The Impact of Behavioral and Economic Drivers on Gig Economy Workers

49 Pages Posted: 20 Nov 2018 Last revised: 14 Jul 2022

See all articles by Gad Allon

Gad Allon

University of Pennsylvania - The Wharton School

Maxime C. Cohen

Desautels Faculty of Management, McGill University

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: October 29, 2018

Abstract

Gig economy companies benefit from labor flexibility by hiring independent workers in response to real-time demand. However, workers' flexibility in their work schedule poses a great challenge in terms of planning and committing to a service capacity. Understanding what motivates gig economy workers is thus of great importance. In collaboration with a ride-hailing platform, we study how on-demand workers make labor decisions; specifically, whether to work and work duration. Our model revisits competing theories of labor supply regarding the impact of financial incentives and behavioral motives on labor decisions. We are interested in both improving how to predict the behavior of flexible workers and understanding how to design better incentives. Using a large comprehensive dataset, we develop an econometric model to analyze workers' labor decisions and responses to incentives while accounting for sample selection and endogeneity. We find that financial incentives have a significant positive influence on the decision to work and on the work duration---confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory as workers exhibit income-targeting behavior (working less when reaching an income goal) and inertia (working more after working for a longer period). We demonstrate via numerical experiments that incentive optimization based on our insights can increase service capacity by 22% without incurring additional cost, or maintain the same capacity at a 30% lower cost. Ignoring behavioral factors could lead to understaffing by 10--17% below the optimal capacity level. Lastly, our insights inform the design of platform strategy to manage flexible workers amidst an intensified competition among gig platforms.

Keywords: gig economy, labor supply, worker behavior, behavioral operations, empirical operations, incentives, sample selection

Suggested Citation

Allon, Gad and Cohen, Maxime C. and Sinchaisri, Wichinpong, The Impact of Behavioral and Economic Drivers on Gig Economy Workers (October 29, 2018). Available at SSRN: https://ssrn.com/abstract=3274628 or http://dx.doi.org/10.2139/ssrn.3274628

Gad Allon

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Maxime C. Cohen (Contact Author)

Desautels Faculty of Management, McGill University ( email )

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

Wichinpong Sinchaisri

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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