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 of in-sample prediction errors; however, the objectiv...

Full description

Bibliographic Details
Main Authors:	McBride, Linden, Nichols, Austin
Format:	Journal Article
Published:	Published by Oxford University Press on behalf of the World Bank 2020
Subjects:	TARGETING PROXY MEANS TEST POVERTY POVERTY ASSESSMENT
Online Access:	http://hdl.handle.net/10986/33525

id	okr-10986-33525
recordtype	oai_dc
spelling	okr-10986-335252021-05-25T10:54:42Z Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning McBride, Linden Nichols, Austin TARGETING PROXY MEANS TEST POVERTY POVERTY ASSESSMENT 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. 2020-04-03T14:53:44Z 2020-04-03T14:53:44Z 2018-10 Journal Article World Bank Economic Review 1564-698X http://hdl.handle.net/10986/33525 CC BY-NC-ND 3.0 IGO http://creativecommons.org/licenses/by-nc-nd/3.0/igo World Bank Published by Oxford University Press on behalf of the World Bank Publications & Research :: Journal Article Publications & Research
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
topic	TARGETING PROXY MEANS TEST POVERTY POVERTY ASSESSMENT
spellingShingle	TARGETING PROXY MEANS TEST POVERTY POVERTY ASSESSMENT McBride, Linden Nichols, Austin Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
description	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.
format	Journal Article
author	McBride, Linden Nichols, Austin
author_facet	McBride, Linden Nichols, Austin
author_sort	McBride, Linden
title	Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
title_short	Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
title_full	Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
title_fullStr	Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
title_full_unstemmed	Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
title_sort	retooling poverty targeting using out-of-sample validation and machine learning
publisher	Published by Oxford University Press on behalf of the World Bank
publishDate	2020
url	http://hdl.handle.net/10986/33525
_version_	1764478954924670976

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

Similar Items