University of California, Berkeley
Vicens Vives – 14:30
Targeting is a central challenge in the design of anti-poverty programs: given available data, how does one identify the individuals and households with the greatest need? Here we show that machine learning, applied to non-traditional data from satellites and mobile phones, can improve the targeting of anti-poverty programs. Our analysis is based on data from three field-based projects — in Togo, Afghanistan, and Kenya — that illustrate the promise, as well as some of the potential challenges, of this new approach to targeting. Collectively, the results highlight the potential for new data sources to improve humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.