Targeting Development Aid with Machine Learning and Mobile Phone Data

Abstract

Recent papers demonstrate that non-traditional data, from mobile phones and other digital sensors, can be used to roughly estimate the wealth of individual subscribers. This paper asks a question more directly relevant to development policy: Can non-traditional data be used to more efficiently target development aid? 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 other households deemed ineligible. We show that supervised learning methods leveraging mobile phone data can identify ultra-poor households as accurately as standard survey-based measures of poverty, including 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. We discuss the implications and limitations of these methods for targeting extreme poverty in marginalized populations.

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