Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform

By Ucla M. KEITH CHEN. ACM Conference on Economics and Computation, 2016

358 citations recorded by the source corpus.

Subfields: Automotive Engineering, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/132f2fdccfdf6ccbe9305ed25e0f3c0901d2e758

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Hai Wang
2. Hai Yang

In Marketing

1. Hai Wang
2. Geoffrey G. Parker
3. Panos Constantinides
4. Ola Henfridsson
5. Hai Yang

In Sociology and Political Science

1. Panos Constantinides
2. Ola Henfridsson
3. Hai Wang
4. Hai Yang

Citations in the corpus

• A Marketplace for Data: An Algorithmic Solution (2018, background): “In [32] and [10], platform design and the function of general intermediary service providers for such markets is studied; in [17], advertising auctions are studied; in the context of ride–sharing such as those in Uber and Lyft, the efficiency of matching in [12] and optimal pricing in [8] are studie”
• The Welfare Effects of Peer Entry in the Accommodation Market: The Case of Airbnb (2018, neutral): “This is consistent with analysis of suppliers on Taskrabbit (Cullen and Farronato (2017)) and Uber (Hall et al. (2016), Chen and Sheldon (2015)).”
• Introduction - Platforms and Infrastructures in the Digital Age (2018, background): “Dynamic, or surge pricing, as it is some-times called, can help establish such a supply/demand balance by paying attention to labor-supply elasticities, that is, “the responsiveness of supply hours to changes in the prevailing price of services” (Chen and Sheldon 2016,p.2).”
• Dynamic pricing and matching in ride‐hailing platforms (2019, neutral): “…time horizon: experienced drivers on ride-hailing platforms eventually become cognizant about the time periods and areas of high earnings due to high demand and surge pricing, and flexibly adjust their work schedule to drive during these time periods (Chen and Sheldon 2016, Cachon et al. 2017).”
• Ridesourcing systems: A framework and review (2019, background): “hourly earnings for Uber drivers, Chen et al. (2017a) argue that drivers benefit from real-time flexibility in terms of driver surplus, which is defined as the excess wage paid by the platform over the driver’s reservation wage.”
• From Flexible Labour to ‘Sticky Labour’: A Tracking Study of Workers in the Food-Delivery Platform Economy of China (2021, neutral): “Chen and Sheldon (2016) find that Uber drivers take into consideration the income aspect and are able to flexibly extend their work time during periods of surge pricing.”
• Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers (2019, neutral): “Chen and Sheldon (2015) examined transactional data associated with 25 million trips obtained from Uber and showed empirically that dynamic wage (due to surge pricing) could entice independent drivers to work for longer hours.”