To make high-quality research more accessible and easier to explore.

Fields:
2 results

Spatial Equilibrium, Search Frictions, and Dynamic Efficiency in the Taxi Industry

Review of Economic Studies 2022 89(2), 556-591
This article analyses the dynamic spatial equilibrium of taxicabs and shows how common taxi regulations lead to substantial inefficiencies as a result of search frictions and mis-allocation. To analyse 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.

Personalized Pricing and the Value of Time: Evidence From Auctioned Cab Rides

Econometrica 2025 93(3), 929-958 open access
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. By conditioning prices on drivers' wait times and not on consumers' data, the platform can capture a significant portion of the profits garnered from personalized pricing, and simultaneously benefit consumers.