Optimal enforcement of competition law

Despite the recent theoretical developments in the field of antitrust law enforcement, much still needs to be done in practice by competition authorities in order to prevent collusion and price-fixing in the major industries. Although penalties were recently increased considerably and new instruments of cartel deterrence, such as leniency programs, were introduced, still complete deterrence of antitrust law violations has not been achieved. This thesis contributes to the solution of the problem of optimal competition law enforcement. We approach this problem from the angle of possible refinements of current penalty schemes for violations of competition law. In particular, we determine the optimal combination of instruments such as the amount of the fine and the rate of law enforcements, and the optimal structure and design of leniency programs. In the thesis, the main features of current penalty systems are modeled employing the tools of game theory, dynamic games, and dynamic optimization. We stress the importance of the dynamic analyses of competition law enforcement, since it captures better both the current antitrust rules and the crime process in general. Application of the abovementioned tools allows to compare current US and EU penalty schemes for violations of antitrust law and to develop policy implications on how existing penalty schemes can be modified in order to increase their deterrence power.