Global poverty estimation using private and public sector big data sources

Contents

1. Abstract
2. Citation
3. Links

Abstract

Household surveys give a precise estimate of poverty, however, surveys are costly and are fielded infrequently. We demonstrate the importance of jointly using multiple public and private sector data sources to estimate levels and changes in wealth for a large set of countries. We train models using 63,854 survey cluster locations across 59 countries, relying on data from satellites, Facebook Marketing information, and OpenStreetMaps. The model generalizes previous approaches to a wide set of countries. On average across countries the model explains 55% (min = 14%; max = 85%) of the variation in levels of wealth at the survey cluster level and 59% (min = 0%; max = 93%) of the variation at the district level and the model explains 4% (min = 0%; max = 17%) and 6% (min = 0%; max = 26%) of the variation of changes in wealth at the cluster and district levels. Models perform best in lower income countries and in countries with higher variance in wealth. Features from night time lights, OpenStreetMaps, and land cover data are most important in explaining levels of wealth, and features from night time lights are most important in explaining changes in wealth.

This is an output of the ieConnect for Impact Programme.

Citation

Marty R and Duhaut A. 'Global poverty estimation using private and public sector big data sources'. Scientific Reports 2024: volume 14 issue 1, article number 3160

Links

Global poverty estimation using private and public sector big data sources

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