Publications

Spatial Equilibrium, Search Frictions and Dynamic Efficiency in the Taxi Industry
Review of Economic Studies Volume 89, Issue 2, March 2022
Abstract: This paper analyzes the dynamic spatial equilibrium of taxicabs and shows how common taxi regulations lead to substantial inefficiencies as a result of search frictions and misallocation. To analyze the role of regulation on frictions and efficiency, I pose a dynamic model of spatial search and matching between taxis and passengers. Using a comprehensive dataset of New York City yellow medallion taxis, I use this model to compute the equilibrium spatial distribution of vacant taxis and estimate intraday demand given price and medallion regulations. My estimates show that the weekday New York market achieves about $5.7 million in daily welfare or about $27 per trip, but an additional 53 thousand customers fail to find cabs due to search frictions. Counterfactual analysis shows that implementing simple tariff pricing changes can enhance allocative efficiency and expand the market, offering daily consumer surplus gains of up to $227 thousand and up to 49 thousand additional daily taxi-passenger matches, a similar magnitude to the gain in matches generated by adopting a perfect static matching technology.  [Equilibrium Algorithm Demo]

Semiparametric Estimation of Dynamic Discrete Choice Models (with Matt Shum and Haiqing Xu)
Journal of Econometrics Volume 223, Issue 2, August 2021
Abstract: We consider the estimation of dynamic binary choice models in a semiparametric setting, in which the per-period utility functions are known up to a finite number of parameters, but the distribution of utility shocks is left unspecified. This semiparametric setup differs from most of the existing identification and estimation literature for dynamic discrete choice models. To show identification we derive and exploit a new Bellman-like recursive representation for the unknown quantile function of the utility shocks. Our estimators are straightforward to compute, and resemble classic closed-form estimators from the literature on semiparametric regression and average derivative estimation. Monte Carlo simulations demonstrate that our estimator performs well in small samples.

Working Papers

Personalized Pricing and the Value of Time: Evidence from Auctioned Cab Rides (with Laura Doval, Jakub Kastl, Filip Matejka and Tobias Salz), [Feb. 2024]
Conditionally Accepted, Econometrica
Abstract: We recover valuations of time using detailed data from a large ride-hail platform, where drivers bid on trips and consumers choose between a set of rides with different prices and wait times. Leveraging a consumer panel, we estimate demand as a function of both prices and wait times and use the resulting estimates to recover heterogeneity in the value of time across consumers. We study the welfare implications of personalized pricing and its effect on the platform, drivers, and consumers. Taking into account drivers’ optimal reaction to the platform’s pricing policy, personalized pricing lowers consumer surplus by 2.5% and increases overall surplus by 5.2%. Like the platform, drivers benefit from personalized pricing. ETA-based pricing– where different prices are set for various wait times and where extensive use of consumer data is not required–can capture a significant portion of the profits garnered from personalized pricing, while simultaneously benefiting consumers.

Rethinking Reference Dependence: Wage Dynamics and Optimal Taxi Labor Supply (with Matt Shum and Haiqing Xu) [Aug. 2024] Submitted
Abstract: Workers with variable earnings and flexible hours offer unique opportunities to evaluate intertemporal labor supply elasticities. Existing static analyses, however, have generated well-known puzzles, suggesting evidence of downward sloping labor supply curves. Using a large sample of shifts of New York City taxicab drivers, we estimate a dynamic optimal stopping model of drivers' work times and quitting decisions. Our results demonstrate that the behavioral biases documented in the literature can be explained as rational behavior once we account for forward-looking driver behavior. We use our model to provide new estimates of individual earnings elasticities and show that taxi drivers have similar elasticities to workers in markets where experimental evidence has been obtained. Finally, we use data spanning a 2012 fare change to estimate labor supply elasticities with respect to market prices, accounting for the equilibrium impact of prices on supply and demand.  We find market elasticities to be a small fraction of the size of individual elasticities, suggesting that existing estimates of the benefits to recent earnings legislation in the taxi and ride-hail industries are overstated. 

Selected Works in Progress

Equilibria in the Decentralized Freight Network (with Richard Faltings and John Lazarev)
Abstract: We study equilibrium market structure and pricing across the nationwide freight trucking network, a trillion-dollar market responsible for moving 72% of the nation's goods. Using detailed auction data from the U.S. freight spot market, we document a three-fold per-mile price dispersion across 171 U.S. cities, evidence of significant geographic labor supply preferences, and imbalanced trade flows between regions. To understand these patterns, we pose a micro-founded model of carrier bidding behavior across local markets. Local market outcomes are linked to each other in a spatial equilibrium, as the movement of trucks in the network influences the value of bidding on different shipment destinations. The auction-based setting allows us to obtain rich estimates of carrier cost heterogeneity, search frictions and home-region preferences. Despite the large and competitive national pool of carriers and drivers, markets with a thinner flow of trucks in equilibrium give rise to localized market power. We use our estimates to quantify how these factors contribute to the observed spatial price dispersion and capacity patterns over the network.

Platform Design in Ridehail: An Empirical Investigation (with Laura Doval, Jakub Kastl and Tobias Salz)