High-resolution poverty maps in Sub-Saharan Africa

Kamwoo Lee and Jeanine Braithwaite

World Development, 2022, vol. 159, issue C

Abstract: Up-to-date poverty maps are an important tool for policymakers, but have been prohibitively expensive to produce and maintain ongoing accuracy. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the method for 25 Sub-Saharan African countries and validated against survey data. Our method can increase the validity of both single country and cross-country estimations, leading to more accurate poverty maps with a higher geographic precision for Sub-Saharan African countries. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Keywords: Poverty estimation; Small-area estimation; Cross-country estimation; Machine learning; Satellite imagery; OpenStreetMap; Sub-Saharan Africa (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:wdevel:v:159:y:2022:i:c:s0305750x22002182

DOI: 10.1016/j.worlddev.2022.106028

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