Rosa Abrantes-Metz, March 21, 2012
Price-fixing, bid-rigging and manipulation cases exist in varied markets and multiple jurisdictions.Â Recent examples include: price-fixing in gasoline retail stations in Canada and Brazil; bid-rigging in pharmaceutical markets in Mexico; bid-rigging in municipal bonds in the United States; stock options backdating and springloading in the United States; and of course, the recent alleged LIBOR conspiracy and manipulation with ongoing investigations worldwide.
More often than not, conspiracy and manipulation cases are detected through whistle blower programs.Â Over the last few years, other detection tools based on empirical analyses have started to gain traction.Â These tools, commonly known as screens, have flagged various potentially illegal behavior worldwide.Â The most recent and largest example is the LIBOR.Â I and co-authors flagged the possibility of anticompetitive behavior in the LIBOR in August 2008, following two Wall Street Journal articles and long before both investigations were made public in March 2011 and the filling for leniency by UBS with the US Department of Justice later in the spring of 2011.
Other recent episodes of such screening successes include the uncovering of cartels in Mexico and Brazil (Antitrust Chronicle, CPI, March 2012) while, previously in financial markets, screens were successfully used to detect stock options backdating and springloading as well as to flag an alleged conspiracy among NASDAQ dealers.
In the attached article, I and co-author Albert Metz present new evidence from the Libor setting and explain how screens can be used to assist in distinguishing explicit (illegal) collusion from tacit (legal) collusion.Â In particular, this article focuses on the time period in which banks’ quotes became identical to each other over a year-long period in which the level of the Libor was also essentially constant.Â We ask ourselves whether such equality in quotes could have reasonably been achieved in a competitive setting. We point to sudden changes in banks’ daily quotes and explain why these patterns are more likely the outcome of an explicit agreement among participating banks rather than of strategic or learned behavior, or even non-cooperative decisions by the banks.