By Camilo Arias
The effective design, implementation and evaluation of public policies rely on accurate socioeconomic data. It would be impossible, for instance, to implement a poverty alleviation program without knowing who the relevant target populations are, where they live, and their economic status. As the United Nations Population Fund puts it, “without accurate data, those most in need remain invisible.”
Traditionally, governments have relied on national censuses to gather this critical data. Data from these country-wide surveys tend to be fairly accurate but can be expensive—too expensive in some cases—for some poorer countries to coordinate on a regular basis. The Democratic Republic of the Congo and Eritrea took their last census in 1985, Myanmar’s last census was in 1983, and Lebanon has not had a census since 1932. Professor Joshua Blumenstock at the University of California, Berkeley, School of Information recently published a study in the American Economic Association Papers and Proceedings, in which he described a machine learning approach he developed to estimate socioeconomic characteristics using “call detail records,” which include records of phone calls, text messages, airtime purchases, mobile money use and other user data. In his study, Blumenstock approximated the wealth of people in Rwanda and Afghanistan using just two months of such call detail records, achieving the same accuracy of a national census at a fraction of the cost.
The idea underlying Blumenstock’s research is that a person’s wealth is correlated with mobile transactions. By determining these correlations, a researcher can use call detail records to estimate general trends for an individual, a region or even an entire country...
Joshua Blumenstock is an assistant professor at the UC Berkeley School of Information and the director of the Data-Intensive Development Lab.