Data for Social Good

Data for Social Good

The proliferation of mobile phones and other technologies in all regions of the world provides a unique opportunity to use large-scale digital data for social good.  By developing a set of methods for understanding social and economic behavior in developing countries, work at the DataLab is helping to promote effective public policy for alleviating poverty and producing positive social change.  

Current Projects

Promises and Pitfalls of Mobile Money in Afghanistan: Evidence from a Randomized Control Trial

We present the results of a field experiment in Afghanistan that was designed to increase adoption of mobile money, and determine if such adoption led to measurable changes in the lives of the adopters. The intervention we evaluate is a mobile salary payment program, in which a random subset of individuals of a large firm were transitioned into receiving their regular salaries in mobile money rather than in cash. While mobile money salaries led to immediate and significant cost savings to the employer, we find little consistent evidence that mobile money had an impact on several key indicators of individual wealth or well-being. Taken together, these results suggest that while mobile salary payments may greatly increase the efficiency and transparency of traditional economies, in the short run the benefits may be realized by those making the payments, rather than by those receiving them.

A Society of "Silent Separation": Migration and Ethnic Segregation in Estonia

We exploit a novel source of data to model the impact of migration and urbanization on segregation in Estonia.  Analyzing the complete mobile phone records of hundreds of thousands of Estonians, we observe the ethnicity of each individual on the network (Russian or Estonian), the complete history of locations visited by each individual, and every phone-based interactions taking place over the network.  We find that the ethnic composition of an individual's geographic neighborhood heavily influences the structure of the individual's phone-based network.  We further find that patterns of segregation are significantly different for migrants than for the at-large population: migrants are more likely to interact with coethnics than non-migrants, but are less sensitive to the ethnic composition of their immediate neighborhood than non-migrants.