The Effects of Surge Pricing on Driver Behavior in the Ride-Sharing Market: Evidence from a Quasi-Experiment

Abstract

Surge pricing (dynamic pricing) is commonly used to coordinate supply and demand in the ride-sharing industry; however, its effect on driver behavior remains unclear. We examine how surge pricing causally affects driver earnings and labor supply patterns by leveraging a unique quasi-experimental setting in which a leading ride-sharing company in China introduced surge pricing in different cities over different periods of time. We collect detailed trip-level data for 7 million trips associated with a random sample of 17,400 drivers over a 10-month period from two geographically proximate cities, one with surge pricing introduced and the other without. We investigate the causal impact of surge pricing on driver supply patterns via a difference-in-differences design facilitated by the causal forest method, and obtain the following results. First, we find that surge pricing increased a driver’s weekly revenue (on average). Next, to examine the impact of surge pricing on driver behavior, we decompose the driver’s weekly revenue into “intensive margin” and “extensive margin” factors. Our intensive margin analysis reveals two countervailing effects at play: a cherry-picking effect that increases the driver’s daily revenue, and a competition effect that decreases the driver’s daily revenue. On average, we find that the latter effect dominates, resulting in decreased daily revenue. Consequently, the increased weekly revenue can be explained by the extensive margin: Drivers worked on more days to make up for the decreased daily revenue. When we consider heterogeneous treatment effects across drivers, we find that surge pricing enticed more part-time drivers to flood the market and crowd out full-time drivers, and that the increase in the drivers’ weekly revenue was primarily driven by part-time drivers. Our results provide an important managerial implication for the ride-sharing platform: While surge pricing increased total revenue, the benefit was unevenly distributed across full-time and part-time drivers. Therefore, the platform might consider compensating full-time drivers for their income loss to reduce driver churn and achieve sustainable profitable growth.