Algorithmic Pricing, Price Wars, and Tacit Collusion: Evidence from E-Commerce
TL;DR
A novel e-commerce data set is employed to examine the effect of algorithmic pricing in the wild and finds that “resetting” strategies are effective at coaxing competitors to raise their prices.
Abstract
As the economy digitizes, menu costs fall, and firms can more easily monitor prices. These trends have led to the rise of automated pricing (and repricing) tools. We employ a novel e-commerce data set to examine the effect of algorithmic pricing in the wild. Evidence from an event study suggests that firms that start employing repricing tools drop their prices by 16.93%, with market prices falling by 9.67%. However, algorithmic pricing companies have developed “resetting” strategies (which regularly raise prices in the hope that competitors will follow) in order to avoid stark Bertrand-Nash competition. We find that these strategies are effective at coaxing competitors to raise their prices; when a resetting strategy is adopted on a market with less than six serious competitors, both competitor prices and market prices eventually increase by 11.4%. Although 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 the average price over the cycle will be the monopoly price. Moreover, if the available repricing technologies remain fixed, cycling and prices could rise significantly. However, cycling is still relatively rare in the data, even when studying a convenience sample of products with at least one merchant using a repricing tool. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02462 .