The effects of surge pricing on driver behavior in the ride‐sharing market: Evidence from a quasi‐experiment

Abstract

Surge pricing has been used to coordinate supply and demand in the ride-sharing industry, but its causal effects on driver behavior remain unclear. This motivates us to examine how surge pricing causally affects driver earnings and labor supply by leveraging a unique quasi-experiment, in which a leading ride-sharing company in China introduced surge pricing in two cities at different times. Using a difference-in-differences design with the causal forest method, we find that surge pricing led to increases in drivers' weekly revenue. Decomposing the weekly revenue into "intensive margin" and "extensive margin" factors, we discover two countervailing effects at play: a cherry-picking effect and a competition effect, and the daily revenue decreased because the latter dominated. Consequently, the increased weekly revenue can be explained by the extensive margin: drivers worked on more days to compensate for the decreased daily revenue, a result consistent with the income targeting behavior. Finally, we examine heterogeneous treatment effects across drivers, and 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. Therefore, the benefit of surge pricing was unevenly distributed across drivers.

A Systematic Literature Review on Machine Learning in Shared Mobility

“…In conclusion, there exists a notable research gap concerning dynamic pricing and the effective deployment of incentives. While industry giants such as Uber, Lyft, and Didi have seamlessly integrated dynamic pricing into their business models [258], other shared service providers stand to gain from embracing similar strategies. Such approaches can not only mitigate challenges like improper parking but also diminish the necessity for resource-intensive repositioning by providers.…”