Matching in Labor Marketplaces With Experiential Learning

75 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; Cornell University - Cornell Tech NYC

Karan Girotra

Cornell Tech; Cornell SC Johnson College of Business

Date Written: June 17, 2024

Abstract

Online labor marketplaces match workers to short-term jobs. Though the quality of hired workers directly impacts the quality of matches, marketplace reputational mechanisms are often inadequately informative, prompting buyers to assess and screen workers directly. We study the platform intermediary's problem of matching workers to jobs when buyers learn worker quality experientially. As a basic trade-off, platforms can either explore new matches to expedite buyers' experiential learning or maximize short-term match quality. Additionally, over-exploring incurs efficiency losses, because every new match incurs new setup costs as workers tailor services to buyers. We develop a structural estimation method to infer hidden worker quality from buyers' hiring decisions and improve platform matching. Our empirical analysis of 1.2M hiring decisions on a major online freelancer platform demonstrates that, in contrast to visible ratings, experiential learning explains approximately 87% of the variation in applicants' utility to buyers. Based on our estimates, we propose improved platform matching policies that importantly calibrate between promoting existing matches and exploring new workers. They increase buyer welfare by up to 45-47% of gross revenue. We observe high value from exploration: in the two markets we study, greedy policies under-explore and therefore underperform revenue-wise by 18.9% and 8.7%.

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

Suggested Citation

Moon, Ken and Zhang, Jiding and Belavina, Elena and Girotra, Karan, Matching in Labor Marketplaces With Experiential Learning (June 17, 2024). 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

Cornell University - Cornell Tech NYC ( email )

2 West Loop Rd.
New York, NY 10044
United States

Karan Girotra

Cornell Tech ( email )

2 West Loop Rd.
New York, NY 10044
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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
663
Abstract Views
2,870
Rank
82,408
PlumX Metrics