Steering via Algorithmic Recommendations

Abstract

This article studies whether self-preferencing affects algorithmic recommendations on dominant platforms. We focus on the dual role of Amazon.com as a platform owner and retailer. We find that products sold by Amazon receive substantially more “Frequently Bought Together” recommendations across popularity deciles. To establish causality, we exploit within-product variation generated by Amazon stockouts. We find that when Amazon is out of stock, identical products sold by third-party sellers face an eight-percentage-point decrease in the probability of receiving a recommendation. The pattern can be explained by the economic incentives of steering but not explained by consumer preference. Furthermore, the steering lowers recommendation efficiency.

Keywords: Self-preferencing; product recommendation; vertical integration; e-commerce; digital platform; algorithmic bias

JEL Classification: D22, D43, L11, L81