Reducing Discrimination with More Information

From Cui, Li and Zhang:

We conduct four randomized field experiments among 1,801 hosts on Airbnb by creating fictitious guest accounts and sending accommodation requests to them. We find that requests from guests with African American-sounding names are 19.2 percentage points less likely to be accepted than those with white-sounding names. However, a positive review posted on a guest’s page significantly reduces discrimination: When guest accounts receive a positive review, the acceptance rates of guest accounts with white-sounding and African American-sounding names are statistically indistinguishable.

In other words, taste based discrimination is weak but statistical discrimination is common. Statistical discrimination happens when legitimate demands for trust are frustrated by too little information. Statistical discrimination is a second-best solution to a problem of trust that both owners/sellers/employers and renters/buyers/workers want to solve. Unfortunately, many people try to solve statistical discrimination problems as if they were problems of invidious prejudice.

If you think the problem is invidious prejudice, it’s natural to try to punish and prevent with penalties and bans. Information bans and penalties, however, often have negative and unintended consequences. Airbnb, for example, chose to hide guest photos until after the booking. But this doesn’t address the real demands of owners for trust. As a result, owners may start to discriminate based on other cues such as names. Instead market designers and regulators should approach issues of discrimination by looking for ways to increase mutually profitable exchanges. From this perspective, providing more information is often the better approach. As Cui, Li, and Zhang write in a HBR op-ed:

Our recommendation is for the platform companies to build a credible, easy-to-use online reputation and communication system. Bringing information to light, rather than trying to hide it from users, is more likely to be a successful approach to tackling discrimination in the sharing economy.

Addendum: See also Tyler and I in The End of Asymmetric Information. We need to work with information abundance rather than try to push against the tide.