Spatial Pricing in Ride-Sharing Networks

Kostas Bimpikis, Ozan Candogan and Daniela Saban
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Kostas Bimpikis: Stanford University
Ozan Candogan: University of Chicago
Daniela Saban: Stanford University

Research Papers from Stanford University, Graduate School of Business

Abstract: We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders are heterogeneous in terms of their destination preferences and their willingness to pay for receiving service. Drivers decide whether, when, and where to provide service so as to maximize their expected earnings, given the platform's prices. Our findings highlight the impact of the demand pattern on the platform's prices, profits, and the induced consumer surplus. In particular, we establish that profits and consumer surplus are maximized when the demand pattern is "balanced" across the network*s locations. In addition, we show that they both increase monotonically with the balancedness of the demand pattern (as formalized by its structural properties). Furthermore, if the demand pattern is not balanced, the platform can benefit substantially from pricing rides differently depending on the location they originate from. Finally, we consider a number of alternative pricing and compensation schemes that are commonly used in practice and explore their performance for the platform.

Date: 2018-01
New Economics Papers: this item is included in nep-com, nep-mkt, nep-net, nep-pay, nep-tre and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:repec:ecl:stabus:3482

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