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Small Area Estimation of Non-Monetary Poverty with Geospatial Data

Author

Listed:
  • Masaki,Takaaki
  • Newhouse,David Locke
  • Silwal,Ani Rudra
  • Bedada,Adane
  • Engstrom,Ryan

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.

Suggested Citation

  • Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9383
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    References listed on IDEAS

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    Cited by:

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    3. 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.
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    Inequality; Employment and Unemployment; ICT Applications; Labor&Employment Law; Educational Sciences;
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