The Empowering and Competition Effects of the Platform-Based Sharing Economy on the Supply and Demand Sides of the Labor Market

ABSTRACT

The sharing economy has fundamentally changed the way many individuals work. In this paper, we study the impact of the entry of a major ridesharing platform into U.S. Metropolitan Statistical Areas (MSAs), on the supply and demand sides of the labor market. Leveraging the difference-in-differences (DID) research design and a data set combining multiple U.S. Census archival sources, we exploit the variation in labor market metrics before and after Uber’s entry into the MSAs. Our empirical findings reveal that the introduction of the ridesharing platform has an empowering effect on workers (the supply side of the labor market) and a competition effect on traditional jobs (the demand side of the labor market). Specifically, Uber’s entry into the MSAs increases labor force participation, decreases the unemployment rate of residents living below the poverty level, and improves the employment and financial status of low-income workers. In addition, Uber’s entry reduces the employment number and increases wages of conventional low-skill and/or low-wage jobs. This paper provides empirical evidence of the impact of a digital sharing economy platform on the labor market and suggests that policymakers and platform operators should account for this broader impact when they devise policies and make strategic decisions.

KEYWORDS:

• Sharing economy
• labor market
• two-sided platforms
• empowering effect
• competition effect
• ridesharing platforms
• market entry
• traditional jobs
• low-wage jobs

Supplemental Material

Supplemental data for this article can be accessed on the publisher’s website

Notes

¹ “Alternative work arrangements” refer to temp agency workers, on-call workers, contract workers, and independent contractors or freelancers.

² Metropolitan Statistical Areas (MSAs) are defined by the U.S. Office of Management and Budget for statistical purposes (https://www.census.gov/programs-surveys/metro-micro/about.html).

³ According to World Bank’s World Development Report, “empowerment” refers to “the enhancement of an individual or group’s capacity to make choices and transform those choices into desired actions and outcomes.” We use the World Bank definition of this term. It is important to point out that it is somewhat different than the dictionary definition of empowerment.

⁴ A survey conducted by the Benenson Strategy Group was released on December 7, 2015. The survey found that flexibility is a top motivating factor for people to work as Uber drivers (https://www.uber.com/newsroom/driver-partner-survey/).

⁵ https://www.uber.com/newsroom/driver-partner-survey/.

⁶ We acquired this information from one Uber operations manager during a company visit and verified its accuracy using multiple online news sources.

⁷ “Employees” are all part-time and full-time workers who are paid a wage or salary. The survey does not cover self-employed individuals, such as owners and partners in unincorporated firms, household workers, or unpaid family workers.

⁸ https://www.cnbc.com/2020/03/09/uber-lyft-doordash-react-to-coronavirus-workers-say-its-not-enough.html.

⁹ https://www.lyft.com/blog/posts/supporting-our-community.

Additional information

Notes on contributors

Ziru Li

Ziru Li ([email protected]) is a Ph.D. candidate in the department of Information Systems at the W. P. Carey School of Business, Arizona State University. Her research focuses on business analytics in online platforms, such as sharing economy platforms, mobile software platforms. She is also interested in big data analysis, machine learning, stochastic modelling and network analysis. She received Outstanding Research Award in the Department of Information Systems for Academic Year 2018-2019.

Yili Hong

Yili Hong ([email protected]; corresponding author) is a Professor and Bauer Senior Fellow in the Department of Decision & Information Sciences in the C. T. Bauer College of Business at University of Houston, where he also manages the Bauer College Ph.D. programs. His research areas are future of work, digital platforms, and human-AI interaction. Dr. Hong’s research has been published in such journals as Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of AIS, and others. He is a Senior Editor of Production and Operations Management and an associate editor of other journals.

Zhongju Zhang

Zhongju Zhang ([email protected]) is Professor of Information Systems and Data Analytics at the W. P. Carey School of Business, Arizona State University. His research focuses on how information systems/technology and data analytics impact consumer behavior, create business value, and transform business models. He has specific interests in online collaborative platforms and social media, fraud and fake news detection as well as its business and social impacts, platform economy and business models, economic aspects of digital transformation, machine learning and data science. Dr. Zhang’s work has appeared in such journals as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Production and Operations Management, and many others. He serves on the editorial boards of several journals and has won numerous research and teaching awards.