Surge Pricing Solves the Wild Goose Chase - Castillo, Juan Camilo; Knoepfle, Dan (EC '17 Proceedings of the 2017 ACM Conference on Economics and Computation, 2017)
Ride-hailing applications (apps) like Uber and Lyft introduced a matching technology and market design that is more efficient than traditional taxi systems. However, unlike traditional street-hailing taxi systems, they are prone to a failure mode first anticipated by Arnott (1996): When supply is too low relative to demand, idle drivers end up being too thinly spread throughout a city, forcing matches between drivers and passengers that are far away from each other. Cars are thus sent on a wild goose chase (WGC) to pick up distant customers. This effectively removes cars from the road both directly (as the cars are busy making pick-ups) and indirectly (as cars exit in the face of reduced earnings), exacerbating the problem. This harmful feedback cycle results in a dramatic fall in welfare, hurting both drivers and passengers. We build a theoretical model to highlight the problem of WGCs and then show empirical evidence that they take place in the Uber market in Manhattan. We calibrate our model to make welfare computations. We find that without surge pricing the only way to avoid WGC would be to set a very high price at all times. This implies that surge pricing has been an essential tool for ride-hailing apps, since without it they would not be able to set low prices at times of low demand and become a more desirable alternative to traditional taxis.