World Development Dataset
wlddev.RdThis dataset contains 5 indicators from the World Bank's World Development Indicators (WDI) database: (1) GDP per capita, (2) Life expectancy at birth, (3) GINI index, (4) Net ODA and official aid received and (5) Population. The panel data is balanced and covers 216 present and historic countries from 1960-2020 (World Bank aggregates and regional entities are excluded).
Apart from the indicators the data contains a number of identifiers (character country name, factor ISO3 country code, World Bank region and income level, numeric year and decade) and 2 generated variables: A logical variable indicating whether the country is an OECD member, and a fictitious variable stating the date the data was recorded. These variables were added so that all common data-types are represented in this dataset, making it an ideal test-dataset for certain collapse functions.
Usage
data("wlddev")Format
A data frame with 13176 observations on the following 13 variables. All variables are labeled e.g. have a 'label' attribute.
countrychr Country Name
iso3cfct Country Code
datedate Date Recorded (Fictitious)
yearint Year
decadeint Decade
regionfct World Bank Region
incomefct World Bank Income Level
OECDlog Is OECD Member Country?
PCGDPnum GDP per capita (constant 2010 US$)
LIFEEXnum Life expectancy at birth, total (years)
GINInum GINI index (World Bank estimate)
ODAnum Net official development assistance and official aid received (constant 2018 US$)
POPnum Population, total
Source
https://data.worldbank.org/, accessed via the WDI package. The codes for the series are c("NY.GDP.PCAP.KD", "SP.DYN.LE00.IN", "SI.POV.GINI", "DT.ODA.ALLD.KD", "SP.POP.TOTL").
Examples
data(wlddev)
# Panel-summarizing the 5 series
qsu(wlddev, pid = ~iso3c, cols = 9:13, vlabels = TRUE)
#> , , PCGDP: GDP per capita (constant 2010 US$)
#>
#> N/T Mean SD Min Max
#> Overall 9470 12048.778 19077.6416 132.0776 196061.417
#> Between 206 12962.6054 20189.9007 253.1886 141200.38
#> Within 45.9709 12048.778 6723.6808 -33504.8721 76767.5254
#>
#> , , LIFEEX: Life expectancy at birth, total (years)
#>
#> N/T Mean SD Min Max
#> Overall 11670 64.2963 11.4764 18.907 85.4171
#> Between 207 64.9537 9.8936 40.9663 85.4171
#> Within 56.3768 64.2963 6.0842 32.9068 84.4198
#>
#> , , GINI: Gini index (World Bank estimate)
#>
#> N/T Mean SD Min Max
#> Overall 1744 38.5341 9.2006 20.7 65.8
#> Between 167 39.4233 8.1356 24.8667 61.7143
#> Within 10.4431 38.5341 2.9277 25.3917 55.3591
#>
#> , , ODA: Net official development assistance and official aid received (constant 2018 US$)
#>
#> N/T Mean SD Min Max
#> Overall 8608 454'720131 868'712654 -997'679993 2.56715605e+10
#> Between 178 439'168412 569'049959 468717.916 3.62337432e+09
#> Within 48.3596 454'720131 650'709624 -2.44379420e+09 2.45610972e+10
#>
#> , , POP: Population, total
#>
#> N/T Mean SD Min Max
#> Overall 12919 24'245971.6 102'120674 2833 1.39771500e+09
#> Between 216 24'178573 98'616506.7 8343.3333 1.08786967e+09
#>
# By Region
qsu(wlddev, by = ~region, cols = 9:13, vlabels = TRUE)
#> , , PCGDP: GDP per capita (constant 2010 US$)
#>
#> N Mean SD Min
#> East Asia & Pacific 1467 10513.2441 14383.5507 132.0776
#> Europe & Central Asia 2243 25992.9618 26435.1316 366.9354
#> Latin America & Caribbean 1976 7628.4477 8818.5055 1005.4085
#> Middle East & North Africa 842 13878.4213 18419.7912 578.5996
#> North America 180 48699.76 24196.2855 16405.9053
#> South Asia 382 1235.9256 1611.2232 265.9625
#> Sub-Saharan Africa 2380 1840.0259 2596.0104 164.3366
#> Max
#> East Asia & Pacific 71992.1517
#> Europe & Central Asia 196061.417
#> Latin America & Caribbean 88391.3331
#> Middle East & North Africa 116232.753
#> North America 113236.091
#> South Asia 8476.564
#> Sub-Saharan Africa 20532.9523
#>
#> , , LIFEEX: Life expectancy at birth, total (years)
#>
#> N Mean SD Min Max
#> East Asia & Pacific 1807 65.9445 10.1633 18.907 85.078
#> Europe & Central Asia 3046 72.1625 5.7602 45.369 85.4171
#> Latin America & Caribbean 2107 68.3486 7.3768 41.762 82.1902
#> Middle East & North Africa 1226 66.2508 9.8306 29.919 82.8049
#> North America 144 76.2867 3.5734 68.8978 82.0488
#> South Asia 480 57.5585 11.3004 32.446 78.921
#> Sub-Saharan Africa 2860 51.581 8.6876 26.172 74.5146
#>
#> [ reached getOption("max.print") -- omitted 3 matrix slice(s) ]
# Panel-summary by region
qsu(wlddev, by = ~region, pid = ~iso3c, cols = 9:13, vlabels = TRUE)
#> , , Overall, PCGDP: GDP per capita (constant 2010 US$)
#>
#> N/T Mean SD Min
#> East Asia & Pacific 1467 10513.2441 14383.5507 132.0776
#> Europe & Central Asia 2243 25992.9618 26435.1316 366.9354
#> Latin America & Caribbean 1976 7628.4477 8818.5055 1005.4085
#> Middle East & North Africa 842 13878.4213 18419.7912 578.5996
#> North America 180 48699.76 24196.2855 16405.9053
#> South Asia 382 1235.9256 1611.2232 265.9625
#> Sub-Saharan Africa 2380 1840.0259 2596.0104 164.3366
#> Max
#> East Asia & Pacific 71992.1517
#> Europe & Central Asia 196061.417
#> Latin America & Caribbean 88391.3331
#> Middle East & North Africa 116232.753
#> North America 113236.091
#> South Asia 8476.564
#> Sub-Saharan Africa 20532.9523
#>
#> , , Between, PCGDP: GDP per capita (constant 2010 US$)
#>
#> N/T Mean SD Min Max
#> East Asia & Pacific 34 10513.2441 12771.742 444.2899 39722.0077
#> Europe & Central Asia 56 25992.9618 24051.035 809.4753 141200.38
#> Latin America & Caribbean 38 7628.4477 8470.9708 1357.3326 77403.7443
#> Middle East & North Africa 20 13878.4213 17251.6962 1069.6596 64878.4021
#> North America 3 48699.76 18604.4369 35260.4708 74934.5874
#> South Asia 8 1235.9256 1488.3669 413.68 6621.5002
#> Sub-Saharan Africa 47 1840.0259 2234.3254 253.1886 9922.0052
#>
#> [ reached getOption("max.print") -- omitted 13 matrix slice(s) ]
# Pairwise correlations: Ovarall
print(pwcor(get_vars(wlddev, 9:13), N = TRUE, P = TRUE), show = "lower.tri")
#> PCGDP LIFEEX GINI ODA POP
#> PCGDP 1 (9470)
#> LIFEEX .57* (9022) 1 (11670)
#> GINI -.44* (1735) -.35* (1742) 1 (1744)
#> ODA -.16* (7128) -.02 (8142) -.20* (1109) 1 (8608)
#> POP -.06* (9470) .03* (11659) .04 (1744) .31* (8597) 1 (12919)
# Pairwise correlations: Between Countries
print(pwcor(fmean(get_vars(wlddev, 9:13), wlddev$iso3c), N = TRUE, P = TRUE), show = "lower.tri")
#> PCGDP LIFEEX GINI ODA POP
#> PCGDP 1 (206)
#> LIFEEX .60* (199) 1 (207)
#> GINI -.42* (165) -.40* (165) 1 (167)
#> ODA -.25* (172) -.21* (172) -.19* (145) 1 (178)
#> POP -.07 (206) -.02 (207) -.04 (167) .50* (178) 1 (216)
# Pairwise correlations: Within Countries
print(pwcor(fwithin(get_vars(wlddev, 9:13), wlddev$iso3c), N = TRUE, P = TRUE), show = "lower.tri")
#> PCGDP LIFEEX GINI ODA POP
#> PCGDP 1 (9470)
#> LIFEEX .31* (9022) 1 (11670)
#> GINI -.01 (1735) -.16* (1742) 1 (1744)
#> ODA -.01 (7128) .17* (8142) -.08* (1109) 1 (8608)
#> POP .06* (9470) .29* (11659) .01 (1744) -.11* (8597) 1 (12919)