  Many online retailers and search intermediaries present products on ranked
product lists. Because changing the ordering of products influences their
revenues and consumer welfare, these platforms can design rankings to maximize
either of these two metrics. In this paper, I study how rankings serving these
two objectives differ and what determines this difference. First, I highlight
that when position effects are heterogeneous, rankings can increase both
revenues and consumer welfare by increasing the overall conversion rate.
Second, I provide empirical evidence for this heterogeneity, highlighting that
cheaper alternatives have stronger position effects. Finally, I quantify
revenue and consumer welfare effects across the different rankings. To this
end, I develop an estimation procedure for the search and discovery model of
Greminger (2022) that yields a smooth likelihood function by construction. The
results from the model show that rankings designed to maximize revenues can
also increase consumer welfare. Moreover, these revenue-based rankings reduce
consumer welfare only to a limited extent relative to a
consumer-welfare-maximizing ranking.
