This article, available here, tries to quantify the deterrent power of fines imposed by the Spanish competition authority from 2011 to 2015. Despite being authored by senior staff at the Spanish competition authority, the paper concludes that most of the fines imposed by the Spanish competition authority during this period were under deterrent.

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The argument is structured as follows:

Section II sets out how to quantify cartel gains.

A deterrent optimal fine can be defined as a fine that deters a company from participating in a cartel. Such an outcome is achieved when there is no expected net gain from participating in the cartel in the first place, i.e. when the expected illicit gain of entering into a cartel is lower than the expected loss from being sanctioned for cartel participation. Therefore, the reference value for an optimal fine should be determined by reference to an estimate of the illicit gain (also known as excess profit) flowing from cartel membership. This illicit gain is a percentage of the total turnover in the affected market, which depends on three parameters: the competitive mark-up, the cartel overcharge and the price elasticity of demand in absolute value.

The competitive mark-up is the percentage increase in market price over marginal costs, an amount that is added to these costs for reasons other than collusion. The increase in price due to the existence of a cartel is the percentage by which cartelisation increased the price by comparison to the competitive price. This is often difficult to estimate because there is usually no information on the competitive situation in the absence of the cartel (the counterfactual or ‘but-for’ scenario). Finally, the price elasticity of demand measures the consumer reaction to a change in the price of the good or service. As the price elasticity of demand is negative for a normal good, the quantity demanded will fall when price increases, so that the elasticity value in a market significantly affects the illicit profit made by firms that are part of a cartel.

If we determine the value of the three parameters discussed in this section, an estimate of the illicit gain of cartelists can be obtained – and using estimates available in the literature, this can vary between 3% and 21% of the turnover in the affected market.

Section III discusses how to set an optimally deterrent fine.

A deterrent fine is one that, from the point of view of firms, makes the formation or continuation of a cartel unprofitable. This occurs when the expected illicit gain of entering a cartel is lower than the expected loss. In order to calculate the expected gain and loss, and in addition to the elements discussed in the previous section, one needs to add the probability of detection to the estimated net gain.

It is hard to determine what the probability of detection is in practice.  While the number of cartels detected is available, it is unknown how many are still operating in the market. Therefore, the authors use a probability of detection that reflects the most reliable empirical estimates produced so far – and conclude that it seems prudent to accept that the annual probability of detection of an anticompetitive conduct oscillates around 15%, and it should not exceed 20%.

For the purposes of calculating an optimally deterrent fine, the authors relate the probability of detection to the incentives a company may have to break the collusive agreement. It is assumed that the profit of not complying with a cartel is at least slightly greater than the profit of complying with the cartel, as the non-compliant company will be able to set a price below that of the cartel, thus increasing its sales at the expense of its cartelised competitors. From this framework, the authors derive expected net gain for a firm joining a cartel. Two possible deviations from that strategy are then considered: not joining the cartel at any time, or initially joining but breaking the cartel afterwards. Having in mind that the strategy to withdraw from the cartel is slightly more beneficial for the company than complying with the cartel, the authors derive a deterrent optimal fine from a dynamic perspective (dynamic deterrent fine, DDF). A fine equal to or higher than DDF would make at least one firm break the cartel agreement, leading the others to then choose to compete – terminating the cartel. According to this approach, a fine will be dissuasive if it is at least equal to the expected excess or illicit profit multiplied by a factor equal to the inverse of the probability of detection.

Section IV looks at the fines imposed in Spain between 2011 and 2015.

Between 2011 and 2015, the Spanish Competition Authority imposed fines of around EUR 1.3 million on nearly 780 companies. The average fine is slightly below EUR 1.7 million, with the minimum fine varying from around EUR 200 to almost EUR 4,700, while the maximum fine ranges from EUR 270,424 to over EUR 36.1 million. The standard deviation, therefore, is quite high.

Further, several court rulings changed the method for setting antitrust fines in Spain during this period. Until 2013, fines frequently exceeded the upper limit of 10% of total sales of the preceding year, so the fine applied was the legal maximum – 10% of total sales. After a judgement this year, the average values of the fines fell sharply. Since the Supreme Court’s judgment of January 2015, the fining rates are even lower,

The authors compiled a database which contains all infringements of Article 1 (anticompetitive agreements) of the Spanish Competition Act from January 2011 through December 2015, amounting to 95 cases. They divided the cases in three periods, according to the date in which a decision was reached: (1) decisions from January 2011 to October 2013, when a new institutional framework for competition enforcement was implemented; (2) from 1 October 2013 through 29 January 2015, when an important the Supreme Court’s judgment on fines was issued; and (3) from 30 January 2015 onwards, when a new fining methodology was developed according to the new Supreme Court’s jurisprudence.

The average fining rate during period (1) accounted for 4% of the total volume of sales of the firms involved; in period (2), the average fining rate was slightly lower but the median value increased; and in period (3) both the average and median fining rates are significantly lower than in the preceding periods. A second ratio measures fines against total sales in the relevant market. During period (1), the fine imposed to each company was on average 5.21% of the volume of sales of each firm in the affected market during the infringement period; during periods (2) and (3) this diminishes to 3.7%, with some significant outliers.

Section V finally looks at whether the fines imposed by the Spanish competition authority are deterrent.

To do this, they compare the Spanish fines reviewed in the last section with the methodology outlined earlier in the paper. Since very few cases contain information about the three parameters needed to estimate the margin of illicit gain, the authors rely on the estimates of the literature discussed in section two in order to obtain the most probable range of the margin of illicit gain for each firm, as well as an average value.

They develop three scenarios depending on this range: a lower scenario, an average scenario and a higher scenario. The illicit gain by infringing firms is estimated to be between 3% (lower scenario) and 23% (higher scenario) of the affected market turnover. This leads to an average scenario where the average illicit gain is 13%. The authors then use the same approach to determine the probability of detection. Starting from an annual probability of detection of 15%, they calculate the maximum global probability of detection in each case – e.g. for  example, a cartel lasting ten years would face a global probability of detection slightly over 80%, while an infringement that lasted fifteen years would face a global probability of detection above 91%.

The authors then determine a deterrence ratio, i.e. the ratio between fines actually imposed and the optimally deterrent fine in each case. They find that under the lower scenario (i.e. with the lowest expected amount of illicit gain, 3%), the fines imposed by the Spanish competition authority from January 2011 through December 2015 have been on average 36% below the optimal deterrent fine. More significantly, for nearly 80% of the companies the fines imposed were on average 64% below the optimal deterrent fine. In the average scenario (i.e. 13% expected illicit gain), the level of under-deterrence is significantly higher (as one would expect). The fines imposed to this group of companies were on average 86% below the optimal deterrent fine, a number so large that the deterrence ratio varies very little between periods (between 12% and 17% per cent) despite substantial reductions in fines (by comparison to company size) during the reference period. In the higher scenario, fines were on average 92% below the optimal level, and the great majority of the fines did not even reach 20% of the mean value of the optimal deterrent fine.

Conclusion

The results of this study are striking – but need to be taken with care. Personally, I did not find them that surprising. One needs to remember that the fining thresholds were originally (and broadly speaking) based on the idea that: (i) overcharges were around 10% of the competitive price; (ii) the odds of detection were about one in two. Given recent studies that indicate much lower rates of detection, and consistently higher overcharges, it is a common refrain in the literature that fines are too low to be optimally deterrent. This is naturally subject to qualifications regarding the accuracy of the available numbers – something on which colleagues of mine have been working –, the need for additional work, and the lack of data regarding the reputational impact and private claims following-on infringement decisions.

Nonetheless, in the US it is taken as a given that penalties other than fines are required to effectively deter cartels. An interesting question is why the rest of the world – e.g. Europe – have failed to draw the logical conclusions flowing from this body of academic research. I personally expect this to be an area where we may see policy developments in coming years.

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