Spatial Equilibrium, Search Frictions and Dynamic Efficiency in the Taxi Industry [updated 2/2020]
Revise & Resubmit, Review of Economic Studies
Abstract: This paper analyzes the dynamic spatial equilibrium of taxicabs and shows how common taxi regulations lead to substantial inefficiencies. Taxis compete for passengers by driving to different locations around the city. Search costs ensure that optimal search behavior will still result in equilibrium frictions in the form of waiting times and spatial mismatch. Medallion limit regulations and fixed fare structures exacerbate these frictions by preventing markets from clearing on prices, leaving empty taxis in some areas, and leaving excess demand in other areas. 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 $4.7 million in daily welfare split across 182 thousand trips, but an additional 45 thousand customers fail to find cabs due to search frictions. Counterfactual analysis shows that implementing pricing rules that vary by time, location or distance can enhance allocative efficiency and expand the market, offering daily net surplus gains of up to $232 thousand and 93 thousand additional daily taxi-passenger matches, substantially better than the gains due to a perfect static matching technology. [Equilibrium Algorithm Demo]
The Value of Time: Evidence from Auctioned Cab Rides (with Laura Doval, Jakub Kastl, Filip Matejka and Tobias Salz) [updated 8/2020]
Revise & Resubmit, Econometrica
Abstract: We recover valuations of time using detailed consumer choice data from a large ride-hail platform, where drivers bid on trips and consumers choose between a set of rides with different prices and waiting times. We estimate demand as a function of prices and waiting times and find that price elasticities are substantially higher than waiting-time elasticities. We show how these estimates can be mapped into values of time that vary by place, person, and time of day. The value of time during non-work hours is 16% lower than during work hours. Most of the heterogeneity in the value of time, however, is explained by individual differences. Unlike in other studies that focus on long-run choices, we do not find evidence of spatial sorting. We apply our estimates to study optimal time incentives in highway procurement. Standard industry practices, which set incentives based on a uniform value of time, lead to mis-priced time costs by up to ninety percent.
Semiparametric Estimation of Dynamic Discrete Choice Models (with Matt Shum and Haiqing Xu) [updated 9/2019]
Forthcoming, Journal of Econometrics
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.
Selected Works in Progress
- Flexible Work Hours and Dynamic Labor Supply: Evidence from Taxi Drivers (with Matt Shum and Haiqing Xu) (old draft)
- Platform Design in Ridehail: An Empirical Investigation (with Laura Doval, Jakub Kastl and Tobias Salz)