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 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.

Bibliographic Details

Main Authors:	Aiken, Emily L., Bedoya, Guadalupe, Blumenstock, Joshua E., Coville, Aidan
Format:	Working Paper biblioteca
Language:	English English
Published:	World Bank, Washington, DC 2022-12
Subjects:	MACHINE LEARNING, TARGETING, CASH TRANSFERS, RECIPIENTS, TARGETING ULTRA-POOR HOUSEHOLD DATA, MOBILE PHONE DATA,
Online Access:	http://documents.worldbank.org/curated/en/099329412062214006/IDU0b56d850209a2e040610a5d8019aa2250f2d4 http://hdl.handle.net/10986/38491
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