  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.
