The Impact of Behavioral and Economic Drivers on Gig Economy Workers.

This article investigates how gig economy workers, specifically ride-hailing drivers, make labor supply decisions regarding whether to work and for how long, focusing on the interplay between financial incentives and behavioral motives. Using a large dataset from a U.S. ride-hailing platform, the authors develop an econometric two-stage model that accounts for sample selection, endogeneity, and market competition, finding that higher wages increase both the likelihood of working and work duration (positive income elasticity), while accumulated earnings reduce these outcomes, indicating income-targeting behavior. Additionally, the study uncovers an inertia effect, where drivers who have worked longer recently are more likely to continue working, contrasting with traditional time-targeting theories. Numerical experiments demonstrate that incorporating these behavioral insights into incentive design can increase service capacity by 22% without extra cost or reduce costs by 30% while maintaining capacity, whereas ignoring behavioral factors risks understaffing by 10%–17%. The findings have implications for gig platforms' operational strategies and policymaking in managing flexible labor forces amid competitive markets.