Using Information to Curb Racial Discrimination

Christopher Knittel, Donald MacKenzie, Michiko Namazu, Bora Ozaltun, Dan Svirsky and Stephen Zoepf

No 33118, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We test whether more information about customers decreases racial bias. Our setting is the market for shared mobility services. Prior work by Ge et al. (2020) found that Uber drivers are two times more likely to cancel a ride if the passenger’s name is one used predominantly by African Americans. In a randomized control trial, we test whether two alterations to the Uber platform app reduce racial discrimination. Within the standard Uber app, drivers see only the passenger’s rating before accepting a ride. Once they accept the ride, they see the name of the passenger. In the first intervention, we increased the size of the font of the rating to draw attention to the quality of the passenger. In the second intervention, the passenger’s name appears from the beginning. Using the control group observations, we confirmed that the more likely African Americans were to use a name, the more likely a driver cancels the ride. However, increasing the font size of the passenger’s rating eliminates this racial bias. In contrast, we do not find much evidence that showing the name on the initial screen reduces or increases cancellation rates.

JEL-codes: D83 J71 L92 (search for similar items in EconPapers)
Date: 2024-11
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