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Small Area Estimation of Non-Monetary Poverty with Geospatial Data

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  • Takaaki Masaki
  • David Newhouse
  • Ani Rudra Silwal
  • Adane Bedada
  • Ryan Engstrom

Abstract

This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost.
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Suggested Citation

  • Takaaki Masaki & David Newhouse & Ani Rudra Silwal & Adane Bedada & Ryan Engstrom, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," World Bank Publications - Reports 34469, The World Bank Group.
    • Handle: RePEc:wbk:wboper:34469
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    References listed on IDEAS

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    2. George Joseph & Sveta Milusheva & Sturrock,Hugh James William & Mapako,Tonderai & Ayling,Sophie Charlotte Emi & Hoo,Yi Rong, 2023. "Estimating spatially disaggregated probability of severe COVID-19 and the impactof handwashing interventions : The case of Zimbabwe," Policy Research Working Paper Series 10328, The World Bank.
    3. Corral Rodas,Paul Andres & Kastelic,Kristen Himelein & Mcgee,Kevin Robert & Molina,Isabel, 2021. "A Map of the Poor or a Poor Map ?," Policy Research Working Paper Series 9620, The World Bank.
    4. Corral Rodas,Paul Andres & Henderson,Heath Linn & Segovia Juarez,Sandra Carolina, 2023. "Poverty Mapping in the Age of Machine Learning," Policy Research Working Paper Series 10429, The World Bank.
    5. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    6. Paul Corral & Kristen Himelein & Kevin McGee & Isabel Molina, 2021. "A Map of the Poor or a Poor Map?," Mathematics, MDPI, vol. 9(21), pages 1-40, November.

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