The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Gérard P. Cachon (), Kaitlin M. Daniels () and Ruben Lobel ()
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Gérard P. Cachon: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Kaitlin M. Daniels: Washington University in St. Louis, St. Louis, Missouri 63130
Ruben Lobel: AirBnB, San Francisco, California 94107

Manufacturing & Service Operations Management, 2017, vol. 19, issue 3, 368-384

Abstract: Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform’s profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.

Keywords: self-scheduling capacity; peer-to-peer markets; contract design; dynamic pricing; service operations; ride sharing (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (179)

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