High-Resolution Poverty Maps in Sub-Saharan Africa

Kamwoo Lee and Jeanine Braithwaite

Papers from arXiv.org

Abstract: Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. 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.

Date: 2020-09, Revised 2021-05
New Economics Papers: this item is included in nep-afr, nep-big, nep-cmp and nep-dev
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Published in World Development 159 (2022): 106028

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.00544

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