The Impact of Behavioral and Economic Drivers on Gig Economy Workers

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, when to work and for how long. Academic/Practical Relevance: Our model offers a way to reconcile 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 gig economy workers and understanding how to design better incentives. Methodology: 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. Results: 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, inertia could be a potential sign of workers’ loyalty to the platform