Scalable Targeting of Social Protection: When Do Algorithms Out-Perform Surveys and Community Knowledge?

Emily Aiken, Anik Ashraf, Joshua Blumenstock, Raymond Guiteras and Ahmed Mushfiq Mobarak
Additional contact information
Emily Aiken: UC San Diego
Anik Ashraf: Ludwig-Maximilians-Universitat Munchen
Joshua Blumenstock: UC Berkeley
Ahmed Mushfiq Mobarak: Yale University

No 2443, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers Ñ proxy means-testing, nominations from community members, and an algorithmic approach using machine learning to predict poverty using mobile phone usage behavior Ñ and study how their cost-effectiveness varies with the scale and scope of the program. We collect mobile phone records from all major telecom operators in Bangladesh and conduct community-based wealth rankings and detailed consumption surveys of 5,000 households, to select the 22,000 poorest households for $300 transfers from 106,000 listed households. While proxy-means testing is most accurate, algorithmic targeting becomes more cost-effective for national-scale programs where large numbers of households have to be screened. We explore the external validity of these insights using survey data and mobile phone records data from Togo, and cross-country information on benefit transfer programs from the World Bank.

Pages: 88 pages
Date: 2025-06-08
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Working Paper: Scalable Targeting of Social Protection: When Do Algorithms Out-Perform Surveys and Community Knowledge? (2025)
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