skip to main content
10.1145/3219166.3219192acmconferencesArticle/Chapter ViewAbstractPublication PagesecConference Proceedingsconference-collections
abstract

Surge Pricing Moves Uber's Driver-Partners

Published: 11 June 2018 Publication History

Abstract

We study the impact of dynamic pricing (so-called "surge pricing") on relocation decisions by Uber's driver-partners and the corresponding revenue they collected. Using a natural experiment arising from an outage in the system that produces the surge pricing heatmap for a portion of Uber's driver-partners over 10 major cities, and a difference-in-differences approach, we study the short-run effect that visibility of the surge heatmap has on 1) drivers' decisions to relocate to areas with higher or lower prices and 2) drivers' revenue. We demonstrate that the ability to see the surge heatmap has a statistically significant impact on both outcomes. Ability to see the surge heatmap explains 10%-60% of Uber drivers' self-positioning decisions, attracts drivers toward areas with higher surge prices, and increases drivers' revenue on surged trips by up to 70%. This suggests that dynamic pricing helps drivers move to where riders' demand is largest, and that the resulting reduction in spatial search friction and spatial mismatch improves waiting times and welfare for both riders and drivers.

Supplementary Material

MP4 File (p3.mp4)

Cited By

View all
  • (2024)A queueing model of dynamic pricing and dispatch control for ride-hailing systems incorporating travel timesQueueing Systems10.1007/s11134-023-09901-y106:1-2(1-66)Online publication date: 4-Feb-2024
  • (2023)Spatial Matching under Resource CompetitionSSRN Electronic Journal10.2139/ssrn.4488342Online publication date: 2023
  • (2023)Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systemsTransportation Research Part B: Methodological10.1016/j.trb.2023.102848178(102848)Online publication date: Dec-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
EC '18: Proceedings of the 2018 ACM Conference on Economics and Computation
June 2018
713 pages
ISBN:9781450358293
DOI:10.1145/3219166
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2018

Check for updates

Author Tags

  1. dynamic pricing
  2. labor economics
  3. ride sharing economy

Qualifiers

  • Abstract

Conference

EC '18
Sponsor:

Acceptance Rates

EC '18 Paper Acceptance Rate 70 of 269 submissions, 26%;
Overall Acceptance Rate 664 of 2,389 submissions, 28%

Upcoming Conference

EC '25
The 25th ACM Conference on Economics and Computation
July 7 - 11, 2025
Stanford , CA , USA

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)19
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)A queueing model of dynamic pricing and dispatch control for ride-hailing systems incorporating travel timesQueueing Systems10.1007/s11134-023-09901-y106:1-2(1-66)Online publication date: 4-Feb-2024
  • (2023)Spatial Matching under Resource CompetitionSSRN Electronic Journal10.2139/ssrn.4488342Online publication date: 2023
  • (2023)Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systemsTransportation Research Part B: Methodological10.1016/j.trb.2023.102848178(102848)Online publication date: Dec-2023
  • (2022)Distributed service with proximal capacity and pricing on a two‐sided sharing economy platformJournal of Operations Management10.1002/joom.122269:5(742-763)Online publication date: 27-Sep-2022
  • (2021)Revenue Management and the Rise of the Algorithmic EconomyManagement Science10.1287/mnsc.2020.371267:9(5389-5398)Online publication date: 1-Sep-2021
  • (2021)Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ride-Sharing PlatformsINFORMS Journal on Applied Analytics10.1287/inte.2021.109151:5(373-390)Online publication date: Sep-2021
  • (2020)Learning-based open driver guidance and rebalancing for reducing riders’ wait time in ride-hailing platforms2020 IEEE International Smart Cities Conference (ISC2)10.1109/ISC251055.2020.9239059(1-7)Online publication date: 28-Sep-2020
  • (2020)A review of Ride-Matching strategies for Ridesourcing and other similar servicesTransport Reviews10.1080/01441647.2020.1866096(1-22)Online publication date: 30-Dec-2020
  • (2019)Spatio-temporal Adaptive Pricing for Balancing Mobility-on-Demand NetworksACM Transactions on Intelligent Systems and Technology10.1145/333145010:4(1-28)Online publication date: 24-Jul-2019
  • (2019)Spatial crowdsourcing: a surveyThe VLDB Journal10.1007/s00778-019-00568-7Online publication date: 29-Aug-2019
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media