The Impact of Behavioral and Economic Drivers on Gig Economy Workers

Gad Allon (), Maxime C. Cohen () and Wichinpong Park Sinchaisri ()
Additional contact information
Gad Allon: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Maxime C. Cohen: Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 0G4, Canada
Wichinpong Park Sinchaisri: Haas School of Business, University of California, Berkeley, California 94720

Manufacturing & Service Operations Management, 2023, vol. 25, issue 4, 1376-1393

Abstract: Problem definition: 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. Methodology/results: Using a large comprehensive data set, 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). Managerial implications: 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 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2023.1191 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:25:y:2023:i:4:p:1376-1393

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().