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 method...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
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Washington, D.C
The World Bank
2022
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| Collection: | World Bank E-Library Archive - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | 47 pages |