Regulating TNCs: Should Uber and Lyft Set Their Own Rules?
April 19, 2019, Webb 1100
UC Berkeley, Electrical Engineering and Computer Sciences
We evaluate the impact of two proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for drivers, and (2) a cap on the number of drivers or vehicles. The impact is assessed using a queuing theoretic equilibrium model, which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings pushes the ride-hailing platform to hire more drivers, at the same time that passengers enjoy faster and cheaper rides, while platform rents are reduced. Contrary to standard economic theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for a large range of model parameters, and it occurs because the quality of service measured by passenger pickup time improves as the number of drivers increases. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. We also construct variants of the model to discuss platform subsidy, platform competition, and autonomous vehicles.