Enhancing Equitable Access to Taxis in NYC Through Search Friction Reduction and Spatial Pricing

This paper studies how search friction and spatial pricing can impact the taxi market in New York City. We use a mean field model, in which the taxi drivers strategically search for customers in different neighborhoods across the city, taking into account the spatial and temporal distribution of the supply and demand as well as the prices across the city. A crucial component of our model is the matching model that captures the statistical economies of scale in the taxi market. Our model captures the interplay between search friction, due to empty taxis and customers within the same neighborhood failing to pair efficiently and spatial pricing, where prices depend on either the origin of the ride alone or both its origin and destination. The use of mobile applications can alleviate search friction within a neighborhood while spatial pricing can incentivize relocation of empty taxis to a neighborhood. We fit our model to a dataset of New York City taxi rides over four years and conduct a series of counterfactual studies to explore how spatial pricing impacts demand for and supply of rides, consumer welfare, and drivers’ profit. Our analysis reveals that there is significant unmet demand in areas with low demand density, while there is little unmet demand in areas with high demand density. Spatial prices that only use origin information can increase consumer surplus by 7.0% of the average fare and serve 2.6% more customers without hurting the drivers’ profit. Moreover, we find that eliminating the (local) search inefficiency alone can increase consumer surplus by 13.9% of the average fare and serve 4.3% more customers while simultaneously increasing drivers’ profit by 2.5% of the average fare. We also observe that improving search efficiency primarily impacts under-served neighborhoods such as upper Manhattan, Brooklyn and Queens, while pricing primarily impacts well-served neighborhoods, for example, the airports, midtown, and downtown Manhattan. This underscores the value of a hybrid mechanism. We propose a mechanism in which (local) search is eliminated in all neighborhoods while spatial pricing is only used in well-served neighborhoods. This mechanism increases consumer surplus by 21.5% of the average fare and serves 8.7% more customers while avoiding higher prices in less affluent neighborhoods of the city. The proposed mechanism achieves 96.3% of the benefits of a citywide spatial pricing and friction removal mechanism