Basic Information

Position: Assistant Professor, The Information School
Adjunct Assistant Professor, Computer Science & Engineering
Co-Director, Data Science and Analytics Lab
University of Washington
Contact: joshblum[@]uw[.]edu / +1 (206) 685-8746
for encrypted correspondence: my public key
My work focuses on developing new methods for using massive, spatiotemporal network data to better understand the economic lives of the poor. Most of this work is based in developing and conflict-affected countries. I have research positions for qualified PhD students, and an open staff scientist position.

News and updates

Recently Published

Predicting Poverty and Wealth from Mobile Phone Metadata - joint with Gabriel Cadamuro (University of Washington) and Robert On (UC Berkeley)
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of "big data" exist. 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.
Promises and Pitfalls of Mobile Money in Afghanistan: Evidence from a Randomized Control Trial - joint with Michael Callen (Harvard), Tarek Ghani (UC Berkeley), Lucas Koepke (UW)
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 - joint with Marcel Fafchamps (Oxford) and Nathan Eagle (Santa Fe Institute)
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. The magnitude of these transfers is small in absolute terms, but statistically strong. 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]
We describe how large sources of geotagged data generated by mobile phones can provide fine-grained insight into internal migration. We develop and formalize the concept of inferred mobility, and compute this and other metrics on a large dataset containing the phone records of 1.5 million Rwandans over four years. Our empirical results corroborate the findings of a recent government survey that notes relatively low levels of permanent migration in Rwanda. [View Video]

In Progress

Automatic payroll deductions represent one of the most effective means of increasing savings in developed countries. We design and experimentally evaluate a mobile phone-based account that allows savings to be automatically deducted from salaries in Afghanistan, a country with extremely low levels of formal financial inclusion. We find that employees who are automatically enrolled in a defined-contribution account are 40 percentage points more likely to contribute to the account than individuals with a default contribution of zero. We also randomize employer matching contributions and find that the effect of automatic enrollment on participation is approximately equivalent to providing financial incentives equal to a 50 percent match.
Violence and Financial Decisions: Experimental Evidence from Mobile Money in Afghanistan - joint with Michael Callen (UCLA) and Tarek Ghani (UC Berkeley)
Private firms in conflict-affected countries face insecurity, corruption, poor infrastructure, and weak property rights. Disbursing employee wages is a challenge as cash-based payment systems are vulnerable to indirect costs in the form of leakage and theft. We implement a randomized field experiment in Afghanistan to test the effects of a mobile phone-based salary payment system on performance outcomes in a private firm with approximately 400 employees.
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.