Machine learning and phone data can improve targeting of humanitarian aid.
Nature
; 603(7903): 864-870, 2022 03.
Article
in English
| MEDLINE | ID: covidwho-1747206
ABSTRACT
The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes-including exclusion errors, total social welfare and measures of fairness-under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4-21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Relief Work
/
Cell Phone
/
Machine Learning
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
Limits:
Humans
Language:
English
Journal:
Nature
Year:
2022
Document Type:
Article
Affiliation country:
S41586-022-04484-9
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