Algorithmic Pricing Facilitates Tacit Collusion: Evidence from E-Commerce

Abstract

As the economy digitizes, menu costs fall, and firms can more easily monitor prices. These trends have led to the rise of automatic pricing tools. We employ a novel e-commerce dataset to examine the potential implications of these developments on price competition. We provide evidence from an RDD that the immediate impact of automatic pricing is a significant decline in prices. However, repricers have developed strategies to avoid the stark competitive realities of Bertrand-Nash competition. By employing plausibly exogenous variation in the execution of repricing strategies, we find that 'resetting' strategies (which regularly raise prices, e.g., at night) effectively coax competitors to raise their prices. While the resulting patterns of cycling prices are reminiscent of Maskin-Tirole's Edgeworth cycles, a model of equilibrium in delegated strategies fits the data better. This model suggests that if the available repricing technology remains fixed, cycling will increase, and prices could rise significantly in the future.