Reducing Discrimination with Reviews in the Sharing Economy: Evidence from Field Experiments on Airbnb

Ruomeng Cui (), Jun Li () and Dennis J. Zhang ()
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Ruomeng Cui: Information Systems and Operations Management, Goizueta Business School, Emory University, Atlanta, Georgia 30322
Jun Li: Technology and Operations, Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48103
Dennis J. Zhang: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130

Management Science, 2020, vol. 66, issue 3, 1071-1094

Abstract: Recent research has found widespread discrimination by hosts against guests of certain races in online marketplaces. In this paper, we explore ways to reduce such discrimination using online reputation systems. We conducted 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- and African American–sounding names are statistically indistinguishable. We further show that a nonpositive review and a blank review without any content can also help attenuate discrimination, but self-claimed information on tidiness and friendliness cannot reduce discrimination, which indicates the importance of encouraging credible peer-generated reviews. Our results offer direct and clear guidance for sharing-economy platforms to reduce discrimination.

Keywords: discrimination; field experiment; service operations; platform operations (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (36)

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