The Future of Cartel Deterrence and Detection

Abstract

Over the last few decades, leniency programs recorded a successful history of identifying and dismantling cartels. The essential idea is simple, that authorities will reward a cartel member who self-reports. It is instructive to consider why authorities have relied so heavily on leniency in the past. First, it is not particularly resource intensive to implement. It doesn’t require collecting large amounts of data and employing economists and data analysts to sift through the haystack in hopes of finding the occasional needle. Second, almost by definition leniency is likely to have a high success rate of prosecution, among those applications selected to be fully investigated. It is noteworthy though that authorities are reluctant to produce statistics on the overall efficacy of their leniency programs. We should have a better idea of how many leniency applications are reviewed and investigated (among all those filed), and of those, how many lead nowhere. But we are not privy to such valuable information. Can authorities continue to almost exclusively depend on leniency programs going forward? There is no other area of criminal investigation which essentially waits for the guilty to confess as its key detection tool, yet the advantages of resource and success of such “passive detection” would equally apply to robbery or homicide: it doesn’t cost much to wait for a confession, and if someone confesses, the case will almost certainly be closed successfully. But while the police surveil neighborhoods to monitor possible illegal conduct, ready to not only detect ongoing conduct but also hopefully to deter such conduct from even getting started, several competition authorities still tend to lack the proactive nature of detection and deterrence through screening or market monitoring, relying (almost or entirely) on leniency programs. Hence, cartel detection appears, at least at first, to be a uniquely passive area of law enforcement. In this short paper we explore the role of leniency programs in the next generation of cartel detection, and ultimately deterrence. Will it continue to be the dominant source of cartel detection, or will advances in data collection and analysis – so-called “big data” and “machine learning” – reduce the cost and increase the effectiveness of screening and artificial intelligence techniques? Will traditional leniency and whistleblower programs even remain effective in a future which may keep no “paper trail” of communications proving the necessary intent? Have we learned the lessons from extensive rigging in financial markets? Do we need to revisit the entire detection approach?