In digital markets with peer-to-peer reviews, new products encounter the so-called “cold-start” problem. Little-known products are bought too rarely and remain little known. In this paper, we examine the inefficiency associated with social learning on Airbnb, including its implications for hosts’ price, entry and exit decisions. We estimate a dynamic structural model of demand and supply for Airbnbs in Manhattan, New York. We find that addressing the cold-start problem by lowering the price of new listings relative to incumbent ones leads to a welfare increase exceeding 8% of total host revenue.

This paper studies bid rigging in auctions with bidder preselection. We develop a theoretical model to analyze the optimal behavior of a partial bid-rigging cartel and show how commonly used two-stage auction formats, in which the first stage is used to preselect bidders, may be exploited. Bidder preselection based on opening bids allows cartels to exclude competitive rivals and thereby increase procurement costs above what would be possible without preselection. To test our predictions, we use administrative data from public procurement in Slovakia. We develop a collusion marker reflecting the optimal cartel strategy and identify bidders suspected of collusion. After a selective auction procedure was abandoned, these collusive bidders adjusted their strategy and the savings gap between auctions with and without collusion decreased.

We want to find out whether the concern of seller and consumer harm as a result of Amazon entry finds emprical support beyond anecdotal evidence provided by the media. To this end, we measure the predictors and effects of Amazon first-party retail entry on consumer and third-party merchant outcomes in the Home & Kitchen department of Germany’s Marketplace between 2016 and 2021. While the empirical setting presented challenges for estimating causal effects, our results are broadly inconsistent with systematic adverse effects of Amazon entry on Amazon Marketplace.