The impact of qualitative reviews in online markets: Empirical and experimental evidence on statistical discrimination

Authors: Noriko Amano-Patiño, Konstantinos Ioannidis, James Morris

Stage: Data collection in progress

Abstract: We investigate the role of customer reviews and host demographics in statistical discrimination within the sharing economy (specifically in online rental markets). Using a controlled experiment in an Airbnb-like setting, we measure how a host's race, a host's gender, and customer reviews interact to affect accommodation demand. We create fictitious listings using scraped data from Airbnb and systematically vary host characteristics across three primary dimensions in a fully crossed 2x2x2 factorial design: Host Race (Black/White), Host Gender (Man/Woman), and a Review factor (High/Low). To isolate specific mechanisms of review-based discrimination, the exact nature of the Review factor varies across three between-participant treatments, manipulating either review quantity, positive informativeness, or negative informativeness. Our experimental design uses a forced-choice pairwise mechanism across three budget blocks (Low, Mid, High). To ensure perfect orthogonality and counterbalancing, the pairings are drawn from a comprehensive property map, with participants assigned to one of 56 block-randomised survey versions. This approach allows us to estimate the causal main effects of each attribute, as well as their interactions, to understand whether specific types of high-quality reviews can mitigate intersectional demographic penalties. Our findings will provide insights for platform design to reduce discrimination in the sharing economy.

Funding: Cambridge Humanities Research Grants (£19,800)

Keywords: statistical discrimination, online rental markets, sharing economy

JEL codes: D83, J15, L84