Essays on the industrial organization of the taxi industry

This dissertation addresses several open questions in industrial organization economics, with a particular focus on dynamic economic models of spatial equilibrium and labor supply; spatial differentiation of supply and demand; spatially differentiated regula- tion; and the frictions stemming from the search and matching process. These questions are centered around the market for taxicabs, an important segment of the transportation industry that is currently facing regulatory overhaul in cities around the world. The first chapter analyzes the dynamic spatial equilibrium of taxicabs and shows how common taxi regulations lead to substantial inefficiencies. Taxis compete for pas- sengers 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 ar- eas, and excess demand in other areas. To analyze the role of regulation on frictions and efficiency, I pose a dynamic model of search and matching between taxis and passengers under regulation. 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. My estimates show that search frictions reduce welfare by $422M per year, or 62%. Counterfactual analysis reveals that existing regulations attain only 11% of the efficiency implied by a social planner’s solution, while the adoption of optimized two- part tariff pricing would lead to 89% efficiency, or a welfare gain on the order of $2.1B per year. These gains are further enhanced by the addition of directed matching technology. The second chapter considers the estimation of dynamic discrete choice models in a semi-parametric 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. 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; resembling classic estimators from the literature on semi-parametric regression and average derivative estimation. We use this estimator to evaluate a structural model of dynamic labor supply for New York City taxicab drivers. The third chapter presents an analysis of tipping in taxi markets. Tipping represents an approximately $50 billion economy in the U.S., but little empirical research has been done to understand the systematic determinants of tips. A substantial impediment to this type of analysis is a lack of data; not only are tips frequently unreported, they are often computed in part based on a judgement of service quality, a highly subjective characteristic that is difficult to reliably measure. This paper characterizes the determinants of tipping. Using New York taxi industry data, I identify and document new information about how tips are paid out in the taxi industry.