|Position:||Assistant Professor, School of Information|
Director, Data-Intensive Development Lab
University of California, Berkeley
p: (510) 642-1464 / f: (510) 642-5814
for encrypted correspondence: my public key
|My work focuses on using novel data and methods to better understand the economic lives of the poor. Most projects are based in developing and conflict-affected countries.|
News and updates
- New: Postdoc opening! We're recruiting a postdoc to work at the intersection of machine learning and economic development.
- 12/2017: Upcoming talks at Paris School of Economics, the University of Göttingen, and DIW Berlin.
- 11/2017: Three papers accepted at ICTD, on measuring development with satellite imagery, measuring the impact of transport infrastructure, and understanding phone upgrades in community cellular networks.
- show more...
Blumenstock, JE (2016). Fighting Poverty with Data, Science, 353(6301), 753-754 [pdf]
Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens. In situations where reliable survey data are missing or out of date, a novel line of research combines big data and machine learning to offer promising alternatives.
Blumenstock, JE, Cadamuro, G, On, R (2015). Predicting Poverty and Wealth from Mobile Phone Metadata, Science, 350(6264), 1073-1076 [pdf]
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. We show that an individual's past history of phone use can be used to infer his or her socioeconomic status, and that the predicted attributes of millions of individuals can in turn be used to accurately reconstruct the distribution of wealth of an entire nation, or to infer the asset distribution of micro-regions comprised of just a few households. In resource-constrained environments where censuses and household surveys are rare, this creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
Blumenstock, JE, Eagle, N, Fafchamps, M (2016). Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters, Journal of Development Economics, 120: 157-181 [pdf]
We provide empirical evidence that an early form of "mobile money" is used to share risk. Our analysis is based on the entire universe of mobile phone-based communications over a four-year period in Rwanda, including millions of interpersonal transfers sent over the mobile phone network. Exploiting the quasi-random timing and location of natural disasters, we show that people make transfers to individuals affected by economic shocks. Unlike other documented forms of risk sharing, the mobile-phone based transfers are sent over large geographic distances and in response to covariate shocks. Transfers are more likely to be sent to wealthy individuals, and are sent predominantly between pairs of individuals with a strong history of reciprocal exchange. [View Video]
Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan - joint with Michael Callen (Harvard) and Tarek Ghani (UC Berkeley)
We report on an experiment examining why default options impact behavior. By randomly assigning employees to different varieties of a salary-linked savings account, we find that default enrollment increases participation by 40 percentage points---an effect equivalent to providing a 50% matching incentive. We then use a series of experimental interventions to differentiate between explanations for the default effect, which we conclude is driven largely by present-biased preferences and the cognitive cost of thinking through different savings scenarios. Default assignment also changes employees' saving habits, and makes them more likely to actively decide to save after the study concludes.
Social Networks and Internal Migration - joint with Xu Tan (University of Washington)
How does the structure of an individual's social network affect his or her decision to migrate? Economic theory suggests two prominent mechanisms --- as conduits of information about jobs, and as a safety net of social support --- that have historically been difficult to differentiate. We bring a rich new dataset to bear on this question, which allows us to adjudicate between these two mechanisms and add considerable nuance to the discussion. Using the universe of mobile phone records of an entire country over a period of four years, we first characterize the migration decisions of millions of individuals with extremely granular quantitative detail. We then use the data to reconstruct the complete social network of each person in the months before and after migration, and show how migration decisions relate to the size and structure of the migrant's social network. We use these stylized results to develop and estimate a structural model of network utility, and find that the average migrant benefits more from networks that provide social support than networks that efficiently transmit information. Finally, we show that this average effect masks considerable heterogeneity in how different types of migrants derive value from their social networks.
Why do referrals work? Selection and peer influence in a million-person network experiment - joint with Greg Fischer (LSE), Dean Karlan (Yale), and Adnan Khan (LSE)
In contexts ranging from health and agriculture to product marketing and job search, individuals referred through social networks are often more likely to take an action than individuals acting in isolation. We conduct an experiment on Pakistan's largest mobile phone network to understand why referrals are effective. The experiment allows us to separate the effect of "selection," whereby individuals referred through social networks have characteristics that make them more likely to act (Beaman & Magruder), from "influence," or the effect of the referral itself, including endorsement (BenYishay & Mobarak), enforcement (Bryan, Karlan, Zinman), and learning (Conley & Udry).
Violence and Financial Decisions: Experimental Evidence from Mobile Money in Afghanistan - joint with Michael Callen (UCLA) and Tarek Ghani (UC Berkeley)
We provide evidence that violence affects how people make financial decisions. Exploiting the quasi-random timing of several thousand violent incidents in Afghanistan, we show that individuals who are exposed to violence are less likely to adopt and use mobile money, a new financial technology, and are more likely to retain cash on hand. This effect is corroborated using data from three independent sources: (i) the entire universe of 5 years of mobile money transactions in Afghanistan; (ii) high-frequency data from a randomized experiment designed to increase mobile money adoption; and (iii) a behavioral lab-in-the-field experiment with experienced mobile money users. Collectively, the evidence highlights an economic cost of violence that operates through individual beliefs, which is large enough to impede the development of formal financial systems in conflict settings.
A Society of Silent Separation: The Impact of Migration on Ethnic Segregation in Estonia - joint with Ott Toomet (Tartu University)
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 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.