Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan
研究利用阿富汗反贫困项目的调查数据和手机日志，发现机器学习方法能通过手机数据准确识别极端贫困家庭，其效果接近基于消费和财富的调查指标，且结合两者可提高分类准确性。
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show 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.