Abstract: Gig economy firms benefit from labor flexibility by hiring independent self-scheduling workers. Such flexibility poses a great challenge in terms of planning and committing to a service capacity. Understanding the motivations that drive 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. Our model offers a way to potentially reconcile competing theories of labor supply regarding the impact of income shocks on labor decisions. We are interested not only in improving the prediction of the number of active workers but also in understanding how to design better financial incentives for workers. Using a large comprehensive dataset, we analyze workers’ decisions and responses to incentives to work while accounting for sample selection, simultaneity, and endogeneity biases. We find that financial incentives have a significant positive influence on the decision to work and on the number of hours worked—confirming the positive income elasticity posited by the standard income effect. We also find support for a behavioral theory such as income-targeting behavior (working less when reaching an earning goal) and inertia (continuing to work more after working for a longer period). We show via numerical experiments that incentive optimization based on our model 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.