The Impact of Behavioral and Economic Drivers on Gig Economy Workers

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. Funding: This study was supported by The Jay H. Baker Retailing Center, The William and Phyllis Mack Institute for Innovation Management, The Wharton Risk Management and Decision Processes Center, and The Fishman-Davidson Center for Service and Operations Management. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1191 .