A Critique of Recent Remedies for Third-Party Pricing Algorithms and Why the Solution Is Not Restrictions On Data Sharing

A growing antitrust challenge is competitors using a pricing algorithm supplied by the same data analytics company. Although there can be procompetitive efficiencies in outsourcing pricing, the risk of anticompetitive harm in having a common agent influence competitors’ prices is severe. To deal with this challenge, a remedy has recently been proposed in the United States at the federal level and is being adopted at the local level. This remedy prohibits a third party’s use of nonpublic competitor data. If firms A and B both subscribe to the same third party, that third party is prohibited from using the nonpublic data of firm B in the pricing algorithm that recommends prices to firm A. The contribution of this paper is to critically examine this remedy. First, it is explained the remedy creates inefficiencies that need to be recognized. Second, and more importantly, it is shown the remedy may not prevent the harm it is intended to prevent. More specifically, a workaround is developed whereby a third party can result in firms charging supracompetitive prices while not using nonpublic competitor data. The problem is that the remedy focuses on shared data when the source of harm is shared objective.