Many digital platforms operate as two-sided markets, facilitating the matching between sellers and buyers, and the use of algorithms on such platforms is pervasive. The case of Amazon is particularly interesting: Amazon governs the interactions between buyers and sellers but the platform also acts as a seller itself.

This project aims to contribute to the debate on how sellers’ and buyers’ information is exploited on online marketplaces and how algorithmic systems impact on players’ choices, studying to what extent consumers are aware of the functioning of online platforms and whether their behavior is affected by platforms’ design choices. On Amazon, after having searched for an item, a customer can directly proceed to checkout by clicking on the so-called Buy-Box button; however, it is Amazon’s ranking algorithm that selects the seller to be placed in this prominent position, whereas all other sellers are relegated to a second page. Our plan is to quantify the relevance of the Buy-Box channel in terms of sales and to measure consumers’ literacy on the functioning of the platform. We investigate to what extent providing partial or full information on the real features of the Buy-Box design alters consumers’ purchasing behavior in order to understand whether the current design is welfare-enhancing. We plan to collect novel individual-level data on consumers’ purchasing behavior on Amazon through an incentivized experiment where we manipulate the amount of information disclosed to consumers on the functioning of the Buy-Box mechanism. The design will include three treatment conditions: baseline, partial and full information. Our objective is to respond to a fast-growing regulatory demand and to investigate the functioning of algorithms and their impact on online marketplaces. Consumer protection is a first-order policy concern, and our findings could help understand how to implement measures to foster transparency and consumer awareness in these

environments.