IIn 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. While consumers benefit from observing reviews written by others, they do not account for the benefit they generate from trying a new product and revealing the product’s quality to everyone else. In this paper, we examine the inefficiency linked to 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, from 2016 to 2019. We then introduce a counterfactual tax-subsidy scheme aimed at changing the relative price of listings with few compared to many reviews, thereby shifting demand and addressing the cold-start problem. In our main counterfactual, we find that the average price decreases by around 17% for listings with at most one review, but increases by almost 10% for listings with more than 15 reviews, which leads to 14% higher demand for the former. Furthermore, the number of listings with at most one review declines by 18%, although the total number of listings increases by 5%. Based on our conservative estimates, widening the price gap between new and established listings leads to a welfare increase amounting to roughly 8.5% of total host revenue on Airbnb in Manhattan.

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.