Steering via Algorithmic Recommendations

32 Pages Posted: 13 Jan 2020 Last revised: 25 Apr 2023

See all articles by Nan Chen

Nan Chen

National University of Singapore

Hsin-Tien Tsai

National University of Singapore (NUS), Department of Economics

Date Written: December 1, 2019


This article studies whether self-preferencing affects algorithmic recommendations on dominant platforms. We focus on the dual role of 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

Suggested Citation

Chen, Nan and Tsai, Hsin-Tien, Steering via Algorithmic Recommendations (December 1, 2019). RAND Journal of Economics, Forthcoming, Available at SSRN: or

Nan Chen

National University of Singapore ( email )

21 Lower Kent Ridge Rd
Singapore, 117417


Hsin-Tien Tsai (Contact Author)

National University of Singapore (NUS), Department of Economics ( email )


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