Research

Working papers

Optimal Public Transportation Networks: Evidence from the World's Largest Bus Rapid Transit System in Jakarta NBER Working Paper NBER Digest
(with Arya Gaduh, Tilman Graff, Rema Hanna, and Ben Olken)
August 2024 (conditional accept, American Economic Review)
Abstract: Designing public transport networks involves tradeoffs between coverage, service frequency, and direct service. We use the expansion of the bus system in Jakarta, Indonesia, to study these tradeoffs. We analyze how new direct connections, changes in bus travel time, and wait time reductions affect bus ridership and aggregate flows, and estimate a transit network demand model by matching the route launch events. Commuters in Jakarta are 2-3 times more sensitive to wait time than bus time, and inattentive to long routes. We develop a flexible framework to characterize optimal networks. A less concentrated network would increase ridership and commuter welfare.

Infrastructure Inequality: Who Pays the Cost of Road Roughness? NBER Working Paper
(with Lindsey Currier and Edward L. Glaeser)
November 2024 (revise and resubmit, Quarterly Journal of Economics)
Abstract: Which Americans experience the worst infrastructure? What are the costs of living with that infrastructure? We measure road roughness throughout America using vertical acceleration data from Uber rides across millions of American roads. Our measure correlates strongly and positively with other measures of road roughness where they are available, negatively with driver speed. We find that road repair events decrease roughness and increase speeds. We measure drivers’ willingness-to-pay to avoid roughness by measuring how speeds change with salient changes in road roughness, such as those associated with town borders and road repaving events in Chicago. These estimates suggest that one standard deviation of road roughness in the US generates losses to drivers of 33 cents per driver-mile. Roads are worse near coasts, and in poorer towns and in poorer neighborhoods, even within towns. We find that a household that drives 3,000 miles annually on predominantly local roads will suffer $450 per year more in driving pain if they live in a predominantly Black neighborhood than in a predominantly White neighborhood. The relationship between road roughness and both race and income is substantially stronger in less populous and rich places. Road roughness has little ability to explain subsequent road resurfacing in eleven cities, which suggests American rides could be much smoother if the bumpiest roads were fixed first.

The Effect of Exposure: Evidence from Spatial Choices in Nairobi
(with Joshua T. Dean and Oluchi Mbonu)
May 2025
Abstract: We study how prior exposure affects high-stakes choices and consideration in the context of spatial decisions. In a sample of 800 casual workers in Nairobi, we offer short-term employment in locations across the city and experimentally induce exposure by training participants on the task in either familiar or unfamiliar locations. Participants are willing to travel 3.5 km further or take a pay cut worth 22% of the median daily wage to avoid working in a location never visited before. This differential is fully offset after one visit to an unfamiliar neighborhood. These results are inconsistent with sorting and information channels, and we find little evidence of first-time navigation costs or risk. The results are most consistent with one-time psychological exposure costs or biased beliefs. Using a separate elicitation, we show that participants are also initially less likely to spontaneously consider working in an unfamiliar neighborhood and a single visit closes part of this gap. Our results suggest that past exposure is an important component of urban mobility costs in cities like Nairobi.

Work In Progress

Women’s Urban Mobility Barriers: Evidence from Delhi’s Free Public Transport Policy (with Girija Borker and Dev Patel) (fieldwork completed) 

Workers in Space (with Julia Cajal Grossi) (fieldwork completed)

Publications

Spatial Externalities, Inefficiency, and Sufficient Statistics Data and Code
(with Kartik Patekar)
AEA Papers and Proceedings 2025 (invited)
Abstract: How much economic inefficiency is generated by spatial externalities such as agglomeration or congestion? What can we learn with data and variation around an inefficient equilibrium? We express deadweight loss in an equilibrium model with spatial externalities, building on Harberger (1964). Our expressions depend on two empirical objects, an externality matrix and a Slutsky substitution matrix. This provides a basis for assessing how modeling assumptions, especially related to patterns of demand substitution, and the variation used for estimation, may constrain inefficiency conclusions. We illustrate extensions to our approach with two examples, on electric vehicle chargers, and on peak-hour traffic congestion.

Peak-Hour Road Congestion Pricing:  Experimental Evidence and Equilibrium Implications  NBER Working Paper (Appendix) Data and Code
Econometrica, Vol. 92, No. 4 (July, 2024) http://dx.doi.org/10.3982/ECTA18422
Abstract: Developing country megacities suffer from severe road traffic congestion, yet the level of congestion is not a direct measure of equilibrium inefficiency. I study the peak-hour traffic congestion equilibrium in Bangalore. To measure travel preferences, I use a model of departure time choice to design a field experiment with congestion pricing policies and implement it using precise GPS data. Commuter responses in the experiment reveal moderate schedule inflexibility and a high value of time. I then show that in Bangalore, traffic density has a moderate and linear impact on travel delay. My policy simulations with endogenous congestion indicate that optimal congestion charges would lead to a small reduction in travel times, and small commuter welfare gains. This result is driven primarily by the shape of the congestion externality. Overall, these results suggest limited commuter welfare benefits from peak-spreading traffic policies in cities like Bangalore.

Measuring Commuting and Economic Activity inside Cities with Cell Phone Records NBER Working Paper  Data and Code
(with Yuhei Miyauchi)
The Review of Economics and Statistics (2023) 105 (4). https://doi.org/10.1162/rest_a_01085
Abstract: We show how to use commuting flows to infer the spatial distribution of income within a city. A simple workplace choice model predicts a gravity equation for commuting flows whose destination fixed effects correspond to wages. We implement this method with cell phone transaction data from Dhaka and Colombo. Model-predicted income predicts separate income data, at the workplace and residential level, and by skill group. Unlike machine learning approaches, our method does not require training data, yet achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high-skill commuters.

Citywide effects of high-occupancy vehicle restrictions: Evidence from “three-in-one” in Jakarta, (with Rema Hanna and Ben Olken)
Science, Vol. 357 (6346), 2017.
Press coverage: Los Angeles Times, CNN, Spectrum IEEE, The Guardian
Abstract: Widespread use of single-occupancy cars often leads to traffic congestion. Using anonymized traffic speed data from Android phones collected through Google Maps, we investigated whether high-occupancy vehicle (HOV) policies can combat congestion. We studied Jakarta’s “three-in-one” policy, which required all private cars on two major roads to carry at least three passengers during peak hours. After the policy was abruptly abandoned in April 2016, delays rose from 2.1 to 3.1 minutes per kilometer (min/km) in the morning peak and from 2.8 to 5.3 min/km in the evening peak. The lifting of the policy led to worse traffic throughout the city, even on roads that had never been restricted or at times when restrictions had never been in place. In short, we find that HOV policies can greatly improve traffic conditions.

Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs Worldwide, (with Abhijit Banerjee, Rema Hanna, and Ben Olken)
World Bank Research Observer, Vol. 32 (2), 2017.
Press Coverage: The New York Times, Vox
Abstract. Targeted transfer programs for poor citizens have become increasingly common in the developing world. Yet, a common concern among policy-makers and citizens is that such programs tend to discourage work. We re-analyze the data from seven randomized controlled trials of government-run cash transfer programs in six developing countries throughout the world, and find no systematic evidence that cash transfer programs discourage work. 

Rapid Innovation Diffusion in Social Networks, (with Peyton Young)
Proceedings of the National Academy of Sciences, Vol. 111 (3), 2014.
Abstract. Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks. 

Fast Convergence in Evolutionary Equilibrium Selection, (with Peyton Young)
Games and Economic Behavior, Vol. 80, 2013.
Abstract. Stochastic best response models provide sharp predictions about equilibrium selection when the noise level is arbitrarily small. The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable equilibrium. An important exception arises when players interact locally in small close-knit groups; in this case convergence can be rapid for small noise and an arbitrarily large population. We show that a similar result holds when the population is fully mixed and there is no local interaction. Moreover, the expected waiting times are comparable to those in local interaction models.

Permanent working papers

Driving Delhi? Behavioural Responses to Driving Restrictions
Press coverage: The Indian Express (1, 2). Ideas for India (1, 2). Nature News.
Abstract. This paper examines two related hypotheses: the ability of urban drivers to effectively bypass policies that restrict road traffic, and whether these behavioural responses render such policies ineffective. I study an unexpected, large scale driving restriction policy experiment in Delhi in 2016. In the short run, around half of the affected drivers are able to lawfully bypass it by switching to existing unrestricted private travel modes. However, the policy also led to a precisely estimated decrease in average driving travel time excess delay. Both effects are broadly similar during a second, anticipated round of the policy. Methodologically, this paper makes two contributions: traffic congestion is quantified using rich data from Google Maps, and short-term driving substitution patterns are identified using panel daily driver data and the essentially random assignment of odd and even license plates.