We evaluate the impact of three proposed regulations of transportation
network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for
drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip
congestion tax. 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 and offer more rides, at the same time that passengers enjoy faster
rides and lower total cost, 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 almost all model parameters, and it occurs because
the wage floors curbs TNC labor market power. 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. The congestion tax has the expected
impact: fares increase, wages and platform revenue decrease. We also construct
variants of the model to briefly discuss platform subsidy, platform
competition, and autonomous vehicles
