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Machine learning and phone data can improve targeting of humanitarian aid

Economics

Machine learning and phone data can improve targeting of humanitarian aid

E. Aiken, S. Bellue, et al.

This study highlights how mobile phone network data can revolutionize the targeting of humanitarian aid. By leveraging machine-learning algorithms to analyze mobile usage patterns, researchers, including Emily Aiken, Suzanne Bellue, Dean Karlan, Chris Udry, and Joshua E. Blumenstock, demonstrated a significant reduction in exclusion errors in aid distribution when compared to traditional geographic methods. Discover the innovative approach transforming aid allocation in crisis situations.

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Playback language: English
Abstract
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
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