Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization o...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2016
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2016/10/26839841/retooling-poverty-targeting-using-out-of-sample-validation-machine-learning http://hdl.handle.net/10986/25166 |
| Summary: | Proxy means test (PMT) poverty targeting
tools have become common tools for beneficiary targeting and
poverty assessment where full means tests are costly.
Currently popular estimation procedures for generating these
tools prioritize minimization of in-sample prediction
errors; however, the objective in generating such tools is
out-of-sample prediction. This paper presents evidence that
prioritizing minimal out-of-sample error, identified through
cross-validation and stochastic ensemble methods, in PMT
tool development can substantially improve the out-of-sample
performance of these targeting tools. The USAID poverty
assessment tool and base data are used for demonstration of
these methods; however, the methods applied in this paper
should be considered for PMT and other poverty-targeting
tool development more broadly. |
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