We evaluate the problem of firms that operate platforms matching buyers and sellers, while
also selling goods on these same platforms. By being able to guide consumer search through
algorithmic recommendations, these firms can influence market outcomes, a finding that has
worried regulators. To analyze this phenomenon, we combine rich novel data about sales and
recommendations on Amazon Marketplace with a structural model of intermediation power.
In contrast to prior literature, we explicitly model seller entry. This feature enables us to assess
the most plausible theory of harm from self-preferencing, i.e. that it is a barrier to entry. We
find that recommendations are highly price elastic but favor Amazon. A substantial fraction of
customers only consider recommended offers, and recommendations hence noticeably raise the
price elasticity of demand. By preferring Amazon’s offer, the recommendation algorithm raises
consumer welfare by approximately $4.5 billion (since consumers also prefer these offers).
However, consumers are made worse off if self-preferencing makes the company raise prices
by more than 7.8%. By contrast, we find no evidence of consumer harm from self-preferencing
through the entry channel. Nevertheless, entry matters. The algorithm raises consumer welfare
in the short and medium run by increasing the purchase rate and intensifying price competition.
However, these gains are mostly offset by reduced entry in the long run.