|Position:||Assistant Professor, School of Information|
Director, Data-Intensive Development Lab
Co-Director, Center for Effective Global Action
University of California, Berkeley
|My work focuses on using novel data and methods to better understand the economic lives of the poor. Most active 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.
Park, PS, Blumenstock, JE, and Macy, MW (2018). The strength of long-range ties in population-scale social networks, Science, 362(6421), 1410-1413 [pdf]
Long-range connections that span large social networks are widely assumed to be weak, comprised of sporadic and emotionally distant relationships. However, researchers historically have lacked the population-scale network data needed to verify the predicted weakness. Using data from eleven culturally diverse population-scale networks on four continents -- encompassing 56M Twitter users and 58M mobile phone subscribers -- we find long-range ties are nearly as strong as social ties embedded within a small circle of friends. These high bandwidth connections have important implications for diffusion and social integration.
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 distracted 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
What is the value of a social network? Prior work suggests two distinct mechanisms that have historically been difficult to differentiate: as a conduit of information, and as a source of social and economic support. We use a rich 'digital trace' dataset to link the migration decisions of millions of individuals to the topological structure of their social networks. We find that migrants systematically prefer 'interconnected' networks (where friends have common friends) to 'expansive' networks (where friends are well connected). A micro-founded model of network-based social capital helps explain this preference: migrants derive more utility from networks that are structured to facilitate social support than from networks that efficiently transmit information.
How Do Firms Respond to Insecurity? Evidence from Afghan Phone Records - 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. This paper provides new evidence on how insecurity impedes economic development by linking data on violent events in Afghanistan to corporate mobile phone records. We first show that phone data can be used to measure private sector activity and validate our approach with administrative and survey data for 2,300 firms in Afghanistan. Next, we find that major terrorist attacks reduce the presence of firms in targeted districts by 4-6\%. The effect is driven both by an increase in the exit of existing firms following attacks and a decrease in the entry of new firms. Our effects are heterogeneous by firm size, suggesting that insecurity disproportionally impacts larger firms.
Manipulation-Proof Machine Learning - joint with Daniel Björkegren (Brown)
An increasing number of decisions are guided by machine learning algorithms. An individual's behavior is typically used as input to an estimator that determines future decisions. But when an estimator is used to allocate resources, individuals may strategically alter their behavior to achieve a desired outcome. This paper develops a new class of estimators that are stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.
Violence and Financial Decisions: Experimental Evidence from Mobile Money in Afghanistan - joint with Michael Callen and Tarek Ghani
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.
Welfare-Aware Machine Learning with Imperfect Data - joint with Esther Rolf, Max Rabinowitz, Lydia Liu, Sarah Dean, Daniel Björkegren and Moritz Hardt
Although real-world decisions typically aim to balance many competing objectives, algorithmic decisions are often evaluated with a single objective function. For example, financial institutions may wish to balance profit with the social welfare of loan recipients, but loan decisions are usually optimized for repayment. Social media platforms may wish to promote user engagement whilst limiting the spread of overtly negative or hostile content, but news feeds typically optimize for engagement. This paper studies algorithmic policies which attempt to optimally trade off between two distinct measures of performance; i.e. profit and social utility, or user engagement and user health. We formalize this trade off and present empirical results on real world datasets for content recommendation and the targeting of humanitarian aid
(Machine) Learning what Governments Value - joint with Daniel Björkegren and Samsun Knight
The rationale behind targeting criteria is not always clear. We combine program eligibility criteria with recent advances in machine learning heterogeneous treatment effects to infer a policymaker's preferences over households and outcomes. Our method can be used to better understand and articulate the allocation of social programs. We find for Mexico's PROGRESA anti-poverty program, government allocations are consistent with a consumption value of 2.03 pesos for each day of child school attendance and 2.64 pesos for each child sick day. Allocations imply welfare weights that place 16.9% more value on the median household for each additional household member, 8% more value if indigenous, and 0.6% less value for each additional year of education of the household head. Alternate eligibility criteria could have marginally improved average health and schooling outcomes at a small cost to average consumption outcomes.
The Impact of Mobile Phones: Experimental Evidence from the Random Assignment of New Cell Towers - 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.
A Society of Silent Separation: The Impact of Migration on Ethnic Segregation in Estonia - joint with Ott Toomet
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.