Surge Pricing and Labor Supply in the Ride-Sourcing Market

TLDR

The study develops equilibrium models of ride‑sourcing labor supply under various behavioral assumptions to evaluate surge pricing performance. Using a time‑expanded network, the authors formulate and solve bi‑level programs that model drivers’ work‑hour choices under neoclassical and income‑targeting hypotheses, and analyze surge pricing and a simple regulation scheme. The results show that surge pricing raises revenue for the platform and drivers but can harm customers during intense surge periods.

Abstract

This study proposes equilibrium models under different behavioral assumptions of labor supply in a ride-sourcing market and then investigates the performance of surge pricing. A time-expanded network is first proposed to delineate possible work schedules of drivers. Based on the proposed network, we provide formulations and algorithms for both neoclassical and income-targeting hypotheses to characterize the labor supply of ride-sourcing drivers, i.e., their choices of work hours. We then investigate the impact of surge pricing using a bi-level programing framework, with the lower-level problem capturing the equilibrium work hour choices while the upper-level one representing revenue-maximizing surge pricing. Compared to static pricing, the platform and drivers in general enjoy higher revenue while customers may be made worse off during highly surged periods. A simple regulation scheme to reduce market power is discussed.