How Good a Map? Putting Small Area Estimation to the Test
The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for...
| Main Authors: | , , , |
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| Language: | English |
| Published: |
World Bank, Washington, DC
2012
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2007/03/7488024/good-map-putting-small-area-estimation-test http://hdl.handle.net/10986/7040 |
| Summary: | The authors examine the performance of
small area welfare estimation. The method combines census
and survey data to produce spatially disaggregated poverty
and inequality estimates. To test the method, they compare
predicted welfare indicators for a set of target populations
with their true values. They construct target populations
using actual data from a census of households in a set of
rural Mexican communities. They examine estimates along
three criteria: accuracy of confidence intervals, bias, and
correlation with true values. The authors find that while
point estimates are very stable, the precision of the
estimates varies with alternative simulation methods. While
the original approach of numerical gradient estimation
yields standard errors that seem appropriate, some
computationally less-intensive simulation procedures yield
confidence intervals that are slightly too narrow. The
precision of estimates is shown to diminish markedly if
unobserved location effects at the village level are not
well captured in underlying consumption models. With well
specified models there is only slight evidence of bias, but
the authors show that bias increases if underlying models
fail to capture latent location effects. Correlations
between estimated and true welfare at the local level are
highest for mean expenditure and poverty measures and lower
for inequality measures. |
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