Abstract
The majority of cartels that were discovered by the European Commission (EC) over the last 30 years involved firms that engaged in collusion in more than one market. I investigate the impacts of the EC’s cartel enforcement on the hazards that firms join and leave cartels in multiple markets. I estimate discrete-time recurrent event hazard models for a set of 126 multi-market colluders that were prosecuted between 1985 and 2014. EC investigations in a market decrease the rate at which the cartel members join new cartels and increases the rate at which they leave cartels in other markets. My results shed light on enforcement efforts against cartels and other forms of organized crime .
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Notes
Marx et al. (2015, p. 3) make the same remark.
Lefouili and Roux (2012) also note that conspirators in a specific product or geographic market are likely to have engaged in cartel activities in the other markets.
Under the “Amnesty Plus” program, the US Department of Justice (DOJ) offers a cartel member a penalty discount for disclosing previously undetected cartels that are different from the one that first brought that cartelist to the DOJ’s attention.
See also Marx et al. (2015), especially footnote 10.
The DOJ maintains strict confidentiality regarding the schedule of its cartel investigation. Although it is possible to find data or make inferences in some cases, more commonly the starting date of an investigation is unknowable from publicly available data.
These cartels are operated by trade associations or shipping conferences that are the addressees of the EC decisions. The identity of the individual firms is usually undisclosed in the published decisions.
For example, Degussa AG joined the Hydrogen Peroxide and Perborate cartel in January 1994 and ended its collusion in the cartel in December 2000. FMC Corporation joined the cartel in May 1997 and stopped collusion in December 1999.
Alternative approaches to handle linked failure times in continuous-time hazard frameworks have been developed in the literature. See, e.g., Breslow (1974). While computationally undemanding, Breslow’s method will be inaccurate if there are many links in the data set, which happens to be my case.
I obtain similar results from probit models.
Formally, if we define the risk set in month m, \(R_m\), as the number of spells that do not experience an event by the start of month m, and the number of events in month m as \(S_m\), the Kaplan–Meier empirical hazard is defined as \(S_m/R_m\).
To my best effort, I am not able to find information of the dates of the DOJ’s investigation initiation for the majority of cartels that affected the American markets.
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Acknowledgments
I thank Eric van Damme, Adriaan Dierx, Dennis Gaertner, Martin Holterman, Fabienne Ilzkovitz, Mario Mariniello, Leslie M. Marx, and Guntram B. Wolf for helpful conversations. I am indebted to Robert Feinberg and Lawrence J. White (the editors) for many perceptive comments and suggestions. Part of this work was carried out while the author was visiting Bruegel, whose hospitality is gratefully acknowledged. Any mistakes are my own.
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Zhou, J. The Rise and Fall of Cartels with Multi-market Colluders. Rev Ind Organ 48, 381–403 (2016). https://doi.org/10.1007/s11151-016-9509-0
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DOI: https://doi.org/10.1007/s11151-016-9509-0