Geographic microtargeting of social assistance with high-resolution poverty maps

Isabella S. Smythe and Joshua Blumenstock
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Isabella S. Smythe: a School of International and Public Affairs, Columbia University, New York, NY 10027;

Proceedings of the National Academy of Sciences, 2022, vol. 119, issue 32, e2120025119

Abstract: Many antipoverty programs use geographic targeting to prioritize benefits to people living in specific locations. This paper shows that high-resolution poverty maps, constructed with machine learning algorithms from satellite imagery, can improve the geographic targeting of benefits to the poorest members of society. This approach was used by the Nigerian government to distribute benefits to millions of the extreme poor. As high-resolution poverty maps become globally available, these results can inform the design and implementation of social assistance programs worldwide.

Keywords: poverty; targeting; satellite imagery; Nigeria (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:nas:journl:v:119:y:2022:p:e2120025119

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