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

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Listed:
  • Newhouse,David Locke
  • Merfeld,Joshua David
  • Ramakrishnan,Anusha Pudugramam
  • Swartz,Tom
  • Lahiri,Partha

Abstract

Estimates of poverty are an important input into policy formulation in developing countries. Theaccurate measurement of poverty rates is therefore a first-order problem for development policy. This paper showsthat combining satellite imagery with household surveys can improve the precision and accuracy of estimated povertyrates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that ahousehold-level model outperforms other common small area estimation methods. However, poverty estimates in 2015derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These resultsindicate that the incorporation of household survey data and widely available satellite imagery can improve on existingpoverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly,even for small subgroups.

Suggested Citation

  • Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    • Handle: RePEc:wbk:wbrwps:10175
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    References listed on IDEAS

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