The PovcalNet website and API have now been retired, and replaced by the Poverty and Inequality Platform (PIP). As a result, the PovcanetR API client is now longer relevant. Please use the pipR package instead to interact with the PIP API. Thanks!
The povcalnetR package allows R users to compute poverty and
inequality indicators for more than 160 countries and regions from the
World Bank’s database of household surveys. It has the same
functionality as the PovcalNet
website. PovcalNet
is a computational tool that allows users to estimate poverty rates for
regions, sets of countries or individual countries, over time and at any
poverty line.
PovcalNet is managed jointly by the Data and Research Group in the World Bank’s Development Economics Division. It draws heavily upon a strong collaboration with the Poverty and Equity Global Practice, which is responsible for the gathering and harmonization of the underlying survey data.
PovcalNet reports the following measures at the chosen poverty line:
- Headcount ratio
- Poverty Gap
- Squared Poverty Gap
- Watts index
It also reports these inequality measures:
- Gini index
- mean log deviation
- decile shares
The underlying welfare aggregate is per capita household income or consumption expressed in 2011 PPP-adjusted USD. Poverty lines are expressed in daily amounts, while means and medians are monthly.
For more information on the definition of the indicators, click
here
For more information on the methodology, click
here
You can install the released version of povcalnetR from
CRAN with:
install.packages("povcalnetR")The development version can be installed from GitHub with:
install.packages(c("devtools", "httr"))
devtools::install_github("worldbank/povcalnetR")This is a basic example that shows how to retrieve some key poverty statistics from PovcalNet using this package
library(povcalnetR)
library(dplyr)
df <- povcalnet(country = "ALB")
glimpse(df)
#> Observations: 5
#> Variables: 31
#> $ countrycode <chr> "ALB", "ALB", "ALB", "ALB", "ALB"
#> $ countryname <chr> "Albania", "Albania", "Albania", "Albania", "Al...
#> $ regioncode <chr> "ECA", "ECA", "ECA", "ECA", "ECA"
#> $ coveragetype <chr> "N", "N", "N", "N", "N"
#> $ year <dbl> 1996, 2002, 2005, 2008, 2012
#> $ datayear <dbl> 1996, 2002, 2005, 2008, 2012
#> $ datatype <chr> "consumption", "consumption", "consumption", "c...
#> $ isinterpolated <dbl> 0, 0, 0, 0, 0
#> $ usemicrodata <dbl> 1, 1, 1, 1, 1
#> $ ppp <dbl> 58.16801, 58.16801, 58.16801, 58.16801, 58.16801
#> $ povertyline <dbl> 1.9, 1.9, 1.9, 1.9, 1.9
#> $ mean <dbl> 187.8427, 191.9880, 217.0335, 237.5353, 225.2692
#> $ headcount <dbl> 0.011291240, 0.020473200, 0.011237280, 0.003705...
#> $ povertygap <dbl> 0.0019115400, 0.0035450460, 0.0018274740, 0.000...
#> $ povertygapsq <dbl> 0.0005560317, 0.0010593800, 0.0004780857, 0.000...
#> $ watts <dbl> 0.0023108880, 0.0043677770, 0.0021404260, 0.000...
#> $ gini <dbl> 0.2701034, 0.3173898, 0.3059566, 0.2998467, 0.2...
#> $ median <dbl> 165.0867, 158.3630, 184.6848, 198.7757, 195.0467
#> $ mld <dbl> 0.1191043, 0.1648116, 0.1544128, 0.1488934, 0.1...
#> $ polarization <dbl> NA, NA, NA, NA, NA
#> $ population <dbl> 3.168033, 3.051010, 3.011487, 2.947314, 2.900401
#> $ decile1 <dbl> 0.03863, 0.03494, 0.03483, 0.03734, 0.03660
#> $ decile2 <dbl> 0.05289, 0.04859, 0.04920, 0.05137, 0.05193
#> $ decile3 <dbl> 0.06379, 0.05842, 0.05977, 0.06088, 0.06144
#> $ decile4 <dbl> 0.07322, 0.06738, 0.06921, 0.06984, 0.07031
#> $ decile5 <dbl> 0.08380, 0.07653, 0.07988, 0.07912, 0.08084
#> $ decile6 <dbl> 0.09355, 0.08839, 0.09037, 0.08924, 0.09257
#> $ decile7 <dbl> 0.1082, 0.1023, 0.1037, 0.1030, 0.1052
#> $ decile8 <dbl> 0.1247, 0.1198, 0.1213, 0.1193, 0.1229
#> $ decile9 <dbl> 0.1490, 0.1493, 0.1483, 0.1454, 0.1489
#> $ decile10 <dbl> 0.2122, 0.2544, 0.2434, 0.2446, 0.2293