This study demonstrates that mobile phone network data can enhance the targeting of humanitarian aid. Using machine-learning algorithms trained on survey data, the researchers identified patterns of poverty in mobile phone usage. This allowed them to prioritize aid to the poorest mobile subscribers. An evaluation of a cash transfer program in Togo showed that this approach reduced exclusion errors by 4–21% compared to geographic targeting, but increased them by 9–35% when compared to a hypothetical social registry approach. The findings highlight the potential of new data sources to complement traditional methods in targeting humanitarian assistance, especially in crisis situations.
Publisher
Nature
Published On
Mar 31, 2022
Authors
Emily Aiken, Suzanne Bellue, Dean Karlan, Chris Udry, Joshua E. Blumenstock
Tags
humanitarian aid
mobile phone data
machine learning
poverty patterns
cash transfer
targeting methods
exclusion errors
Related Publications
Explore these studies to deepen your understanding of the subject.