论文标题
用机器学习和手机数据定位的计划：来自阿富汗的反贫困干预的证据
Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan
论文作者
论文摘要
手机数据可以改善程序的目标吗？通过将来自阿富汗的“大推动”反贫困计划与计划受益人的详细手机日志结合在一起，我们研究了机器学习方法可以在多大程度上准确地区分有资格从不合格家庭中获得计划收益的超贫困家庭。我们表明，利用移动电话数据的机器学习方法几乎可以确定超贫困家庭的准确性，而基于调查的消费和财富措施也可以识别出超贫困的家庭。与基于单个数据源的措施相结合的基于调查的措施与手机数据相结合的分类更为准确。
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.
