Publication: Small Area Estimation of Non-Monetary Poverty with Geospatial Data

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Date
2020-09
ISSN
Published
2020-09
Author(s)
Masaki, Takaaki
Newhouse, David
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.
Citation
Masaki, Takaaki; Newhouse, David; Silwal, Ani Rudra; Bedada, Adane; Engstrom, Ryan. 2020. Small Area Estimation of Non-Monetary Poverty with Geospatial Data; Small Area Estimation of Non-Monetary Poverty with Geospatial Data. Policy Research Working Paper;No. 9383. © World Bank, Washington, DC. http://openknowledge.worldbank.org/handle/10986/34469 License: CC BY 3.0 IGO.
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