## Abstract

Standard methods for calculating cartel-damage rely on data of prices charged and quantity sold. Such data may not easily be available. In this paper, it is shown that accounting data can be used for computing a lower bound for cartel-damage. Previous literature indicates that economic profits can hardly be inferred from accounting data. Therefore, it is shown under which econometrically testable assumptions on accounting costs a meaningful lower bound for cartel-damage can consistently be estimated when using accounting data. However, the aggregation-level and the publication-frequency of accounting data pose a challenge to the estimation of cartel-damage. A further challenge is to appropriately reflect the strength respectively effectiveness of the collusive agreement in the specification of any such estimation.

This is a preview of subscription content, access via your institution.

## Notes

Case C-453/99,

*Courage Ltd v Bernard Crehan and Bernard Crehan v Courage Ltd and Others*, Judgment of the Court of 20 September 2001.Please note that the analysis provided in this paper requires goods in segment

*i*to be homogenous, such as cement or vitamins. The analysis of cartel damages is complex when goods in the same segment*i*are differentiated. Then, a uniform price*p*_{ i }does not exist. This is the case for e.g. insurances.It is left to further research to examine how marginal costs varying with output and fixed costs varying over time affect damage calculations.

Note that the competitive level of prices does not necessarily refer to the perfectly competitive market outcome, i.e. marginal cost-pricing.

*p*_{ Cj }can be above marginal costs as is, e.g., the case in standard models of oligopolistic (for example Cournot-) competition. Moreover, the elevated cartel-price need not always guarantee the cartelists positive economic profits. To see this, consider the case of an industry where firms produce with significant fixed costs and face a declining demand. Because of the lower demand competition may drive prices below average total costs. Collusion can be a means to avoid this loss.Other types of competition law violations are “price-fixing or market-dividing cartel agreements, […] or exclusionary practices, such as tying and bundling or predatory pricing by a dominant firm” (van Dijk and Verboven 2008).

A discussion and evaluation of the accounting data needed is given in Sect. 2.2.

Grout and Zalewska (2008, p. 160) elaborate further on the effect of likely measurement errors of cost of capital measures for analyses of competition. In particular, they find that comparisons of cost of capital estimates to estimated rates of return may lead to distorted conclusions.

This information is inferred from the Annual Report 2010 of HeidelbergCement. This report is downloadable at http://www.heidelbergcement.com/NR/rdonlyres/0AFACA2F-2EE0-4DB0-8171-3DCAC1491AD6/0/AnnualReport_2010_web_17032011.pdf.

An example for an agreement of this type may constitute the agreement of German consumer goods producers who exchanged information about negotiations with important retailers and about intended price changes (for more information see the press release of the German federal cartel office at http://www.bundeskartellamt.de/wEnglisch/News/press/2011_03_17.php).

For Daiichi data is used for the period 1985–2005 since in 2006 it merged with Sankyo. Data of the merged firm is not easily comparable to data of Daiichi alone.

This regression is performed with heterscedasticity-consistent standard errors because a White-test indicates heteroscedasticity, while a Jarque–Bera test implies normally distributed error terms.

## References

Bartholdy, J., & Peare, P. (2005). Estimation of expected return: CAPM vs. Fama and French.

*International Review of Financial Analysis,**14*(4), 407–427.Brandner, J. A., & Ross, T. W. (2006). Estimating damages from price-fixing.

*Canadian Class Action Review,**3*(1), 335–369.Bruner, R. F., Eades, K. M., Harris, R. S., & Higgins, R. C. (1998). Best practices in estimating the cost of capital: Survey and synthesis.

*Financial Practice and Education, Spring/Summer,**1998*, 13–28.Clarke, J. L., & Evenett, S. J. (2003). The deterrent effects of national anticartel laws: Evidence from the international vitamins cartel.

*The Antitrust Bulletin,**48*(Fall 2003), 689–726.Connor, J. M. (2001).

*Global price fixing–our customers are the enemy*. Boston: Kluwer Academic Publishers.Connor, J. M. (2008).

*Global price fixing*(2nd ed.). Berlin: Springer Verlag.European Commission. (2008). Commission staff working paper accompanying the white paper on damages actions for breach of EC antitrust rules. SEC (2008) 404. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2008:0404:FIN:EN:PDF.

First, H. (2001). The vitamins case: Cartel prosecutions and the coming of international competition law.

*Antitrust Law Journal,**68*, 711–734.Fisher, F. M. (1984). The misuse of accounting rates of return: Reply.

*The American Economic Review,**74*(3), 509–517.Fisher, F. M. (2006). Economic analysis and antitrust damages.

*World Competition,**29*(3), 383–394.Fisher, F. M., & McGowan, J. J. (1983). On the misuse of accounting rates of return to infer monopoly profits.

*The American Economic Review,**73*(1), 82–97.Fisher, F. M., & Romaine, R. C. (1990). Janis Joplin’s yearbook and the theory of damages.

*Journal of Accounting, Auditing and Finance,**5*(1/2—Winter/Spring 1990), 145–157.Goldberg, J., & Gustafson, D. (2010). Obtaining evidence. In A. A. Foer & J. W. Cuneo (Eds.),

*The international handbook on private enforcement of competition law*. Northampton, MA: Edward Elgar Publishing Limited.Green, E. J., & Porter, R. H. (1984). Noncooperative collusion under imperfect price information.

*Econometrica,**52*(1), 87–100.Grout, P. A., & Zalewska, A. (2008). Measuring the rate of return for competition law.

*Journal of Competition Law and Economics,**4*(1), 155–176.Hall, R. E., & Lazear, V. A. (2000). Reference guide on estimation of economic losses in damages awards. In: Manual on scientific evidence (2nd ed.). Federal Judicial Center Reference, 277–332. http://www.fjc.gov/public/pdf.nsf/lookup/sciman00.pdf/$file/sciman00.pdf.

Haltiwanger, J., & Harrington, J. E. (1991). The impact of cyclical demand movements on collusive behavior.

*RAND Journal of Economics,**22*(1), 89–106.Hostettler, S. (1997).

*Economic Valued Added (EVA) Darstellung und Anwendung auf Schweizer Aktiengesellschaften*(5th ed.). Bern: Verlag Paul Haupt.Hotelling, H. (1925). A general mathematical theory of depreciation.

*Journal of the American Statistical Association,**20*(151), 340–353.International Accounting Standards Board (IASB). (2008).

*IFRS 2008*. Weinheim: Wiley-VCH Verlag.Landes, W. M. (1983). Optimal sanctions for antitrust violations.

*The University of Chicago Law Review,**50*(2), 652–678.Long, W. F., & Ravenscraft, D. J. (1984). The misuse of accounting rates of return: Comment.

*The American Economic Review,**74*(3), 494–500.Marshall, R. C., Marx, L. M., & Raiff, M. E. (2008). Cartel price announcements: The vitamins industry.

*International Journal of Industrial Organization,**26*(3), 762–802.Oxera. (2009). Quantifying antitrust damages. Towards non-binding guidance for courts. Study prepared for the European Commission. http://ec.europa.eu/competition/antitrust/actionsdamages/quantification_study.pdf.

Paha, J. (2011). Empirical methods in the analysis of collusion. Forthcoming: Empirica, 1–27. http://www.springerlink.com/content/7wmp935j8624474q/fulltext.pdf.

Peasnell, K. V. (1996). Using accounting data to measure the economic performance of firms.

*Journal of Accounting and Public Policy,**15*(4), 291–303.Rotemberg, J. J., & Saloner, G. (1986). A supergame-theoretic model of price wars during booms.

*The American Economic Review,**76*(3), 390–407.Stark, A. W. (2004). Estimating economic performance from accounting data–a review and synthesis.

*The British Accounting Review,**36*(4), 321–343.van Dijk, T., & Verboven, F. (2008). Quantification of damages. In W. Dale Collins (Ed.),

*Issues in competition law and policy vol. III*(pp. 2331–2348). Antitrust: ABA Publications.

## Acknowledgment

I would like to thank Georg Götz, Peter Winker, an anonymous referee, and my colleagues for their comments. Moreover, I feel grateful for the valuable comments that were provided by the discussants and audiences at several workshops and conferences in Giessen and Mannheim.

## Author information

### Authors and Affiliations

### Corresponding author

## Appendix

### Appendix

### A dataset for the vitamins cartel

In this section the above method for quantifying the cartel-induced damage is applied to the vitamins cartel. Connor (2001, p. 277) considers the vitamins cartel to be “the first, the biggest, most elaborate, most complete, longest lasting, and most influential of the cartel pandemics of the 1990s. [… Moreover, the vitamins cartel] extracted historically unprecedented monopoly overcharges from customers. These overcharges later appeared on the profit statements of the vitamin manufacturers.” First, I briefly describe the vitamins cartel and the data used. Second, I show how such data would be used to calculate a lower bound for damages. In this context, I demonstrate some of the difficulties associated with such calculations as described in Sect. 3 above.

Between 1989 and 1999 several European, Japanese, and North-American producers of vitamins colluded in prices.^{Footnote 12} Among these firms were BASF, Roche, Daiichi, and Takeda that agreed to jointly raise prices of the vitamins shown in Table 1. This table provides some basic information on the structure of these cartels. The supposed ringleader (i.e. Hofmann-La Roche in five cases) is highlighted in bold. The intensity of the collusive agreement varied over both the affected vitamins and over time, as the prices were raised gradually rather than bouncing up immediately after the formation of the cartel. After the discovery of the cartel, prices dropped by about 50% within 2 months leaving prices 30–40% below the pre-cartel level. These price movements are not uniform across all cartelized vitamins. For example, prices for liquid vitamin A almost tripled during the collusive phase while those for vitamins B1, B6, folic acid, C, and B2 were raised less strongly (Connor 2001, p. 322).

Yearly firm-level data of the above firms’ revenues and costs of goods sold is collected from the ThomsonReuters Datastream database for the period 1985–2007.^{Footnote 13} These variables are measured in thousands of each national currency, i.e. Euro for BASF, Swiss Francs for Roche, and Yen for Daiichi and Takeda. Figure 2 provides an overview on the development of these four firms’ revenue and costs of goods sold as well as the difference and ratio of these two variables.

### Quantifying cartel damages in the vitamins cartel

In Sect. 2.1, I show that a lower bound for cartel-damages can in principle be found as the regression-coefficient of a cartel-dummy in a regression of the difference between a firm’s revenue minus its costs of goods sold on possibly explanatory variables. Such a regression is performed for BASF, Roche, Daiichi, and Takeda using the above publicly available data. The aim of this exercise is to show some of the difficulties that are outlined in Sect. 3 and may arise when performing regressions of this type.

A first difficulty consists in the definition of the cartel-dummy. Note that only one cartel-dummy can be used for BASF’s and Roche’s participation in the cartels of vitamins B5 and beta-carotene as both cartels occurred at the same time. As is standard, all dummies take a value of zero when a cartel is inactive and a value of one when the cartel is in force. Such a dummy does not reflect variations in the strength of a collusive agreement that lead to the price movements as described above. As a second difficulty, one should note that the use of yearly data keeps time-series short (23 respectively 21 observations). Therefore, one must be parsimonious in specifying the set of further explanatory variables *X*. These variables and the estimation methodology are presented in turn.

I start with describing the regression methodology for BASF and present the regressions for the other firms further below. For BASF the set of regressors *X* comprises an IFRS-dummy that covers the effects of BASF switching from German-GAAP to IFRS in 2004. Since the US-market for vitamins is quite important, the US-$/Euro exchange rate is also included in *X*. A further variable is incoming orders (*IO*
_{
t
}) in the chemical industry. This value index takes a base value of 100 in year 2000 and is provided on a monthly basis by the German Statistical Office (Destatis). A third difficulty arises because this value index applies to the reunified Germany so that the time series only starts in 1991. The missing values for the period 1985–1990 are backcasted on basis of a regression of *IO*
_{
t
} on *IOw*85_{
t
}, which is a monthly value index for incoming orders in the West German chemical industry with base year 1985 (*IOw85*
_{
t
}) and observations ending in 1994.

A fourth difficulty may arise because of statistical problems or an inadequate specification of the regression. In this context, estimating the damage-regression (9) by standard OLS^{Footnote 14} does not provide informative results. None of the coefficients is statistically significant except for the one associated with incoming orders (*p*-value 0.01%). The Durbin-Watson statistic indicates positive, first-order autocorrelation in the error term which implies misspecification of the model. An Augmented Dickey-Fuller test indicates that both, the dependent variable and incoming orders, are non-stationary, which potentially leads to a spurious correlation problem. Such a problem can, for example, be overcome by estimating an error correction model or by performing a regression in first differences. In this specific case, it turns out that estimating an error correction model does not provide additional information in comparison to a regression in first differences (see Eq. (16)). Therefore, the latter is used to draw further inferences.

The adjusted R² of this regression amounts to 53% with *a*, *γ*, and *δ*
_{
1
} being significant at the 1%-level and taking the expected signs (*γ* < 0 and *δ*
_{
1
} > 0). The *β*-coefficients for the cartels in vitamins B4, C, and B2 are insignificant, which is reasonable since these vitamins were subject to competition. The *β*-coefficients for the cartels in vitamins A, E, beta-carotene and B5 are significant at the 1%- respectively 10%-level. These *β*’s are negative which is counterintuitive. Therefore, in light of the above difficulties a lower bound for cartel damages cannot be quantified. A similar picture can be seen in the regressions for Roche.

A fifth difficulty is imposed by the existence of structural breaks. In case of Takeda it is found that *π* kinks upwards in 1999 such that a simple linear trend rises too strongly prior to 1999 and too softly afterwards. As a consequence, a second linear trend is included starting in 1999. Since for the Japanese chemical industry no data of orders or sales was available the only additional variable is the YEN/USD exchange rate. Therefore, a sixth difficulty arises because of the lack of data. As a consequence, the estimated coefficients of the cartel-dummies do not provide good estimates of the damage caused by Takeda because the high value of R² is mainly due to the inclusion of the trend terms and the constant. A similar result applies to the regression for Daiichi.

Table 2 reports summary statistics for the above regressions, i.e. the adjusted coefficient of determination, the p-values of F-tests examining the hypotheses that (a) all coefficients or (b) all cartel-coefficients jointly equal zero, the p-value of the Jarque–Bera test for normally distributed error terms, and the Durbin-Watson statistics. The latter’s values indicate misspecification for at least BASF and Daiichi. However, the effects of autocorrelation must be traded off with parameter-parsimony that is required by the limited number of observation per series.

## Rights and permissions

## About this article

### Cite this article

Paha, J. Using accounting data in cartel damage calculations: blessing or menace?.
*Eur J Law Econ* **34**, 241–263 (2012). https://doi.org/10.1007/s10657-011-9253-8

Published:

Issue Date:

DOI: https://doi.org/10.1007/s10657-011-9253-8

### Keywords

- Cartel damages
- Accounting data
- Collusion
- Cartel prosecution

### JEL Classifiacation

- D43
- L12
- L13
- L41