Rating the Competition: Seller Ratings and Intra-Platform Competition

This workshop will take place on Zoom

Product ratings and reviews are commonplace on large online platforms, like Airbnb and Amazon Marketplace. While the fact that ratings and reviews reveal information that may affect competition on the platform is an increasingly popular area of analysis in recent studies, there has been less attention on the fact that ratings are used to order or partially order search results. Platform owners are able to choose the extent to which ratings can be used to determine the probability a given seller is observed by a sets of buyers. As highly rated sellers are valued by buyers, biasing search results towards these sellers increases the probability they are observed increases sales, all else equal. the expected level of competition and therefore reduces prices, all else equal. At the same time, biasing search results in this way also increases the expected level of competition across the network, reducing prices. We analyse this trade-off, showing that when the variance in buyer valuations is low or driven by differences in idiosyncratic preferences, the competition effect renders information environments more biased towards highly rated sellers less profitable than random matching. However, as the variance in seller quality increases, the incentive to match buyers with high quality sellers increases, rendering a ratings-based information environment more profitable. Which information environment is more profitable therefore depends on the properties of the market(s) the platform operates in.