Matching and Pricing in Ride Hailing: Wild Goose Chases and How to Solve Them

Juan Camilo Castillo (), Dan Knoepfle () and E. Glen Weyl ()
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Juan Camilo Castillo: Department of Economics, University of Pennsylvania, Philadelphia, Pennsylvania 19130
Dan Knoepfle: Uber Technologies, Inc., San Francisco, California 94158
E. Glen Weyl: Plural Technology Collaboratory, Microsoft Research, Redmond, Washington 98005

Management Science, 2025, vol. 71, issue 5, 4377-4395

Abstract: We show that ride-hailing markets are prone to a matching failure (“wild goose chases”) in which high demand sets off a harmful feedback cycle of few idle drivers, high pickup times, and low earnings, drastically reducing welfare. After characterizing these failures theoretically and showing empirical evidence of their relevance, we analyze how platforms can avoid them. Raising prices brings demand back under control. Platforms can thus set a uniform high price, or they can use high “surge” pricing during high demand times while keeping prices low at other times. Some adjustments to the matching algorithm can also avoid the problem, but surge pricing performs better than them.

Keywords: ride hailing; market design; matching; first dispatch; surge pricing (search for similar items in EconPapers)
Date: 2025
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