Collusion screening in the Indian pharmaceutical market

A means of detecting colluding behaviour by firms using basic and widely available economic data would be an extremely useful tool within the field of economics. In spite of several proposed methods and theories in economic literature cartels are –in practiceonly taken down when either it fails internally, leading to one of its members informing authorities or as the result of journalistic investigation. In this paper we make an attempt at using the theories developed by Marshall and Marx (2012) in order to screen the Indian pharmaceutical market for possible colluding behaviour. We focus on a particular group of medicines that lends itself to analysis due to their partial governmental control and choose one medicine for a detailed case study. Although no definitive proof of collusion is obtained from the analysis we do come across a number of abnormalities across the market that we believe make it hard to accept the absence of collusion and/or unethical practices in general as a truthful fact. We present some possibilities for future case studies in light of the same topic. Introduction Cartels can have a very large influence on a market. Just this year, European based truck manufacturers have agreed to pay a collective 2.93 billion euros in fines. These fines were imposed by the European commission after word of the cartel had come out. The cartel in question is said to have affected the sales of nine out of every ten trucks in the EU market over a period of fourteen years. Before that there was the LIBOR scandal, where bankers reported false interbank interest rates over a long period (as early as the 90’s by some sources) in order for banks to profit on their portfolios, billions of dollars’ worth of fines followed. Finally, the EU antitrust commission imposed 1.47 billion euros in fines on members of a CRT cartel (cathoderay-tubes), another worldwide cartel that involved the price fixing of important components of TV screens and computer monitors between 1996 and 2006. These are just three cases from the last five years in which illegal collusion has been detected and proven, the true extent of cartel formations is unknown. The height of the fines reflect the negative impact that cartels have on the market, being able to detect cartels is therefore a bit of a ‘golden goose’ for antitrust commissions. Finding them appears to be troublesome though, of the three examples listed above, two were ‘detected’ because one or more of the cartel members got cold feet and reported the cartel to authorities in hopes of escaping prosecution and gaining immunity from fines. Only the LIBOR scandal featured an empirical research paper by Snider and Youle (2010), two economics professors who showed that the posted LIBOR rates were inconsistent with observable costs and were clustered around certain points when they shouldn’t have been. Even 1 http://www.seattletimes. com/business/truckmakers-includng-paccar-get-record-324-billion-eu-finefor-price-fixing/ ; http://www.bloomberg. com/news/articles/2016-07-19/truckmakers-fined-record-324-billion-by-eu-for-price-fixing 2 https://en.wikipedia. org/wiki/Libor_scandal#Fines_for_manipulation 3 http://europa.eu/rapid/press-release_IP-12-1317_nl. htm this cartel however was initially brought under the radar, not by the examination of economic evidence and data, but by an article in the Wall Street Journal in 2008. The use of financial data to detect signs of collusion is –unfortunatelynot something that is being worked on a lot. Given the enormity of the impact that large cartels can have on a market, as is highlighted by the large fines cartel members tend to face, we think that this field should receive more attention. This paper will start with an overview of theory regarding the detection, nature and effects of collusion, followed by a historical overview of the Indian pharmaceutical market. This should give the reader enough insight into the theoretical groundwork of our research as well as explaining the features of the Indian market that make it particularly interesting for studying. After the theoretical overview we will present our dataset, followed by the development of our hypotheses in the Method section, which also explains our testing procedure in detail. After this, we present the results when applying the method to the entire dataset, followed by our case study into Isosorbide-5-mononitrate, a drug used to lower the blood pressure which stands out from the rest of the dataset and was chosen to be examined in great detail (more on this in the relevant section). Lastly we turn to summary results of the full set of medicines in our dataset, and provide possible avenues for future research before turning to discuss the meaning of the results in the