Frictions in a Competitive, Regulated Market: Evidence From Taxis (with Guillaume Frechette and Alessandro Lizzeri) -- American Economic Review
Abstract: This paper presents a dynamic equilibrium model of a taxi market. The model is estimated using data from New York City yellow cabs. Two salient features by which most taxi markets deviate from the efficient market ideal are, first, matching frictions created by the need for both market sides to physically search for trading partners, and second, regulatory limitations to entry. To assess the importance of these features, we use the model to simulate the effect of changes in entry, alternative matching technologies, and different market density. We use the geographical features of the matching process to back out unobserved demand through a matching simulation. This function exhibits increasing returns to scale, which is important to understand the impact of changes in this market and has welfare implications. For instance, although alternative dispatch platforms can be more efficient than street-hailing, platform competition is harmful because it reduces effective density.
Estimating Dynamic Games of Oligopolistic Competition: An Experimental Investigation (with Emanuel Vespa) -- accepted at RAND Journal of Economics
Abstract: We evaluate dynamic oligopoly estimators with laboratory data. Using a stylized entry/exit game, we estimate structural parameters under the assumption that the data are generated by a Markov-perfect equilibrium (MPE) and use the estimates to predict counterfactual behavior. The concern is that if the Markov assumption was violated one would mis-predict counterfactual outcomes. The experimental method allows us to compare predicted behavior for counterfactuals to true counterfactuals implemented as treatments. Our main finding is that counterfactual prediction errors due to collusion are in most cases only modest in size.
Intermediation and Competition in Search Markets: An Empirical Case Study -- R&R Journal of Political Economy
Abstract: This paper argues that intermediaries in decentralized markets can affect buyer welfare both directly, by reducing the expenses of buyers with high search cost but also indirectly, through a search-externality that affects the prices paid by those buyers that do not use intermediaries. These two effects are investigated in New York City’s trade waste market, where buyers can either search and haggle by themselves or through a waste broker. Combining elements from the empirical search and procurement-auction literatures, I construct and estimate a model for such a decentralized market. Results from the model show that intermediaries improve welfare and benefit buyers in both the broker and the search market.
Auto Dealer Loan Intermediation: Consumer Behavior and Competitive Effects (with Andreas Grunewald, Jonathan Lanning, and David Low)
Abstract: This paper evaluates the intermediation of auto loans through auto dealers using new and comprehensive administrative data. The arrangement between auto dealers and banks gives dealers discretion to price the loans to consumers. Our project lever- ages detailed knowledge of these vertical relationships to demonstrate that consumers are substantially less responsive to finance charges than to vehicle charges. Dealers take this consumer-specific wedge into account when jointly pricing the car and the loan. The discretion that lenders give to dealers therefore leads to a form of price discrimi- nation. Whether or not this price discrimination is beneficial or harmful for consumers depends on the nature of the wedge. If it is mostly driven by inter-temporal considera- tions, such as credit constraints, dealer discretion can be welfare improving. If instead the wedge is not an accurate representation of underlying preferences or constraints, this price discrimination may decrease consumer welfare. The data favors the latter view. In counterfactual computations we explore what happens to consumer welfare if dealers have no discretion in pricing loans. We find that the total price for consumers would drop by about $350.25, leading to a $1.78 billion increase in annual consumer surplus.
Robust Decisions for Incomplete Models of Strategic Interaction (with Konrad Menzel)
Abstract: We propose Monte Carlo Markov Chain (MCMC) methods for estimation and inference in game-theoretic models with a particular focus on settings in which only a small number of observations for a given type of game is available. In particular we do not assume that it is possible to concentrate out or estimate consistently an equilibrium selection mechanism linking a parametric distribution of unobserved payoffs to observable choices. The algorithm developed in this paper can in particular be used to analyze structural models of social interactions with multiple equilibria using data augmentation techniques. This study adapts the multiple prior framework from Gilboa and Schmeidler (1989) to compute Gamma-posterior expected loss (GPEL) optimal decisions that are robust with respect to assumptions on equilibrium selection, and gives conditions under which it is possible to solve the GPEL problem using one single Markov chain. The practical usefulness of the generic MCMC algorithm is illustrated with an application to revealed preference analysis of two-sided marriage markets with non-transferable utilities.
The Value of Time Across Space: Evidence from Auctioned Cab Rides (with Nick Buchholz, Laura Doval, Jakub Kastl, and Filip Matejka)
Abstract: We use detailed consumer choice data from a large European ride-hailing application to estimate consumer valuations of time. This application offers a unique mechanism that allows drivers to bid on trips and consumers to choose between a set of characteristics of a ride, most importantly, price and waiting time. We leverage rich variation in bids and customer choices to directly measure consumer willingness-to-pay for time savings through revealed preferences. Our estimates provide value-of-time measures that are both time and location dependent. Consumers respond substantially to changes in both price and waiting time, however price elasticities are about three times higher on average than waiting-time elasticities. We then pose a model to microfound the value of time. In the model, the consumer chooses where to spend time as a function of their valuation for different places. The model allows us to decompose the value of time into individual-specific heterogeneity, place-specific heterogeneity, and an interaction between the two. This interaction allows us to measure consumers' location-specific complementarities.
Research in Progress
Platform Design in Ride Hail: An Empirical Ivestigation (with Nick Buchholz, Laura Doval, Jakub Kastl, and Filip Matejka)
Predictive Model Performance and Data Privacy Regulation: Empirical Evidence from GDPR (with Guy Aridor and Yeon-Koo Che)