Retooling poverty targeting using out-of-sample validation and machine learning

Linden McBride and Austin Nichols

No 7849, Policy Research Working Paper Series from The World Bank

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 (search for similar items in EconPapers)
Date: 2016-10-04
References: Add references at CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://documents.worldbank.org/curated/en/352211475589592980/pdf/WPS7849.pdf (application/pdf)

Related works:
Journal Article: Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wbk:wbrwps:7849

Access Statistics for this paper

More papers in Policy Research Working Paper Series from The World Bank 1818 H Street, N.W., Washington, DC 20433. Contact information at EDIRC.
Bibliographic data for series maintained by Roula I. Yazigi ().