Dynamic Pricing Solves the Urban Transportation Disparity – Evidence from NYC On-Demand Ride-Sharing Drivers

In this paper, we empirically investigate the effect of the dynamic pricing system on ride-sharing platform drivers' labor supply. Rather than working-hour and wage-rate relation explored by previous and current literature, we examine the instantaneous response of drivers to price surges. Using data from New York City, we estimate the structural model through a constrained non-parametric instrumental variable (NPIV) approach. We find that the emergence of a price surge is a strong incentive for drivers, and the dynamic pricing scheme of ride-sharing platforms effectively solves the geographical disparity problem of uncoordinated taxi systems. Consequently, the overall accessibility and quantity of pickup service in the entire city will increase. In the absence of dynamic pricing, we show in a counterfactual analysis that platform drivers will be clumped in the Manhattan area and airports, a dilemma shared by the taxi drivers. The counterfactual context implies that 27% of the total supply will be lost, including a significantly large 59% reduction in the non-Manhattan area