  Digital platforms use recommendations to facilitate exchanges between
platform actors, such as trade between buyers and sellers. Aiming to protect
consumers and guarantee fair competition on platforms, legislators increasingly
require that recommendations on market-dominating platforms be free from
self-preferencing. That is, platforms that also act as sellers (e.g., Amazon)
or information providers (e.g., Google) must not prefer their own offers over
comparable third-party offers. Yet, successful enforcement of self-preferencing
bans -- to the potential benefit of consumers and third-party actors --
requires defining and measuring self-preferencing across a platform. In the
context of recommendations through search results, this research contributes by
i) conceptualizing a "recommendation" as an offer's level of search engine
visibility across an entire platform (instead of its position in specific
search queries, as in previous research); ii) discussing two tests for
self-preferencing, and iii) implementing them in two empirical studies across
three international Amazon marketplaces. Contrary to consumer expectations and
emerging literature, our analysis finds almost no evidence for
self-preferencing. A survey reveals that even if Amazon were proven to engage
in self-preferencing, most consumers would not change their shopping behavior
on the platform -- highlighting Amazon's significant market power and
suggesting the need for robust protections for sellers and consumers.
