The Impact of Behavioral and Economic Drivers on Gig Economy Workers
34 Pages Posted: 20 Nov 2018
Date Written: October 29, 2018
Gig economy firms benefit from labor flexibility by hiring independent self-scheduling workers. This labor flexibility poses a great challenge in planning and committing to a service capacity. In collaboration with a ride-hailing company, we study how on-demand workers make labor decisions: when to work and for how long. We are interested not only in improving the prediction of the number of active drivers but also in understanding how to design better financial incentives. Using a large comprehensive dataset, we analyze workers' decisions and responses to incentives while accounting for sample selection bias, simultaneity, and endogeneity. Our results reconcile competing theories of labor supply regarding the impact of income shocks on labor decisions. We find that financial incentives have a significant positive influence on the decision to work and on the number of work hours. This finding confirms the positive income elasticity from the neoclassical theory of labor supply. We also find support for a behavioral theory as workers exhibit income targeting (they work less when they get closer to their earning goal) and inertia (they work more when they have worked for longer). We finally show via numerical experiments that our approach can increase service capacity by 25% without incurring additional cost, or maintain the same capacity at a 27.69% lower cost.
Keywords: empirical operations, gig economy, incentives, sample selection, behavioral operations
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