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

Author

Listed:
  • Emily Aiken
  • Guadalupe Bedoya
  • Joshua Blumenstock
  • Aidan Coville

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.

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  • Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022. "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers 2206.11400, arXiv.org.
    • Handle: RePEc:arx:papers:2206.11400
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    References listed on IDEAS

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