Publication: Small Area Estimation of Non-Monetary Poverty with Geospatial Data
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Date
2020-09
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Published
2020-09
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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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“Masaki, Takaaki; Newhouse, David; Silwal, Ani Rudra; Bedada, Adane; Engstrom, Ryan. 2020. Small Area Estimation of Non-Monetary Poverty with Geospatial Data. Policy Research Working Paper;No. 9383. © World Bank. http://hdl.handle.net/10986/34469 License: CC BY 3.0 IGO.”
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