# Poverty Probability Index

Lookup Tables to Generate Poverty Likelihoods and Rates using the Poverty Probability Index

Image credit: Ernest Guevarra

We just launched the eighth release (version 0.5.3) of ppitables, our R package containing Poverty Probability Index (PPI®) lookup tables for the 61 countries where PPI® can be calculated. The PPI® is a poverty measurement tool for organisations and businesses with a mission to serve the poor created by Innovations for Poverty Action (IPA).

Initially released in March of 2018, ppitables has now been downloaded by more than 12,000 R users averaging more than 500 downloads per month.

We developed ppitables to support our use of the PPI® for surveys and assessments we have conducted. Our main use case for PPI® is as an alternative means to classify wealth in our survey sample as opposed to the more widely used and traditional household asset listing and application of principal components analysis (PCA) for household wealth ranking used by the World Bank and by the Demographic and Health Surveys (DHS). Using the answers to just 10 questions about a household’s characteristics and asset ownership, a score is calculated and the likelihood of a household living below poverty line is computed. To learn more about the PPI®, its history and development, go here. To read more about how to use PPI®, click here.

The ppitables package is aimed at R users whose work and/or research includes the use of the PPI®. The package facilitates the conversion of country-specific household PPI® score into a poverty likelihood value for a household. Users can immediately write appropriate scripts to convert data they may have of a sample of households in a particular country into the respective poverty probabilities using the country-specific lookup tables.

In this eighth iteration of the package, we have added the most recently released lookup tables for Malawi that uses IPA’s current approach to calculating poverty probabilities. For more information about ppitables and how to use it, visit the package website. To view the package source code, see the package’s GitHub repository.

If you have used ppitables before or have used it recently, we’d love to hear from you for feedback and comments. If you find a bug or error or would like to request additional feature/s, file an issue here.

##### Ernest Guevarra
###### Founding Member

I am a public health specialist with a particular interest in health and nutrition metrics and analytics, and in spatial epidemiology. I develop fit-for-purpose R packages as part of my work with data.