Digital Breadcrumbs and Dietary Diversity: Testing the Limits of Cell Phone Metadata in Poverty and Impact Assessment

• Development Workshop

Date and Location

Tuesday, February 22, 2022, 4:10 PM - 5:30 PM
ARE Conference Room, 2102 Social Sciences and Humanities

Abstract

A series of recent papers demonstrate that cell phone metadata, in conjunction with machine
learning algorithms, can be used to estimate the wealth of individual subscribers, and to target
resources to poor segments of society. This paper uses survey data from an emergency cash
transfer program in Haiti, in combination with mobile phone data from potential beneficiaries,
to explore whether similar methods can be used for impact evaluation. A conventional regression
discontinuity-based impact evaluation using survey data shows positive impacts of cash trans-
fers on household food security and dietary diversity. However, machine learning predictions
of food security derived from mobile phone data do not show statistically significant effects;
nor do the predictions accurately differentiate beneficiaries from non-beneficiaries at baseline.
Our analysis suggests that the poor performance is likely due to the homogeneity of the study
population; when the same algorithms are applied to a more diverse Haitian population, per-
formance improves markedly. We conclude with a discussion of the implications and limitations
for predicting welfare outcomes using big data in poor countries.