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

How Do Labor Markets Equilibrate? Estimating the Effect of Local Labor Demand Shocks on Internal Migration and Local Wages

A central question in the study of migration concerns the role that migrants play in bringing an economy towards a more efficient use of its resources.  This project aims to substantially improve our state of knowledge about migration through the use of novel sources of large-scale, behavioral data.

Measuring Migration with Large-Scale Behavioral Data

Understanding the causes and effects of internal migration is critical to the effective design and implementation of policies that promote human development. Here, we describe how large sources of geotagged data generated by mobile phones can provide a novel source of data on internal migration.

We examine the relationship between violence and financial decisions in Afghanistan. Using three separate data sources, we find that individuals experiencing violence retain more cash and are less likely to adopt and use mobile money, a new financial technology.

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.

Risk Sharing and Mobile Phones: Evidence in the Aftermath of Natural Disasters

We provide empirical evidence that an early form of "mobile money" is used to share risk. Our analysis uses a unique dataset containing the entire universe of one country's mobile phone communications over a four-year period, and exploits spatio-temporal variation in communication caused by earthquakes and floods. We show that individuals are significantly more likely to send money to people affected by economic shocks, and that gifts are driven more by a desire for reciprocity than purely altruistic motives.

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.

Many critical policy decisions depend upon reliable and up-to-date information on market prices. We evaluate Premise Data, a new technology for measuring price information using crowd-sourced data contributed by local citizens. Our evaluation focuses on Liberia, a country with a history of economic and political instability. Our results indicate that the crowd-sourced price data is strongly correlated with traditional price indices, but that statistically and economically significant deviations exist that require deeper investigation.

Mobile-izing Savings with Automatic Contributions: Experimental Evidence on Dynamic Inconsistency and the Default Effect in Afghanistan

Automatic payroll deduction plans, such as the popular 401(k) account in the U.S., represent one of the most effective means of increasing savings in developed countries. We designed and evaluated a mobile phone-based automatic payroll deduction system in Afghanistan, a country with limited formal financial infrastructure. Working with Afghanistan's largest telecommunications operator, we developed and launched a new mobile savings account, using a randomized control trial to concurrently evaluate 24 variants of a single basic account. Our results indicate that access to this account significantly increases the average employee's total savings.