This paper examines the effects of over 1,000 acquisitions by major technology firms on innovation. Using detailed patent and workforce data linked to technology acquisitions and a suite of event-study and difference-in differences designs, we document four main findings. First, although most acquired startups hold no patents, those with patents tend to operate in technology areas where the acquirer already has a presence and which subsequently experience further acquisition activity. Second, innovation typically rises before an acquisition but only persists afterward when follow-on acquisitions occur, suggesting that acquisitions reinforce rather than reverse innovation trends. Third, at the patent level, acquired patents receive significantly more citations after the acquisition than comparable patents, and these effects are not driven solely by self-citations from the acquiring firm. These post-acquisition citation effects are smaller when more employees from the acquired firm are retained, consistent with innovation spillovers occurring through employee mobility. Fourth, we document significant workforce attrition exceeding 60% on average at target firms three years post-acquisition. Our results suggest that acquisitions by digital incumbents often amplify, rather than suppress, the diffusion and visibility of acquired technologies.

We examine product recommendations in Amazon’s “Similar items to consider” box in the US and Canada, finding evidence of self- preferencing in Canada. In our dataset, alternatives to Amazon Basics (AB) products are sometimes recommended in the US but never in Canada, while non-AB products are sometimes recommended in Canada but never in the US. By comparing sales across domains, we find that non-AB products not recommended in Canada due to self-preferencing experience a 22% sales decrease compared to those that are not exposed to self-preferencing in the same way.

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.