Small Area Estimation of Non-Monetary Poverty with Geospatial Data
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 g...
| Main Authors: | , , , , |
|---|---|
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/831041599576611927/Small-Area-Estimation-of-Non-Monetary-Poverty-with-Geospatial-Data http://hdl.handle.net/10986/34469 |
| Summary: | 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. |
|---|