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Small Area Estimation of Poverty and Wealth Using Geospatial Data : What Have We Learned SoFar ?

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  • Newhouse,David Locke

Abstract

This paper offers a nontechnical review of selected applications that combine survey andgeospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites andphones predicts poverty and wealth accurately across space, when evaluated against census data, and their use inmodel-based estimates improve the accuracy and efficiency of direct survey estimates. Although the evidence is scant,models based on interpretable features appear to predict at least as well as estimates derived from Convolutional NeuralNetworks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informativesampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible.Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models. Small areaestimates using geospatial data can improve the design of social assistance programs, particularly when the existingtargeting system is poorly designed.

Suggested Citation

  • 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.
    • Handle: RePEc:wbk:wbrwps:10512
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