|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.|
Blumenstock, JE, Callen, M, and Ghani, T (2018). Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan, American Economic Review, 108 (10), 2868-2901 [pdf]
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.
Blumenstock, JE (2018). Don't forget people in the use of big data for development, Nature, 561 (7722), 170-172 [pdf]
Aid organizations, researchers and private companies are looking for ways to leverage the 'data revolution' to transform international development. In the rush to find technological solutions to complex global problems, however, there's a danger that we get by the technology and lose track of the deeper issues that are unique to each local context... The CEO of a popular big-data platform recently described data science as "a blend of Red-Bull-fueled hacking and espresso-inspired statistics." In my view, the successful use of big data in development will require a data science that is considerably more humble than this version that has captured the popular imagination.
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]
Migration and the Value of Social Networks - joint with Guanghua Chi, Xu Tan
How do social networks provide utility to migrants? Prior work suggests two prominent mechanisms - as a conduit of information about jobs, and as a safety net of social support - that have historically been difficult to differentiate. We adjudicate these mechanisms using a rich 'digital trace' dataset that allows us to observe the migration decisions made by millions of individuals over several years, as well as the complete social network of each person in the months before and after migration. These data help us establish a new set of stylized facts about the relationship between social networks and migration, for instance that the average migrant is more drawn to 'interconnected' networks that provide social support than to 'expansive' networks that efficiently transmit information. These patterns motivate a structural model of network utility which, when calibrated, provides more general insight into how people derive value from their social networks.
Community Cellular Networks - joint with Niall Keleher, Arman Rezaee, Erin Troland
Despite the rapid expansion of mobile coverage in the developing world, roughly 10% of the world's population still lives beyond the reach of a cell tower. Through a field experiment in the Philippines, we randomly vary whether and when a number of geographically isolated villages receive a new local cellular tower. Our study provides the first exerpimental evidence on the social and economic impacts of first-time access to a mobile phone network.
Insecurity and Industrial Organization: Evidence from Afghanistan - joint with Tarek Ghani, Sylvan Herskowitz, Ethan B. Kapstein, Thomas Scherer, Ott Toomet
One fifth of the world's population lives in countries affected by fragility, violence and conflict, impeding long-term economic growth. However, little is known about how firms respond to local changes in security, partly because of the difficulty of measuring firm activity in these settings. This paper presents a novel methodology for observing private sector activity using mobile phone metadata. Using Afghanistan as the empirical setting, the analysis combines mobile phone data from over 2,300 firms with data from several other sources to develop and validate measures of firm location, size, and economic activity. Combining these new measures of firm activity with geocoded data on violent events, the paper investigates how the private sector in Afghanistan responds to insecurity. The findings indicate that firms reduce presence in districts following major increases in violence, that these effects persist for up to six months, and that larger firms are more responsive to violence.
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.
Scalable Methods for Discovering Latent Structure in Societal-Scale Data - joint with Sham Kakade
Recently, the rapid proliferation of mobile phones and other digital devices has created an unparalleled opportunity to observe and understand the rapidly changing structure of communities in developing and conflict-affected states. However, current state-of-the-art computational methods used to analyse such data are notoriously ill-suited to answer basic, fundamental questions in the social science and policy arena. While many new, provably efficient algorithms for community detection have been recently developed, these methods have several key limitations: they rarely scale to real-world datasets consisting of millions of interconnected actors; they are not applicable to dynamic contexts where network structure evolves over time; and they are almost never validated.
Directories for Mobile Networks - joint with Brian Dillon, Jenny Aker
Mobile phones have spread rapidly throughout developing countries, but they have done so without complementary information services (e.g., phone books) that allow users to search the mobile phone network. We conducted a set of paired RCTs in central Tanzania, centered on the production and distribution of a paper telephone directory relevant to agricultural households. We randomized enterprises into the directory during a trial period, and randomized household access to the directory at the village level. The directory had substantial impacts on both sides, with large increases in the volume of calls to listed enterprises, and substantially greater use of mobile phones for business purposes by directory recipients.