Matching in Labor Marketplaces: The Role of Experiential Information

58 Pages Posted: 27 Mar 2020

See all articles by Elena Belavina

Elena Belavina

Cornell SC Johnson College of Business

Karan Girotra

Cornell Tech; Cornell SC Johnson College of Business

Ken Moon

University of Pennsylvania - The Wharton School

Jiding Zhang

University of Pennsylvania - The Wharton School

Date Written: February 25, 2020

Abstract

Online labor marketplaces assign workers to short-term jobs. For some jobs, the choice of the best worker is based on ex-ante observable information (e.g., driver assignment based on location in ride-hailing). In others, the assignment is driven by experiential information, that is information obtained privately only through the worker performing the job (e.g., the fit of a childcare provider with a family). This study develops an empirical framework to impute the relative importance of each kind of information from participants' past hiring choices. Our moment inequality approach accommodates high worker turnover, varying choice sets, and limited observations of a very large number of market participants -- all key characteristics of online labor markets. We apply our framework to two markets, exploiting a natural experiment that changed marketplace commissions. Based on over 1.2M hiring decisions, we estimate that experiential information is a key driver of hiring choices, while ex-ante observable fit is relevant only for the simplest jobs. Using our estimates, we propose and evaluate alternate assignment policies. The best-performing policies prioritize repeat work and, surprisingly, ignore ex-ante observable information to instead experiment with new workers and generate experiential information. Such policies can increase buyer welfare by as much as 45.3% (47.1%) of gross revenue in the Data Entry (Web Development) market compared to the current practice of skills-based matching. Policies exploiting buyers' past revealed preferences (in repeat work) without incorporating exploration still under-perform by 18.9% in Data Entry and 8.7% in Web Development.

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

Suggested Citation

Belavina, Elena and Girotra, Karan and Moon, Ken and Zhang, Jiding, Matching in Labor Marketplaces: The Role of Experiential Information (February 25, 2020). Available at SSRN: https://ssrn.com/abstract=3543906 or http://dx.doi.org/10.2139/ssrn.3543906

Elena Belavina

Cornell SC Johnson College of Business ( email )

Ithaca, NY 14850
United States

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

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

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

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