Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan

Emily L. Aiken, Guadalupe Bedoya, Joshua E. Blumenstock and Aidan Coville

Journal of Development Economics, 2023, vol. 161, issue C

Abstract: Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source.

Keywords: Targeting; Machine learning; Mobile phone data; Afghanistan (search for similar items in EconPapers)
JEL-codes: C55 I32 I38 O12 O38 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:161:y:2023:i:c:s0304387822001584

DOI: 10.1016/j.jdeveco.2022.103016

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