44 Pages Posted: 28 Dec 2016 Last revised: 21 Jul 2017
Date Written: July 14, 2017
Ride-hailing apps introduced a more efficient matching technology than traditional taxis (Cramer and Krueger, 2016), with potentially large welfare gains under the appropriate market design. However, we show that when price is too low they fall into a failure mode first pointed out by Arnott (1996) that leads to market collapse. An over-burdened platform is depleted of idle drivers on the streets and is forced to send cars on a wild goose chase to pick up distant customers. These chases occupy cars, reducing the number of customers served, earnings and thus effectively removing drivers from the road and exacerbating the problem. We use data from Uber to show that wild goose chases are indeed a problem in the Manhattan market. The effects of wild goose chases dominate more traditional price theoretic considerations and imply that welfare and profits fall dramatically as price falls below a certain threshold and only gradually move in price above this point. A platform forced to charge uniform prices over time will therefore have to set very high prices to avoid catastrophic chases. Dynamic "surge pricing" can avoid these high prices while maintaining the system functioning when demand is high.
Keywords: wild goose chases, ride-hailing, surge pricing, dynamic pricing, hypercongestion
JEL Classification: D42, D45, D47, L91, R41
Suggested Citation: Suggested Citation
Castillo, Juan Camilo and Knoepfle, Daniel T. and Weyl, E. Glen, Surge Pricing Solves the Wild Goose Chase (July 14, 2017). Available at SSRN: https://ssrn.com/abstract=2890666