statistics Yearbook and data files.
#> 8344 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8359 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8376 International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.
#> 8466 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8467 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8468 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8470 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8471 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8472 World Bank - Non banking financial database
#> 8473 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8474 Sigma Reports, Swiss Re
#> 8475 Sigma Reports, Swiss Re
#> 8476 Nonbanking financial database, World Bank
#> 8477 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8478 Nonbanking financial database, World Bank
#> 8479 World Development Indicators (WDI), World Bank
#> 8480 Global Stock Markets Factbook and supplemental S&P data, Standard & Poor's
#> 8481 Global Stock Markets Factbook and supplemental S&P data, Standard & Poor's
#> 8482 Bank for International Settlements (BIS)
#> 8483 Bank for International Settlements (BIS)
#> 8484 Bank for International Settlements (BIS)
#> 8485 Bank for International Settlements (BIS)
#> 8486 Bank for International Settlements (BIS)
#> 8487 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8488 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8489 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8490
#> 8491
#> 8492
#> 8495
#> 8503 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8508 International Financial Statistics (IFS), International Monetary Fund (IMF)
#> 8511 Bank for International Settlements (BIS)
#> 8512 Bank for International Settlements (BIS)
#> 8516 World Development Indicators (WDI), World Bank
#> 8517 Consolidated banking statistics, Bank for International Settlements (BIS)
#> 8521 White Clarke Global Leasing Report
#> 8522 Factors Chain International
#> 9365 World Information Technology and Services Alliance, Digital Planet: The Global Information Economy, and Global Insight, Inc.
#> 9639
#> 9641
#> 9748 International Telecommunication Union, World Telecommunication/ICT Development Report and database, and World Bank estimates.
#> 11639 Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.
#> 11644
#> 11645
#> 11646 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11647 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11648
#> 11649
#> 11650
#> 11651
#> 11652 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11653 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11654
#> 11655
#> 11656
#> 11657
#> 11658
#> 11659
#> 11660 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11661 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11662 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11663 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11664
#> 11665
#> 11666
#> 11667
#> 11668 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11669 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11670
#> 11671
#> 11672
#> 11673
#> 11674 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11675 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11676
#> 11677
#> 11678 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11679 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11680
#> 11681
#> 11682
#> 11683
#> 11684
#> 11685
#> 11686 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11687 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11688
#> 11689
#> 11690 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11691 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11692
#> 11693
#> 11694 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11695 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11696
#> 11697
#> 11698 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11699 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11700
#> 11701
#> 11710 World Bank national accounts data, and OECD National Accounts data files.
#> 11721 World Bank national accounts data, and OECD National Accounts data files.
#> 11738 World Bank national accounts data, and OECD National Accounts data files.
#> 11748 World Bank national accounts data, and OECD National Accounts data files.
#> 11756
#> 11757 World Bank national accounts data, and OECD National Accounts data files.
#> 11767 World Bank national accounts data, and OECD National Accounts data files.
#> 11779 World Bank national accounts data, and OECD National Accounts data files.
#> 11781 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11782
#> 11783 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11784
#> 11785 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11786
#> 11787 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11788
#> 11813 World Bank national accounts data, and OECD National Accounts data files.
#> 11818 World Bank national accounts data, and OECD National Accounts data files.
#> 11821 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11828
#> 11829 World Bank national accounts data, and OECD National Accounts data files.
#> 11830 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11831
#> 11832
#> 11837 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11841
#> 11850 BADAN PUSAT STATISTIK - Statistics Indonesia
#> 11857
#> 11858
#> 11859 World Bank national accounts data, and OECD National Accounts data files.
#> 11869 World Bank national accounts data, and OECD National Accounts data files.
#> 11870 World Bank country economists.
#> 11876
#> 11877 World Bank national accounts data, and OECD National Accounts data files.
#> 11880 World Bank national accounts data, and OECD National Accounts data files.
#> 11887
#> 11891
#> 11897
#> 11905 World Bank country economists.
#> 11915 World Bank country economists.
#> 11916 World Bank national accounts data, and OECD National Accounts data files.
#> 11936 World Bank national accounts data, and OECD National Accounts data files.
#> 11950
#> 11951 World Bank national accounts data, and OECD National Accounts data files.
#> 11969 World Bank national accounts data, and OECD National Accounts data files.
#> 11970 World Bank national accounts data, and OECD National Accounts data files.
#> 11971 World Bank national accounts data, and OECD National Accounts data files.
#> 11988
#> 11989 World Bank national accounts data, and OECD National Accounts data files.
#> 11995 World Bank national accounts data, and OECD National Accounts data files.
#> 12084 Organisation for Economic Co-operation and Development, Producer and Consumer Support Estimates database. Available online at www.oecd.org/tad/support/psecse.
#> 12088 World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations.
#> 12089
#> 12090 World Bank national accounts data, and OECD National Accounts data files.
#> 12091 World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.
#> 12092 World Bank national accounts data, and OECD National Accounts data files.
#> 12093
#> 12094 World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.
#> 12095 World Bank national accounts data, and OECD National Accounts data files.
#> 12096 World Bank national accounts data, and OECD National Accounts data files.
#> 12097 World Bank national accounts data, and OECD National Accounts data files.
#> 12101
#> 12103
#> 12104 World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations.
#> 12105 World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations.
#> 12106 World Bank national accounts data, and OECD National Accounts data files.
#> 12107 World Bank country economists.
#> 12108 World Bank national accounts data, and OECD National Accounts data files.
#> 12109 World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.
#> 12110 World Bank country economists.
#> 12111
#> 12112 World Bank national accounts data, and OECD National Accounts data files.
#> 12113
#> 12114 World Bank national accounts data, and OECD National Accounts data files.
#> 12115 World Bank national accounts data, and OECD National Accounts data files.
#> 12116
#> 12117
#> 12118 International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
#> 12119 International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
#> 12120
#> 12121
#> 12122 World Bank national accounts data, and OECD National Accounts data files.
#> 12124 World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations.
#> 12125 World Bank national accounts data, and OECD National Accounts data files.
#> 12126 World Bank national accounts data, and OECD National Accounts data files.
#> 12127 World Bank national accounts data, and OECD National Accounts data files.
#> 12128 World Bank national accounts data, and OECD National Accounts data files.
#> 12129 World Bank national accounts data, and OECD National Accounts data files.
#> 12130 International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
#> 12131 International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
#> 12132
#> 12133 "World Bank, International Comparison Programme database.\r"
#> 12134 World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations.
#> 12135 World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations.
#> 12148 World Bank national accounts data, and OECD National Accounts data files.
#> 12153
#> 12154
#> 12155
#> 12156
#> 12157
#> 12158
#> 12159
#> 12160
#> 12193 World Bank national accounts data, and OECD National Accounts data files.
#> 12231 The World Bank
#> 12232
#> 12233
#> 12234
#> 12235
#> 12261 International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
#> 12262 World Bank, International Comparison Program database.
#> 12263 International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
#> 16405 World Bank, Global Education Policy Dashboard
#> 16469 World Bank, Global Education Policy Dashboard
#> 16696
#> 16701
#> 16702 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.
#> 16705
#> 16706 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.
#> 16710
#> 16711 UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.
#> 16714 UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed October 24, 2022. https://apiportal.uis.unesco.org/bdds.
#> 16734
#> 17722 World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on January 30, 2022.
#> 17731 World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on January 30, 2022.
#> 17735
#> 17736 World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on January 30, 2022.
#> 17748 World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).
#> 17751 World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).
#> 17757 World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).
#> 17867 World Bank, World Development Indicators database. Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank.
#> 17868 International Labour Organization, Key Indicators of the Labour Market database.
#> 17869 Derived using data from International Labour Organization, ILOSTAT database. The data retrieved in March 1, 2020.
#> 18629
#> 18630 World Trade Organization, and World Bank GDP estimates.
#> 18631
#> 20862 UNESCO Institute for Statistics
#> 20863 UNESCO Institute for Statistics
#> 20864 UNESCO Institute for Statistics
#> 20865 UNESCO Institute for Statistics
#> 20866 UNESCO Institute for Statistics
#> 20867 UNESCO Institute for Statistics
#> 20868 UNESCO Institute for Statistics
#> 20869 UNESCO Institute for Statistics
#> 20919 UNESCO Institute for Statistics
#> 20920 UNESCO Institute for Statistics
#> 20921 UNESCO Institute for Statistics
#> 20922 UNESCO Institute for Statistics
#> 20923 UNESCO Institute for Statistics
#> 20924 UNESCO Institute for Statistics
#> 20925 UNESCO Institute for Statistics
#> 20926 UNESCO Institute for Statistics
#> 20927 UNESCO Institute for Statistics7.9 World Development Indicators - Summary
Find indicators:
WDIsearch(string = "gdp", field = "name", short = FALSE, cache = NULL)
WDIsearch(string = "gdp", field = "name", short = FALSE, cache = wdi_cache)WDIsearch(string = "NY.GDP.PCAP.KD", field = "indicator", short = FALSE, cache = NULL)
- WDI: Data Themes
- Browse by Indicators: https://data.worldbank.org/indicator
- Featured Indicators or All Indicators
- Obtain the indicator from the detail or the URL
7.9.1 Example: CO2 emissions (metric tons per capita)
- ID: EN.ATM.CO2E.PC
- URL: https://data.worldbank.org/indicator/EN.ATM.CO2E.PC
WDIsearch(string = "EN.ATM.CO2E.PC", field = "indicator",
short = FALSE, cache = wdi_cache)
#> indicator name
#> 6173 EN.ATM.CO2E.PC CO2 emissions (metric tons per capita)
#> description
#> 6173 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.
#> sourceDatabase
#> 6173 World Development Indicators
#> sourceOrganization
#> 6173 Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: https://www.climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator's source.
WDIsearch(string = "EN.ATM.CO2E.PC", field = "indicator",
short = FALSE, cache = wdi_cache) %>% pull(description)
#> [1] "Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring."- Source: Climate Watch. 2020. GHG Emissions. Washington, DC: World Resources Institute. Available at: climatewatchdata.org/ghg-emissions. See SP.POP.TOTL for the denominator’s source.
co2pcap <- WDI(country = "all", indicator = "EN.ATM.CO2E.PC", start = 1960, end = NULL, extra = TRUE, cache = wdi_cache)
co2pcap
#> # A tibble: 16,492 × 13
#> country iso2c iso3c year EN.ATM.CO2E.PC status
#> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 Afghanistan AF AFG 2021 NA NA
#> 2 Afghanistan AF AFG 2020 NA NA
#> 3 Afghanistan AF AFG 2019 0.161 NA
#> 4 Afghanistan AF AFG 2018 0.165 NA
#> 5 Afghanistan AF AFG 2017 0.134 NA
#> 6 Afghanistan AF AFG 2016 0.153 NA
#> 7 Afghanistan AF AFG 2015 0.176 NA
#> 8 Afghanistan AF AFG 2014 0.149 NA
#> 9 Afghanistan AF AFG 2013 0.190 NA
#> 10 Afghanistan AF AFG 2012 0.265 NA
#> # ℹ 16,482 more rows
#> # ℹ 7 more variables: lastupdated <date>, region <chr>,
#> # capital <chr>, longitude <dbl>, latitude <dbl>,
#> # income <chr>, lending <chr>
co2pcap %>% filter(country %in% c("World", "Japan", "United States", "China")) %>%
ggplot(aes(x = year, y = EN.ATM.CO2E.PC, color = country)) +
geom_line()
#> Warning: Removed 128 rows containing missing values
#> (`geom_line()`).
co2pcap %>% filter(!is.na(EN.ATM.CO2E.PC)) %>% pull(year) %>% summary()
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1990 1997 2005 2005 2012 2019
co2pcap %>%
filter(country %in% c("World", "Japan", "United States", "China"), year %in% 1990:2019) %>%
ggplot(aes(x = year, y = EN.ATM.CO2E.PC, color = country)) +
geom_line()
co2pcap %>%
filter(income != "Aggregates", year == 2019) %>%
ggplot(aes(x = income, y = EN.ATM.CO2E.PC, fill = income)) +
geom_boxplot()
#> Warning: Removed 26 rows containing non-finite values
#> (`stat_boxplot()`).
co2pcap %>%
filter(income != "Aggregates", year == 2019, !is.na(EN.ATM.CO2E.PC)) %>%
ggplot(aes(x = income, y = EN.ATM.CO2E.PC, fill = income)) +
geom_boxplot()
- What is
boxplot: https://vimeo.com/222358034
co2pcap %>%
filter(income != "Aggregates", year == 2019, !is.na(EN.ATM.CO2E.PC)) %>%
group_by(income) %>%
summarize(min = min(EN.ATM.CO2E.PC), med = median(EN.ATM.CO2E.PC), max = max(EN.ATM.CO2E.PC), IQR = IQR(EN.ATM.CO2E.PC), n = n())
#> # A tibble: 5 × 6
#> income min med max IQR n
#> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 High income 1.89 6.55 32.8 3.79 57
#> 2 Low income 0.0357 0.165 2.18 0.175 28
#> 3 Lower middle income 0.208 0.901 7.28 1.18 53
#> 4 Not classified 3.88 3.88 3.88 0 1
#> 5 Upper middle income 0.913 3.31 14.0 2.25 52
co2pcap %>%
filter(income != "Aggregates", year == 2019, !is.na(EN.ATM.CO2E.PC)) %>%
filter(!income %in% c("High income", "Low income", "Lower middle income", "Upper middle income"))
#> # A tibble: 1 × 13
#> country iso2c iso3c year EN.ATM.CO2E.PC status
#> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 Venezuela, RB VE VEN 2019 3.88 NA
#> # ℹ 7 more variables: lastupdated <date>, region <chr>,
#> # capital <chr>, longitude <dbl>, latitude <dbl>,
#> # income <chr>, lending <chr>
co2pcap %>%
filter(income != "Aggregates", year == 2019) %>%
filter(income == "Not classified")
#> # A tibble: 1 × 13
#> country iso2c iso3c year EN.ATM.CO2E.PC status
#> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 Venezuela, RB VE VEN 2019 3.88 NA
#> # ℹ 7 more variables: lastupdated <date>, region <chr>,
#> # capital <chr>, longitude <dbl>, latitude <dbl>,
#> # income <chr>, lending <chr>
co2pcap %>%
filter(income != "Aggregates", year == 2019) %>%
ggplot(aes(map_id = country)) +
geom_map(aes(fill = income), map = world_map) + expand_limits(x = world_map$long, y = world_map$lat) +
labs(title = "Income Levels in 2019")
co2pcap %>% distinct(country)
#> # A tibble: 266 × 1
#> country
#> <chr>
#> 1 Afghanistan
#> 2 Africa Eastern and Southern
#> 3 Africa Western and Central
#> 4 Albania
#> 5 Algeria
#> 6 American Samoa
#> 7 Andorra
#> 8 Angola
#> 9 Antigua and Barbuda
#> 10 Arab World
#> # ℹ 256 more rows
world_map %>% distinct(region)
#> region
#> 1 Aruba
#> 2 Afghanistan
#> 3 Angola
#> 4 Anguilla
#> 5 Albania
#> 6 Finland
#> 7 Andorra
#> 8 United Arab Emirates
#> 9 Argentina
#> 10 Armenia
#> 11 American Samoa
#> 12 Antarctica
#> 13 Australia
#> 14 French Southern and Antarctic Lands
#> 15 Antigua
#> 16 Barbuda
#> 17 Austria
#> 18 Azerbaijan
#> 19 Burundi
#> 20 Belgium
#> 21 Benin
#> 22 Burkina Faso
#> 23 Bangladesh
#> 24 Bulgaria
#> 25 Bahrain
#> 26 Bahamas
#> 27 Bosnia and Herzegovina
#> 28 Saint Barthelemy
#> 29 Belarus
#> 30 Belize
#> 31 Bermuda
#> 32 Bolivia
#> 33 Brazil
#> 34 Barbados
#> 35 Brunei
#> 36 Bhutan
#> 37 Botswana
#> 38 Central African Republic
#> 39 Canada
#> 40 Switzerland
#> 41 Chile
#> 42 China
#> 43 Ivory Coast
#> 44 Cameroon
#> 45 Democratic Republic of the Congo
#> 46 Republic of Congo
#> 47 Cook Islands
#> 48 Colombia
#> 49 Comoros
#> 50 Cape Verde
#> 51 Costa Rica
#> 52 Cuba
#> 53 Curacao
#> 54 Cayman Islands
#> 55 Cyprus
#> 56 Czech Republic
#> 57 Germany
#> 58 Djibouti
#> 59 Dominica
#> 60 Denmark
#> 61 Dominican Republic
#> 62 Algeria
#> 63 Ecuador
#> 64 Egypt
#> 65 Eritrea
#> 66 Canary Islands
#> 67 Spain
#> 68 Estonia
#> 69 Ethiopia
#> 70 Fiji
#> 71 Falkland Islands
#> 72 Reunion
#> 73 Mayotte
#> 74 French Guiana
#> 75 Martinique
#> 76 Guadeloupe
#> 77 France
#> 78 Faroe Islands
#> 79 Micronesia
#> 80 Gabon
#> 81 UK
#> 82 Georgia
#> 83 Guernsey
#> 84 Ghana
#> 85 Guinea
#> 86 Gambia
#> 87 Guinea-Bissau
#> 88 Equatorial Guinea
#> 89 Greece
#> 90 Grenada
#> 91 Greenland
#> 92 Guatemala
#> 93 Guam
#> 94 Guyana
#> 95 Heard Island
#> 96 Honduras
#> 97 Croatia
#> 98 Haiti
#> 99 Hungary
#> 100 Indonesia
#> 101 Isle of Man
#> 102 India
#> 103 Cocos Islands
#> 104 Christmas Island
#> 105 Chagos Archipelago
#> 106 Ireland
#> 107 Iran
#> 108 Iraq
#> 109 Iceland
#> 110 Israel
#> 111 Italy
#> 112 San Marino
#> 113 Jamaica
#> 114 Jersey
#> 115 Jordan
#> 116 Japan
#> 117 Siachen Glacier
#> 118 Kazakhstan
#> 119 Kenya
#> 120 Kyrgyzstan
#> 121 Cambodia
#> 122 Kiribati
#> 123 Nevis
#> 124 Saint Kitts
#> 125 South Korea
#> 126 Kosovo
#> 127 Kuwait
#> 128 Laos
#> 129 Lebanon
#> 130 Liberia
#> 131 Libya
#> 132 Saint Lucia
#> 133 Liechtenstein
#> 134 Sri Lanka
#> 135 Lesotho
#> 136 Lithuania
#> 137 Luxembourg
#> 138 Latvia
#> 139 Saint Martin
#> 140 Morocco
#> 141 Monaco
#> 142 Moldova
#> 143 Madagascar
#> 144 Maldives
#> 145 Mexico
#> 146 Marshall Islands
#> 147 North Macedonia
#> 148 Mali
#> 149 Malta
#> 150 Myanmar
#> 151 Montenegro
#> 152 Mongolia
#> 153 Northern Mariana Islands
#> 154 Mozambique
#> 155 Mauritania
#> 156 Montserrat
#> 157 Mauritius
#> 158 Malawi
#> 159 Malaysia
#> 160 Namibia
#> 161 New Caledonia
#> 162 Niger
#> 163 Norfolk Island
#> 164 Nigeria
#> 165 Nicaragua
#> 166 Niue
#> 167 Bonaire
#> 168 Sint Eustatius
#> 169 Saba
#> 170 Netherlands
#> 171 Norway
#> 172 Nepal
#> 173 Nauru
#> 174 New Zealand
#> 175 Oman
#> 176 Pakistan
#> 177 Panama
#> 178 Pitcairn Islands
#> 179 Peru
#> 180 Philippines
#> 181 Palau
#> 182 Papua New Guinea
#> 183 Poland
#> 184 Puerto Rico
#> 185 North Korea
#> 186 Madeira Islands
#> 187 Azores
#> 188 Portugal
#> 189 Paraguay
#> 190 Palestine
#> 191 French Polynesia
#> 192 Qatar
#> 193 Romania
#> 194 Russia
#> 195 Rwanda
#> 196 Western Sahara
#> 197 Saudi Arabia
#> 198 Sudan
#> 199 South Sudan
#> 200 Senegal
#> 201 Singapore
#> 202 South Sandwich Islands
#> 203 South Georgia
#> 204 Saint Helena
#> 205 Ascension Island
#> 206 Solomon Islands
#> 207 Sierra Leone
#> 208 El Salvador
#> 209 Somalia
#> 210 Saint Pierre and Miquelon
#> 211 Serbia
#> 212 Sao Tome and Principe
#> 213 Suriname
#> 214 Slovakia
#> 215 Slovenia
#> 216 Sweden
#> 217 Swaziland
#> 218 Sint Maarten
#> 219 Seychelles
#> 220 Syria
#> 221 Turks and Caicos Islands
#> 222 Chad
#> 223 Togo
#> 224 Thailand
#> 225 Tajikistan
#> 226 Turkmenistan
#> 227 Timor-Leste
#> 228 Tonga
#> 229 Trinidad
#> 230 Tobago
#> 231 Tunisia
#> 232 Turkey
#> 233 Taiwan
#> 234 Tanzania
#> 235 Uganda
#> 236 Ukraine
#> 237 Uruguay
#> 238 USA
#> 239 Uzbekistan
#> 240 Vatican
#> 241 Grenadines
#> 242 Saint Vincent
#> 243 Venezuela
#> 244 Virgin Islands
#> 245 Vietnam
#> 246 Vanuatu
#> 247 Wallis and Futuna
#> 248 Samoa
#> 249 Yemen
#> 250 South Africa
#> 251 Zambia
#> 252 Zimbabwe
world_map0 <- world_map %>%
mutate(region = case_when(region == "Macedonia" ~ "North Macedonia",
region == "Ivory Coast" ~ "Cote d'Ivoire",
region == "Democratic Republic of the Congo" ~ "Congo, Dem. Rep.",
region == "Republic of Congo" ~ "Congo, Rep.",
region == "UK" ~ "United Kingdom",
region == "USA" ~ "United States",
region == "Laos" ~ "Lao PDR",
region == "Slovakia" ~ "Slovak Republic",
region == "Saint Lucia" ~ "St. Lucia",
region == "Kyrgyzstan" ~ "Kyrgyz Republic",
region == "Micronesia" ~ "Micronesia, Fed. Sts.",
region == "Swaziland" ~ "Eswatini",
region == "Virgin Islands" ~ "Virgin Islands (U.S.)",
region == "Russia" ~ "Russian Federation",
region == "Egypt" ~ "Egypt, Arab Rep.",
region == "South Korea" ~ "Korea, Rep.",
region == "North Korea" ~ "Korea, Dem. People's Rep.",
region == "Iran" ~ "Iran, Islamic Rep.",
region == "Brunei" ~ "Brunei Darussalam",
region == "Venezuela" ~ "Venezuela, RB",
region == "Yemen" ~ "Yemen, Rep.",
region == "Bahamas" ~ "Bahamas, The",
region == "Syria" ~ "Syrian Arab Republic",
region == "Turkey" ~ "Turkiye",
region == "Cape Verde" ~ "Cabo Verde",
region == "Gambia" ~ "Gambia, The",
region == "Czech Republic" ~ "Czechia",
TRUE ~ region))
write_csv(world_map0, "./data/world_map0.csv")
map0_url <- "https://icu-hsuzuki.github.io/da4r2022_note/data/world_map0.csv"
world_map0 <- read_csv(map0_url)
co2pcap %>% filter(income != "Aggregates", year == 2019) %>%
anti_join(world_map0, by = c("country"="region"))
#> # A tibble: 13 × 13
#> country iso2c iso3c year EN.ATM.CO2E.PC status
#> <chr> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 Antigua and Barb… AG ATG 2019 5.64 NA
#> 2 British Virgin I… VG VGB 2019 NA NA
#> 3 Channel Islands JG CHI 2019 NA NA
#> 4 Gibraltar GI GIB 2019 NA NA
#> 5 Hong Kong SAR, C… HK HKG 2019 NA NA
#> 6 Macao SAR, China MO MAC 2019 NA NA
#> 7 Sint Maarten (Du… SX SXM 2019 NA NA
#> 8 St. Kitts and Ne… KN KNA 2019 5.24 NA
#> 9 St. Martin (Fren… MF MAF 2019 NA NA
#> 10 St. Vincent and … VC VCT 2019 2.48 NA
#> 11 Trinidad and Tob… TT TTO 2019 11.3 NA
#> 12 Tuvalu TV TUV 2019 0.913 NA
#> 13 West Bank and Ga… PS PSE 2019 NA NA
#> # ℹ 7 more variables: lastupdated <date>, region <chr>,
#> # capital <chr>, longitude <dbl>, latitude <dbl>,
#> # income <chr>, lending <chr>
world_map0 %>% anti_join(co2pcap, by = c("region"="country")) %>% distinct(region) %>% arrange(region)
#> region
#> 1 Anguilla
#> 2 Antarctica
#> 3 Antigua
#> 4 Ascension Island
#> 5 Azores
#> 6 Barbuda
#> 7 Bonaire
#> 8 Canary Islands
#> 9 Chagos Archipelago
#> 10 Christmas Island
#> 11 Cocos Islands
#> 12 Cook Islands
#> 13 Falkland Islands
#> 14 French Guiana
#> 15 French Southern and Antarctic Lands
#> 16 Grenadines
#> 17 Guadeloupe
#> 18 Guernsey
#> 19 Heard Island
#> 20 Jersey
#> 21 Madeira Islands
#> 22 Martinique
#> 23 Mayotte
#> 24 Montserrat
#> 25 Nevis
#> 26 Niue
#> 27 Norfolk Island
#> 28 Palestine
#> 29 Pitcairn Islands
#> 30 Reunion
#> 31 Saba
#> 32 Saint Barthelemy
#> 33 Saint Helena
#> 34 Saint Kitts
#> 35 Saint Martin
#> 36 Saint Pierre and Miquelon
#> 37 Saint Vincent
#> 38 Siachen Glacier
#> 39 Sint Eustatius
#> 40 Sint Maarten
#> 41 South Georgia
#> 42 South Sandwich Islands
#> 43 Taiwan
#> 44 Tobago
#> 45 Trinidad
#> 46 Vatican
#> 47 Wallis and Futuna
#> 48 Western Sahara
world_map0 %>% left_join(iso3166, by = c("region" = "ISOname")) %>%
filter(is.na(a2)) %>% distinct(region)
#> Warning in left_join(., iso3166, by = c(region = "ISOname")): Detected an unexpected many-to-many relationship between
#> `x` and `y`.
#> ℹ Row 36406 of `x` matches multiple rows in `y`.
#> ℹ Row 1 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set
#> `relationship = "many-to-many"` to silence this warning.
#> region
#> 1 Antigua
#> 2 Barbuda
#> 3 Bahamas, The
#> 4 Bolivia
#> 5 Congo, Dem. Rep.
#> 6 Congo, Rep.
#> 7 Cabo Verde
#> 8 Czechia
#> 9 Egypt, Arab Rep.
#> 10 Canary Islands
#> 11 Falkland Islands
#> 12 Micronesia, Fed. Sts.
#> 13 United Kingdom
#> 14 Gambia, The
#> 15 Heard Island
#> 16 Cocos Islands
#> 17 Chagos Archipelago
#> 18 Iran, Islamic Rep.
#> 19 Siachen Glacier
#> 20 Kyrgyz Republic
#> 21 Nevis
#> 22 Saint Kitts
#> 23 Korea, Rep.
#> 24 Lao PDR
#> 25 St. Lucia
#> 26 Saint Martin
#> 27 Moldova
#> 28 North Macedonia
#> 29 Namibia
#> 30 Bonaire
#> 31 Sint Eustatius
#> 32 Saba
#> 33 Korea, Dem. People's Rep.
#> 34 Madeira Islands
#> 35 Azores
#> 36 Palestine
#> 37 South Sandwich Islands
#> 38 South Georgia
#> 39 Saint Helena
#> 40 Ascension Island
#> 41 Slovak Republic
#> 42 Eswatini
#> 43 Sint Maarten
#> 44 Trinidad
#> 45 Tobago
#> 46 Turkiye
#> 47 Tanzania
#> 48 Vatican
#> 49 Grenadines
#> 50 Saint Vincent
#> 51 Venezuela, RB
#> 52 Virgin Islands (U.S.)
#> 53 Yemen, Rep.