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

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Emily L., Aiken [aut]

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Washington, D.C : The World Bank, 2022Beschreibung: 1 Online-Ressource (47 pages)Schlagwörter:

Andere physische Formen: Erscheint auch als: Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan. Druck-Ausgabe Washington, D.C. : The World Bank, 2023DOI: DOI: 10.1596/1813-9450-10252Online-Ressourcen:

Zusammenfassung: 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 sourcePPN: PPN: 1837814465Package identifier: Produktsigel: ZDB-1-WBA | ZDB-110-WBL | ZDB-110-WBO

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