 Retooling Poverty Targeting Using Out-of-Sample Validation and Machine LearningLinden McBride and Austin Nichols The World Bank Economic Review, 2018, vol. 32, issue 3, 531-550 Abstract: 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. We present 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. We take the United States Agency for International Development (USAID) poverty assessment tool and base data 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. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:wbecrv:v:32:y:2018:i:3:p:531-550. Ordering information: This journal article can be ordered from Access Statistics for this article The World Bank Economic Review is currently edited by Eric Edmonds and Nina Pavcnik More articles in The World Bank Economic Review from World Bank Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC. |
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