Aiken, Emily L.Bedoya, GuadalupeBlumenstock, Joshua E.Coville, Aidan2023-01-182023-01-182022-12https://hdl.handle.net/10986/38491Can 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.enCC BY 3.0 IGOMACHINE LEARNINGTARGETINGCASH TRANSFERSRECIPIENTSTARGETING ULTRA-POOR HOUSEHOLD DATAMOBILE PHONE DATAWorking PaperWorld Bank10.1596/1813-9450-10252