Working Paper: NBER ID: w29070
Authors: Emily Aiken; Suzanne Bellue; Dean Karlan; Christopher R. Udry; Joshua Blumenstock
Abstract: The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to over 1.5 billion people. Targeting is a central challenge in administering these programs: given available data, how does one rapidly identify those with the greatest need? 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 Togo’s flagship emergency cash transfer program, which used these algorithms to disburse millions of dollars in 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methods for targeting humanitarian assistance, particularly in crisis settings when traditional data are missing or out of date.
Keywords: Machine Learning; Mobile Phone Data; Humanitarian Assistance; Targeting; COVID-19
JEL Codes: C55; I32; I38; O12; O38
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Machine Learning Approach (C45) | Reduction of Exclusion Errors (Y60) |
| Machine Learning Approach (C45) | Accuracy of Aid Distribution (F35) |
| Machine Learning Approach (C45) | Increase in Exclusion Errors (Y60) |
| Machine Learning Approach (C45) | Improvement in Social Welfare (D60) |
| Machine Learning Model (C45) | Area Under the Curve (AUC) for Rural Targeting (R19) |
| Geographic Targeting Methods (R23) | Area Under the Curve (AUC) for Targeting (C22) |