Design Information Disclosure under Bidder Heterogeneity in Online Advertising Auctions: Implications of Bid-Adherence Behavior

Bidding is a key element of search advertising, but the variation in bidders' valuations and strategies is often overlooked. Disclosing bid information helps uncover this heterogeneity and enables platforms to tailor their disclosure policies to meet objectives like increasing consumer surplus or platform revenue. We analyzed data from a platform that provided bid recommendations based on historical bids. Our findings reveal that advertisers vary significantly in their strategies: some follow the platform's recommendations, while others create their own bids, deviating from the provided information. This highlights the need for customized information disclosure policies in online ad marketplaces. We developed an equilibrium model for Generalized Second Price (GSP) auctions, showing that adhering to bid recommendations with positive probability is suboptimal. We categorized advertisers as bid-adhering or bid-constructing and developed a structural model for self-bidding to identify private valuations. This model allowed for a counterfactual analysis of the impact of different levels of information disclosure. Both theoretical and empirical results suggest that moderate increases in disclosure improve platform revenue and market efficiency. Understanding bidder diversity is crucial for platforms, which can design more effective disclosure policies to address varying bidder needs and achieve their goals through costless information sharing.