Wage Elasticity of Labor Supply in Real-Time Ridesharing Markets : An Empirical Analysis

The prominence of real-time ridesharing services, such as Uber and Lyft, has dramatically changed the landscape of traditional industries. In this study, we provide a comprehensive analysis on the wage elasticity of labor supply in real-time ridesharing markets using data from a major ridesharing platform in China. By exploiting an exogenous shock from uneven driving restrictions as an instrumental variable, we find a negative labor supply elasticity for ridesharing drivers, suggesting that drivers tend to drive less during days with higher average hourly wage. This surprising finding is consistent with the behavioral income-targeting model based on the theory of reference-dependent preferences: Drivers have heuristic daily targets for total earnings and are more motivated to supply labor when they are below their income target than above it. Therefore, they work less on days when earnings per hour are high and quit the market once their income target is reached. In addition, we find that taxi drivers are more rational and are less affected by the income target. What is more interesting is that the labor supply elasticity is less negative for more experienced and patterned ridesharing drivers, which implies that drivers are more rational when they have repeated opportunities of learning. Estimating labor supply elasticity is critical to understand the economic efficiency of various surge pricing algorithms and driver subsidization programs for ridesharing platforms and policy makers. Our research suggests that a uniform price surging or driver subsidization to all ridesharing drivers may not incentivize labor supply of drivers effectively. A more efficient approach is to implement personalized price surging algorithms and driver subsidization programs targeting more experienced and patterned drivers