Regulating Discriminatory Pricing in the Presence of Tacit Collusion

Abstract

Price-setting algorithms have facilitated widespread awareness-based price discrimination, wherein firms charge high prices to customers unaware of alternative choices and low prices to those in competitive markets. This unethical behavior has increased customer complaints and prompted policymakers to enact fairness regulations in response. However, while limiting price discrimination may improve consumer welfare under genuine competition, it also affects firms' incentives to form tacit collusion, another regulatory concern arising from the proliferation of pricing algorithms. We develop an analytical model to examine the interplay between fairness regulation and tacit collusion, and discuss its impact on consumer welfare and policymaking. Firms utilize customers' product unawareness to implement price discrimination and decide whether to collude by comparing profits from collusion and deviation. We then explore the consequences of price fairness regulation on the sustainability of tacit collusion. For homogeneous products, fairness regulation can substantially weaken collusion, potentially rendering it unattainable. However, for differentiated products, strict fairness inadvertently supports collusive behavior, harming consumer welfare. In this case, mild fairness permitting moderate price differentiation can prevent market collusion and optimize welfare. Our key results are valid under various robustness checks such as incorporating Q-learning or using alternative demand models and punishment strategies. We also propose a novel randomized approach to tame fairness-induced collusion. Overall, our study emphasizes the importance of a nuanced approach to regulating discriminatory pricing in the presence of tacit collusion.

Keywords: price discrimination, algorithmic collusion, dynamic competition, fairness

JEL Classification: D63, L13, L40, L51