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Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning

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  • David Newhouse
  • Anusha Ramakrishnan
  • Tom Swartz
  • Josh Merfeld
  • Partha Lahiri

Abstract

Estimates of poverty are an important input into policy formulation in developing countries, making the accurate measurement of poverty rates a first‐order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the accuracy and precision of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that empirical best prediction (EBP) based on a twofold household‐level model outperforms EBPs based on other common small area estimation models. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve poverty estimates in developing countries, even for small subgroups.

Suggested Citation

  • David Newhouse & Anusha Ramakrishnan & Tom Swartz & Josh Merfeld & Partha Lahiri, 2025. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 87(6), pages 1158-1172, December.
    • Handle: RePEc:bla:obuest:v:87:y:2025:i:6:p:1158-1172
    DOI: 10.1111/obes.12678

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    Cited by:

    1. is not listed on IDEAS
    2. Corral, Paul & Henderson, Heath & Segovia, Sandra, 2025. "Poverty mapping in the age of machine learning," Journal of Development Economics, Elsevier, vol. 172(C).
    3. Newhouse,David Locke, 2023. "Small Area Estimation of Poverty and Wealth Using Geospatial Data : What Have We Learned SoFar ?," Policy Research Working Paper Series 10512, The World Bank.
    4. 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).

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