Yao Luo, University of Toronto

Firms can now use algorithms to regulate workers’ time and activities more stringently than ever before. Using rich transaction data from a ride-hailing company in Asia, we document algorithmic wage-setting and study its impact on worker behavior. The algorithm profiles drivers based on their working schedules. Our data show that drivers favored by the algorithm earn 8% more hourly than non-favored drivers. To quantify the welfare effects of such preferential algorithms, we construct and estimate a two-sided market model with time-varying demand and dynamic labor supply decisions. Results show that removing the preferential algorithm would, in the short term, reduce platform revenues by 12% and total surplus by 7%. In the long run, raising rider fares re-balances demand and supply, resulting in minimal welfare loss. Without the preferential algorithm, an additional 10% of drivers would switch to flexible schedules. Lastly, young, male, local drivers benefit more from the non-preferential algorithm.

DATE: Friday, March 10, 2023

TIME: 3:30-5:00 p.m.

LOCATION: ZOOM – Meeting ID: 894 9273 9391 | Passcode: Yao