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

39 Pages Posted: 5 Dec 2015 Last revised: 21 Jun 2016

Gerard P Cachon

The Wharton School - Operations, Information and Decisions Department

Kaitlin M Daniels

University of Pennsylvania - Operations & Information Management Department

Ruben Lobel

University of Pennsylvania - Operations & Information Management Department

Date Written: June 14, 2016

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. 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 in the press and has garnered the attention of regulators due to concerns for the welfare of providers and consumers. However, we find that providers and consumers are generally 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

Suggested Citation

Cachon, Gerard P and Daniels, Kaitlin M and Lobel, Ruben, The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity (June 14, 2016). Available at SSRN: https://ssrn.com/abstract=2698192

Gerard P Cachon (Contact Author)

The Wharton School - Operations, Information and Decisions Department ( email )

Philadelphia, PA 19104
United States

Kaitlin M Daniels

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Ruben Lobel

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Paper statistics

Downloads
1,480
Rank
9,034
Abstract Views
4,057