Leniency Policies and Cartel Success: An Experiment

See https://​www.​justice.​gov/​atr/​speech/​detecting-and-deterring-cartel-activity-through-effective-leniency-program.

In the US, the fine is 20% of “affected volume”, which refers to revenue, and in South Korea it equals 10% of revenue. In December 2021, after this study was conducted, the fine size in Korea was increased to 20% of revenue, and is now comparable to that in the US.

While prior experiments show that awarding such rewards is effective in reducing the number of cartels, we rule out the study of reward systems here. The reason is that we are also interested in achieving high revenue from fines for the regulatory authority.

There are advantages and disadvantages to allowing this communication. Two disadvantages are that such communication is typically illegal in the field and it is difficult to model theoretically. One advantage is that is allows inexperienced players to have a chance at establishing a cartel more readily, since it allows some players to educate others about the benefits of cartelization and build confidence in each other. We felt that cartel formation would be enhanced if communication were allowed. Otherwise, the strategic uncertainty would be too strong for players to collude. We wanted to give cartels a decent chance of occurrence. In the field cartel participants might not communicate directly, but are often quite familiar with how their competitors are thinking.

The payoffs assume that any firms that are not members of the current cartel behave non- cooperatively: as Cournot players vis-a-vis the cartel and other non-cartel members.

In this paper, all comparisons between No-len and High1, as well as between No-len and High2, are one-sided, since we have a hypothesis (1) with regard to the sign of the differences between these treatment pairs. Furthermore, all comparisons between Low2 and High2, and between Low1 and High1 are also one-sided since we also have a hypothesis (3) about differences between these two treatments. Though the hypotheses refer to the cartel success rate – the primary measure of policy effectiveness – we also use one-sided tests for the cartel formation rate, cartel exposure rate, industry profit, and fine revenue, since these are all measures that are related to the cartel success rate. Thus, we test one-sided hypotheses that High1 and High2 lead to lower cartel formation rates, higher exposure rates, lower industry profit, and greater fine revenue than does No-Len. High1 exhibits the same differences relative to Low1 and High2 the same relationships with Low2. All other p-values are based on two-sided tests. In all tests, each group’s activity over the 10 periods that they played is taken as one observation, so that we have ten observations under each treatment. For example, in testing whether the cartel formation rate differs between two treatments, we have ten observations in each treatment, where each observation is the percentage of periods in which a group has formed a cartel in the ten periods that the group interacted.

For this test, the cartel exposure rate is calculated as: (the number of cartels that a group makes that are exposed)/(the number of cartels that a group forms). Thus, for groups that do not form cartels, the variable is not defined, and groups for which this is the case are not included in the test. This particularly affects the comparisons of the exposure rate of full cartels, which form relatively infrequently.