Road Traffic Congestion Charging: Experimental Evidence from Bangalore (Job Market Paper)
Abstract: Urban road traffic congestion, especially during the peak hours and in certain areas, is an important urban disamenity in developing countries. Ever since Pigou, economists have maintained that corrective congestion charges are the answer to this inefficiency. However, practical and empirical experience is sparse. Moreover, quantifying these inefficiencies and designing optimal policies requires knowledge of several fundamental preference parameters: how different commuters can adapt their schedules around their ideal times, in order to avoid peak hours, and how much they value time spent commuting, in order to quantify the gains from hypothetical lower travel times. In this paper, I use a rich data set of travel behavior and a theory-motivated field experiment to study preferences over these decisions. I show in the model why observational travel data is necessary but not sufficient to disentangle the fundamental parameters, and how responses to two congestion charge schemes address the problem. The first scheme penalizes travel at typically congested times of the day, while the second charges a flat fee for driving through a small charged area, designed to induce a longer detour. I collected precise trip data from a sample of 500 private vehicle (non-taxi) commuters in Bangalore using an app on their smartphones, and designed, implemented and measured responses to the two schemes as part of a randomized field experiment. In response to departure time incentives, commuters shifted their trips away from peak times, with most of the effect due to leaving earlier in the morning and later in the evening. Under the area charge, 20% to 30% of trips were diverted towards routes that were took 5 minutes longer on average. These effects are driven by a fraction of commuters who respond more strongly. Overall, these results suggest that pricing can help reduce peak-time and network externalities. In ongoing work, I quantify these potential benefits, and compute optimal policies, by first structurally estimating the model parameters, and combining with a calibrated model of how road traffic speeds depend on the volume of travelers.
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.
Billions of Calls Away from Home: Measuring Commuting and Productivity inside Cities with Cell Phone Records (with Yuhei Miyauchi) (paper coming soon)
Abstract: Large cities in developing countries are economically dynamic, connected and dense, and they are changing fast, commonly growing by 30-40% over a decade. The resulting stress on the urban environment creates demand for high quality data on where economic activity takes place, in order to design and evaluate appropriate urban policies. Policy makers and researchers currently rely on infrequent and often delayed censuses and transportation surveys. In this paper, we show how urban commuting flows extracted from cell phone transaction (CDR) data can be used to measure the spatial distribution of income and economic activity within cities. We use CDR data from Dhaka and Colombo, to construct commuting flow matrices for several million users in each metropolis, with fine spatial coverage and daily frequency. We relate commuting to productivity using a model of workplace choice that predicts a gravity equation. We use the full data and the model to recover workplace productivity values that rationalize observed commuting patterns. This procedure essentially assigns higher labor productivity to locations that attract higher in-commuting, ceteris paribus. Empirically, we show that commuting flows from CDR data correlate strongly with flows from a transportation survey in Dhaka. We then show that model-predicted income is a robust predictor of survey-measured income. We apply our approach to measure variation in economic activity at fine spatial and temporal levels. First, we compare the urban economic structures of Dhaka and Colombo. In a second application, we use the model to calculate the implied economic costs of hartals (a form of strike). We find that hartal days have less travel behavior, an effect that is concentrated on commuting routes with high predicted income.
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 the short run, around half of the affected drivers are able to lawfully bypass it by switching to existing unrestricted private travel modes. However, consistent with high marginal rates of congestion, the policy also led to a precisely estimated decrease in average driving travel time excess delay. I provide suggestive evidence that 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.
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.