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Geographic microtargeting of social assistance with high-resolution poverty maps

Author

Listed:
  • Isabella S. Smythe

    (a School of International and Public Affairs, Columbia University, New York, NY 10027;)

  • Joshua E. Blumenstock

    (b School of Information, University of California, Berkeley, CA 94720)

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.

Suggested Citation

  • Isabella S. Smythe & Joshua E. Blumenstock, 2022. "Geographic microtargeting of social assistance with high-resolution poverty maps," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(32), pages 2120025119-, August.
    • Handle: RePEc:nas:journl:v:119:y:2022:p:e2120025119
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. van der Weide, Roy & Blankespoor, Brian & Elbers, Chris & Lanjouw, Peter, 2024. "How accurate is a poverty map based on remote sensing data? An application to Malawi," Journal of Development Economics, Elsevier, vol. 171(C).
    2. Tim Ohlenburg & Emil Tesliuc & Yuko Okamura, 2024. "Scaling up Social Assistance Where Data is Scarce : Opportunities and Limits of Novel Data and AI," Social Protection Discussion Papers and Notes 189993, The World Bank.
    3. Corral, Paul & Henderson, Heath & Segovia, Sandra, 2025. "Poverty mapping in the age of machine learning," Journal of Development Economics, Elsevier, vol. 172(C).
    4. Yuko Okamura & Tim Ohlenburg & Emil Tesliuc, 2024. "Scaling Up Social Assistance Where Data is Scarce - Opportunities and Limits of Novel Data and AI," World Bank Publications - Reports 41553, The World Bank Group.
    5. Beuermann, Diether W. & Hoffmann, Bridget & Stampini, Marco & Vargas, David L. & Vera-Cossio, Diego, 2025. "Shooting a moving target: Evaluating targeting tools for social programs when income fluctuates," Journal of Development Economics, Elsevier, vol. 172(C).
    6. Sinha Roy, Sutirtha & van der Weide, Roy, 2025. "Estimating poverty for India after 2011 using private-sector survey data," Journal of Development Economics, Elsevier, vol. 172(C).
    7. Yuko Okamura & Tim Ohlenburg & Emil Tesliuc, 2024. "Scaling up Social Assistance Where Data is Scarce," World Bank Publications - Reports 41548, The World Bank Group.

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