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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to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.

Details

Date:
April 11
Time:
11:00 am - 12:00 pm
Calendar(s):
School-wide Events, University-wide Events
Event type(s):
Lectures / Seminars / Forums, Virtual Events
Registration:
https://url.seas.harvard.edu/aisi