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.
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.