Effects of Leniency Programs on Cartel Stability

This paper studies the effect of leniency programs on the stability of cartels under two different regimes of fines, fixed and proportional.We analyze the design of self-reporting incentives, having a group of defendants.Moreover, we consider a dynamic setup, where accumulated (not instantaneous) benefits and losses from crime are taken into account.We obtain that cartel occurrence is less likely if the rules of the leniency programs are more strict and the procedure of application for leniency is more confidential.Moreover, we conclude that, when the procedure of application for leniency is not confidential and penalties and rate of law enforcement are low, leniency may increase duration of cartel agreements.Surprisingly, under a fixed penalty scheme the introduction of a leniency program cannot improve the effectiveness of antitrust enforcement when the procedure of application for leniency is not confidential.antitrust;law;policy

Determination of Optimal Penalties for Antitrust Violations in a Dynamic Setting

We analyze a differential game describing the interactions between a firm that might be violating competition law and the antitrust authority.The objective of the authority is to minimize social costs (loss in consumer surplus) induced by an increase in prices above marginal costs.It turns out that the penalty schemes which are used now in EU and US legislation appear not to be as efficient as desired from the point of view of minimization of consumer loss from price-fixing activities of the .rm.In particular, we prove that full compliance behavior is not sustainable as a Nash Equilibrium in Markovian strategies over the whole planning period, and, moreover, that it will never arise as the long-run steadystate equilibrium of the model.We also investigate the question which penalty system enables us to completely deter cartel formation in a dynamic setting.We found that this socially desirable outcome can be achieved in case the penalty is an increasing function of the degree of offence and is negatively related to the probability of law enforcement.antitrust;policy;law;dynamic games

Analysis of the Properties of Current Penalty Schemes for Violations of Antitrust Law

The main feature of the penalty schemes described in current sentencing guidelines is that the fine is based on the accumulated gains from cartel or price-fixing activities for the firm.These gains are usually difficult to estimate, but they can be approximated by a fraction of the turnover.The regulations thus suggest modeling the penalty as an increasing function of the accumulated illegal gains from price-fixing to the firm, so that the history of the violation is taken into account.We incorporate these features of the penalty scheme into an optimal control model of a profit-maximizing firm under antitrust enforcement.In order to determine the effect of taking into account "the history of the violation", we compare the outcome of this model with a model where the penalty is fixed.The main result of the analysis of the later model is that complete deterrence can be achieved only at the cost of shutting down the firm. The proportional scheme improves upon the fixed penalty since it can ensure complete deterrence in the long run, even when penalties are moderate. Phase-diagram analysis shows that the higher the probability and severity of punishment, the sooner cartel formation is blocked.Further, a sensitivity analysis is provided to show which strategies are most successful in reducing the degree of price-fixing.It turns out that, when the penalties are already high, the antitrust policy aiming at a further increase in the severity of punishment is less efficient than the policy that increases the probability of punishment.antitrust;law;policy

To Protect in Order to Serve: Adverse Effects of Leniency Programs in View of Industry Asymmetry

This paper studies the application of leniency programs. An analysis of the structure and design of leniency programs and existing literature raises a new question: Are leniency programs effective, in the sense that they deter cartels from formation, in asymmetrical markets? A game theoretical model, which allows for asymmetry and predatory pricing, is used to provide an answer. A leniency program does not always lead to a breach of trust. We find that, in certain industries, leniency programs are unable to break collusion. They may have the adverse effect in the sense that they strengthen cartel stability or may even lead to abuse of market power. A relatively large firm can use coercion to remove the option to a smaller firm to self-report to the authorities, thus removing the risk of prosecution posed by the program. In industries characterized by a certain degree of asymmetry in market shares and high sunk costs this is an even more likely scenario. In view of this limitation, a number of policy implications are provided in the paper. Policies aimed at the removal of the threat of retaliation need to be considered in order to convict and deter these kinds of cartels.Antitrust Policy;Antitrust Law;Self-reporting;Leniency Programs

Strictness of Leniency Programs and Cartels of Asymmetric Firms

This paper studies the effects of leniency programs on the behavior of firms participating in illegal cartel agreements.The main contribution of the paper is that we consider asymmetric firms.In general, firms differ in size and operate in several different markets.In our model, they form a cartel in one market only.This asymmetry results in additional costs in case of disclosure of the cartel, which are caused by an asymmetric reduction of the sales in other markets due to a negative reputation effect.This modeling framework can also be applied to the case of international cartels, where firms are subject to different punishment procedures according to the laws of their countries, or in situations where following an application for leniency firms are subject to costs other than the fine itself and where these costs depend on individual characteristics of the firm.Moreover, following the rules of existing Leniency Programs, we analyze the effects of the strictness of the Leniency Programs, which reflects the likelihood of getting complete exemption from the fine even in case many firms self-report simultaneously.Our main results are that, first, leniency programs work better for small (less diversified) companies, in the sense that a lower rate of law enforcement is needed in order to induce self-reporting by less diversified firms.At the same time, big (more diversified) firms are less likely to start a cartel in the first place given the possibility of self-reporting in the future.Second, the more cartelized the economy, the less strict the rules of leniency programs should be.Antitrust Policy;Antitrust Law;Self-reporting;Leniency Programs

Analysis of Current Penalty Schemes for Violations of Antitrust Laws

The main feature of the penalty schemes described in current sentencing guidelines is that the fine is based on the accumulated gains from cartel activities or price-fixing activities for the firm. The regulations suggest modeling the penalty as an increasing function of the accumulated illegal gains from price fixing to the firm, so that the history of the violation is taken into account. We incorporate these features of the penalty scheme into an optimal control model of a profit-maximizing firm under antitrust enforcement. To determine the effect of taking into account the history of the violation, we compare the outcome of this model with a model where the penalty is fixed. The analysis of the latter model implies that complete deterrence can be achieved only at the cost of shutting down the firm. The proportional scheme improves upon the fixed penalty, since it can ensure complete deterrence in the long run, even when penalties are moderate. Phase-diagram analysis shows that, the higher the probability and severity of punishment, the sooner cartel formation is blocked. Further, a sensitivity analysis is provided to show which strategies are most successful in reducing the degree of price fixing. It turns out that, when the penalties are already high, the antitrust policy aiming at a further increase in the severity of punishment is less efficient than the policy that increases the probability of punishment.Optimal Control; Dynamic Analysis; Antitrust Policy; Antitrust Laws

Optimal Enforcement of Competition Law.

Despite the recent theoretical developments in the field of antitrust law enforcement, much still needs to be done in order to prevent collusion and price-fixing in the major indiustries. 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 enforcement, 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 analysis of competition law enforcement, since it captures better both the current antitrust rules and the crime process in general. Application of the above-mentioned tools allows us 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.