Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan

Can mobile phone data improve program targeting? By combining rich survey data from the baseline of a "big push" anti-poverty program in Afghanistan implemented in 2016 with detailed mobile phone logs from program beneficiaries, this paper studies the extent to which machine learning metho...

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Bibliographic Details
Published in:Policy File 2022
Main Authors: Aiken, Emily L, Bedoya Arguelles, Guadalupe, Blumenstock, Joshua Evan, Coville, Aidan
Format: Report
Language:English
Subjects:
Intelligence
Poverty
The World Bank
Online Access:Request full text
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Summary:Can mobile phone data improve program targeting? By combining rich survey data from the baseline of a "big push" anti-poverty program in Afghanistan implemented in 2016 with detailed mobile phone logs from program beneficiaries, this paper studies the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. The paper shows 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.