Flexible Labor Supply Behavior on Ride-Sourcing Platforms

Abstract

With the popularization of ride-sharing services, drivers working as freelancers on ride-sourcing platforms can design their schedules flexibly. They make decisions regarding whether to participate in work, and if so, how many hours to work. Understanding flexible labor supply behaviour is critical for the platform to manage service capacity. It also helps to evaluate the effects of platform incentives on service capacity and driver welfare. We propose a labor supply model to describe how freelance drivers rationally optimize their labor supply decisions on the platform. Specifically, we model drivers’ participation decision at the extensive margin and hours worked decision at the intensive margin, with an objective to maximize their utility from income and leisure time. We analyze the effects of drivers’ heterogeneity in terms of their other income, idle time, and participation cost. The analytical results show that both the participation decision and the working-hours decision depend on all these drivers’ types, and participating drivers’ working-hour elasticity may be negative under some conditions. With data-driven estimated parameters using inverse optimization, we also propose a framework to design the multi-dimensional incentives on ride-sourcing platforms.