Below is a guest post by Ai Deng, PhD of BatesWhite Economic Consulting on empirical screens to detect cartel activity. Ai is an expert in this area and I think it will be one of growing importance. In 2015 the Antitrust Division, for the first time, gave credit in plea agreements for what they call a “forward looking” robust compliance program. The context was companies that were caught in collusive activities but as part of their internal investigation and cooperation with the Division, they made substantial upgrades in their compliance program to change the company culture. Granting any credit for a compliance program was a big step for the Antitrust Division and one welcomed by the compliance community. But, it is logically inconsistent to reward a company for upgrading their compliance program when the get caught in a violation, but not crediting a firm which already had a strong compliance and ethics program, despite the fact that there may have been a violation. Perhaps 2016 will see further movement by the Division in crediting compliance programs.
But regardless of the Division’s developing position on compliance/ethics programs, empirical screens to detect collusion can enable a company to put a stop to any collusion detected, and perhaps approach the Antitrust Division and/or other jurisdictions for leniency. Here is Mr. Deng’s post:
Several readers expressed interest in my recent Law360 article on the use of analytics in antitrust compliance (here). Some also asked about the longer working paper which is the basis of the Law360 article. I am very happy to report that the working paper version of the article titled “Cartel Detection and Monitoring: A Look Forward” can now be downloaded here. In this article, I address several topics summarized in the abstract as follows:
“There is a growing literature in industrial organization on the use of empirical screens to detect cartels. I discuss several methodological issues that have emerged from this literature, and explain why addressing these issues is important for gaining a better understanding of the power and limitations of empirical screens and for extending the retrospective application of empirical screens to dynamic, real-time monitoring of the market. I then compare the treatment of empirical screens in the IO literature with the treatment of related techniques in the literatures of macroeconomics, financial market manipulation, and statistical fraud detection. I highlight the intersections and discuss lessons and experiences that are helpful to the design and use of empirical screens for both screening and monitoring. Future research topics are also suggested.”
Any comments/thoughts/suggestions are welcome.
Ai Deng, PhD
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