This paper investigates the impact of algorithmic assignment on worker behavior and welfare within the ride-hailing industry. We demonstrate how algorithms can impose a flexibility penalty on gig workers, despite their ostensible schedule autonomy. We document a preferential assignment algorithm that favors more committed drivers using rich transaction data from a leading ride-hailing company. Drivers favored by the algorithm earn 8% more hourly than non-favored drivers. Eliminating preferential assignment could raise ride fares by 7.79%, adversely affecting consumers and the platform. On the other hand, an additional 10% of drivers would switch to flexible schedules, leading to a 3.51% surplus gain, especially benefiting young, male, and local drivers.

Prof. Zhe Yuan is a Hundred-Talent Program Research Fellow at the School of Economics, Zhejiang University. He holds a bachelor's degree from Peking University and a Ph.D. from the University of Toronto. His primary research areas include the economics of artificial intelligence, industrial organization, and platform economics, with a focus on the design and impact of algorithms/AI in digital platforms, platform governance, data, and the network economy. Prof. Yuan has published multiple papers in internationally top-tier journals (including RAND Journal of Economics, American Economic Journal: Microeconomics, Management Science, Information Systems Research, and Manufacturing & Service Operations Management). He also serves as an academic advisor for several leading platform companies.