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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

Installation

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")

Example

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