Matching in Labor Marketplaces: The Role of Experiential Information

47 Pages Posted: 27 Mar 2020 Last revised: 15 Jun 2023

See all articles by Ken Moon

Ken Moon

University of Pennsylvania - The Wharton School

Jiding Zhang

Arizona State University (ASU) - Department of Information Systems

Elena Belavina

Cornell SC Johnson College of Business

Karan Girotra

Cornell Tech; Cornell SC Johnson College of Business

Date Written: June 14, 2023

Abstract

Online labor marketplaces match workers into short-term jobs. Whereas the quality of a match often hinges on the worker’s intrinsic quality, for many jobs ratings are insufficient and the buyers of services must directly interact with workers in order to learn worker quality. We study the platform intermediary’s problem of matching workers to jobs when worker quality must be learned experientially. We develop new structural empirical methods to infer experientially learned worker quality from observed hiring decisions on platforms and use our estimates to prescribe better platform matching policies. In the presence of experiential learning, platform matching policies face two major trade-offs. First, platforms must choose between experimenting with matches to accelerate experiential learning or maximizing short-term match quality. Second, many jobs require that workers tailor their services to buyers. Then, every new match incurs renewed setup costs over keeping an existing match, and over-experimenting on new matches incurs efficiency losses. Our empirical study of 1.2M hiring decisions on a major online freelancer platform finds that experiential learning exerts significantly greater influence over hiring decisions than reputational information. The best-performing policies increase buyer welfare by up to 45-47% of gross revenue by balancing a priority for repeat work against experimentation with new workers. Experimenting is still valuable: greedy policies under-explore and therefore under-perform revenue-wise by 18.9% and 8.7% in the two markets we study.

Keywords: Choice modeling and estimation, Empirical operations management, Information friction, Market intermediaries, Marketplace design, Matching with costly inspection, Online labor markets

Suggested Citation

Moon, Ken and Zhang, Jiding and Belavina, Elena and Girotra, Karan, Matching in Labor Marketplaces: The Role of Experiential Information (June 14, 2023). Available at SSRN: https://ssrn.com/abstract=3543906 or http://dx.doi.org/10.2139/ssrn.3543906

Ken Moon (Contact Author)

University of Pennsylvania - The Wharton School ( email )

Jon M. Huntsman Hall
3730 Walnut St.
Philadelphia, PA 19104-6365
United States

Jiding Zhang

Arizona State University (ASU) - Department of Information Systems ( email )

Tempe, AZ
United States

Elena Belavina

Cornell SC Johnson College of Business ( email )

New York, NY 10044
United States

HOME PAGE: http://belavina.com

Karan Girotra

Cornell Tech ( email )

111 8th Avenue #302
New York, NY 10011
United States

HOME PAGE: http://www.girotra.com

Cornell SC Johnson College of Business ( email )

Ithaca, NY 14850
United States

HOME PAGE: http://www.girotra.com

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