Ride-hailing technology mitigates effects of driver racial discrimination, but effects of residential segregation persist

• 成果类型: Article
• 署名作者: Cobb, Anna; Mohan, Aniruddh; Harper, Corey D.; Nock, Destenie; Michalek, Jeremy
• 署名单位: Carnegie Mellon University; Princeton University; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13664
• DOI: 10.1073/pnas.2408936121
• 发表日期: 2024-09-30
• 关键词: uber

摘要

We assess racial disparities in the service quality of app-based ride-hailing services, like Uber and Lyft, by simulating their operations in the city of Chicago using empirical data. To generate driver cancellation rate disparities consistent with controlled experiments (up to twice as large for Black riders as for White riders), we estimate that more than 3% of drivers discriminate by race. We find that the capabilities of ride- hailing technology to rapidly rematch after a cancellation and prioritize long-waiting customers heavily mitigates the effects of driver discrimination on rider wait times, reducing average discrimination-induced disparities to less than 1 min-an order of magnitude less than traditional taxis. However, our results suggest that even in the absence of direct driver discrimination, Black riders in Chicago wait about 50% longer, on average, than White riders because of historically informed geographic residential patterns. We estimate that if Black riders in the city had the same wait times as White riders, the collective travel time saved would be worth $4.2 million to $7.0 million per year.