Managing Multihoming Workers in the Gig Economy

40 Pages Posted: 16 Jul 2023 Last revised: 24 May 2024

See all articles by Gad Allon

Gad Allon

University of Pennsylvania - The Wharton School

Maxime C. Cohen

Desautels Faculty of Management, McGill University

Ken Moon

University of Pennsylvania - The Wharton School

Wichinpong Park Sinchaisri

Massachusetts Institute of Technology (MIT) - School of Engineering; The Wharton School, University of Pennsylvania - Operations, Information and Decisions Department; University of California, Berkeley - Operations and Information Technology Management Group

Date Written: January 7, 2023

Abstract

Gig economy platforms compete to source labor from common pools of workers, who multihome by dynamically allocating their services in real-time across multiple platforms. The question of how such workers choose between competing platforms has grown in salience. However, the unavailability of comprehensive data has limited our understanding of workers' dynamic multihoming decisions and their impact on the labor supply and operations of platforms.
We address this gap by integrating a major ride-hailing platform's proprietary data regarding individual drivers' detailed trips with public data on the drivers' outside options. Using empirical methods that overcome the remaining data limitations using simulation and machine learning, we structurally estimate the perceived costs that motivate drivers' forward-looking decisions and behavior. Our analysis reveals that workers are short-sighted and value sequences of consistent rewards (e.g., jobs and pay) over highly variable ones. Based on these findings, we explore the impact of compensation and incentives on the platform's labor supply and operations. Our counterfactual analyses indicate that consistent pay dominates variable pay in retaining multihoming workers. For gig platforms seeking to maintain a stable platform workforce, they can further control their labor supply by rewarding uninterrupted work or delaying quits. For policymakers, our research gives insights regarding the design of gig economy incentive schemes and regulations, including specifically New York City's 2018 Driver Income Rules, and their impact on multihoming behavior.

Keywords: gig economy, multihoming, structural estimation, applied generative adversarial networks, incentives, empirical operations, behavioral operations

Suggested Citation

Allon, Gad and Cohen, Maxime C. and Moon, Ken and Sinchaisri, Wichinpong, Managing Multihoming Workers in the Gig Economy (January 7, 2023). The Wharton School Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=4502968 or http://dx.doi.org/10.2139/ssrn.4502968

Gad Allon

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Maxime C. Cohen

Desautels Faculty of Management, McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Ken Moon

University of Pennsylvania - The Wharton School ( email )

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

Wichinpong Sinchaisri (Contact Author)

Massachusetts Institute of Technology (MIT) - School of Engineering ( email )

MA
United States

The Wharton School, University of Pennsylvania - Operations, Information and Decisions Department ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

University of California, Berkeley - Operations and Information Technology Management Group

United States

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