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Can Machine Learning and Mobile Phone Data Improve the Targeting of Humanitarian Assistance? by Joshua Blumenstock

April 11 @ 11:00 am - 12:00 pm

Targeting is a central challenge in the administration of anti-poverty programs: given available data, how does one rapidly identify the individuals and families with the greatest need? Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to other feasible targeting options, the machine learning approach reduces errors of exclusion by 4-21%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.


April 11
11:00 am - 12:00 pm
School-wide Events, University-wide Events
Event type(s):
Lectures / Seminars / Forums, Virtual Events