Game Theory as a Yardstick for Antitrust Leniency Policy: the US, EU, and Italian Experiences in a Comparative Perspective

The allocative and productive inefficiencies generated by cartels have induced many governments to tackle them aggressively, thus making those agreements increasingly difficult to detect. Rewarding firms that help expose cartels with immunity or fine reductions is generally believed to enhance the effectiveness of antitrust enforcement, but no consensus exists as to how to frame leniency policies in order to maximize the incentives for firms to co-operate with antitrust authorities.The goal of this paper is threefold: (i) to compare the US, EU, and Italian leniency policies in order to identify their key differential features; (ii) to employ game theory as a yardstick to assess the different solutions and (iii) to single out the most effective ones for an optimal leniency scheme.To this end, this paper will first discuss, drawing upon insights from Rubinfeld, Leslie, Motta, and Polo, the application of game theory to cartels and leniency programs in general. Second, it will carry out a comparative assessment of the US, EU, and Italian systems. Once the key distinctive features have been identified, they will be analyzed through the lens of game theory in order to determine the incentives those solutions create for co-operation by cartel members with antitrust authorities.To conclude, specific recommendations for an optimal leniency scheme will be presented, having specific regard to: i) the eligibility of cartel instigators, ringleaders, and coercers for antitrust leniency; ii) penalty reductions for follow-up whistleblowers; iii) Amnesty Plus-style incentives to report membership in multiple cartels; and iv) decreasing penalty discounts according to co-operation order.