Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with...
| Main Authors: | , , , , |
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| Format: | Working Paper |
| Language: | English English |
| Published: |
World Bank, Washington, DC
2022
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/099430309142231728/IDU0660868530404c0414e0bf180797b525682a5 http://hdl.handle.net/10986/38020 |
| Summary: | Estimates of poverty are an important
input into policy formulation in developing countries. The
accurate measurement of poverty rates is therefore a
first-order problem for development policy. This paper shows
that combining satellite imagery with household surveys can
improve the precision and accuracy of estimated poverty
rates in Mexican municipalities, a level at which the survey
is not considered representative. It also shows that a
household-level model outperforms other common small area
estimation methods. However, poverty estimates in 2015
derived from geospatial data remain less accurate than 2010
estimates derived from household census data. These results
indicate that the incorporation of household survey data and
widely available satellite imagery can improve on existing
poverty estimates in developing countries when census data
are old or when patterns of poverty are changing rapidly,
even for small subgroups. |
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