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Small Area Estimation of Non-Monetary Poverty with Geospatial Data

Takaaki Masaki, David Newhouse, Ani Silwal (), Adane Bedada and Ryan Engstrom

No 9383, Policy Research Working Paper Series from The World Bank

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.

Keywords: Inequality; Employment and Unemployment; ICT Applications; Labor&Employment Law; Educational Sciences (search for similar items in EconPapers)
Date: 2020-09-08
New Economics Papers: this item is included in nep-dev and nep-ict
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