Applied Micro Seminar - Tiffany Tsai (NUS)

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Reshad Ahsan

rahsan@unimelb.edu.au

T: +61390358147

Title: Steering via Algorithmic Recommendations

Abstract: We study a platform's incentive to maximize the value of data in their recommendation. We ask if and how Amazon's dual identity as an information intermediary and a seller may affect its data-driven ``Frequently Bought Together'' recommendations (FBT). We document that Amazon-selling products receive 70% more FBTs while sending a similar number as non-Amazon-selling ones. We show that (1) controlling price and sales, the same product receives 8% fewer FBTs during Amazon's temporary absence; (2) steering is stronger in categories where FBT is estimated to be more effective; (3) recommending non-Amazon-selling recipients is estimated to be more than 50% efficient than recommending Amazon-selling recipients.

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Meeting ID: 91635821413
Password: 454941