Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning

43 Pages Posted: 6 Oct 2016

See all articles by Linden McBride

Linden McBride

A member of the CGIAR Consortium - International Food Policy Research Institute (IFPRI)

Austin Nichols

The Urban Institute; Abt Associates

Date Written: October 4, 2016

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. 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.

Keywords: Small Area Estimation Poverty Mapping, Poverty Impact Evaluation, Poverty Monitoring & Analysis, Poverty Assessment, Poverty Lines, Poverty Diagnostics

Suggested Citation

McBride, Linden and Nichols, Austin and Nichols, Austin, Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning (October 4, 2016). World Bank Policy Research Working Paper No. 7849, Available at SSRN: https://ssrn.com/abstract=2848477

Linden McBride (Contact Author)

A member of the CGIAR Consortium - International Food Policy Research Institute (IFPRI) ( email )

1201 Eye St, NW,
Washington, DC 20005
United States

Austin Nichols

The Urban Institute

Abt Associates ( email )

MD 20814
United States

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