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Price Fixing Hits Home: An Empirical Study of US Price-Fixing Conspiracies

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Abstract

This paper analyzes a sample of Section 1, Sherman Act price fixing cases brought by the US Department of Justice between 1961 and 2013. Over 500 cartels were prosecuted during this period. The determinants of cartel formation and cartel breakup are estimated, including analysis of the impact of the discount rate, business cycles, and antitrust policy. We find that cartels are more likely to break up during periods of high real interest rates, presumably because higher interest rates are associated with greater impatience. The adoption of a stronger amnesty policy has no significant impact on cartel breakup over this period, although the results suggest some association with lower cartel formation rates.

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Notes

  1. Baer and Hosko (2013, p. 2).

  2. We follow Tirole’s (1988, pp. 245–253) representation of the classic cartel dilemma (Stigler 1964).

  3. This policy provides for full amnesty from criminal prosecution for the first member of a cartel to report a previously unknown cartel to the Justice Department, encouraging a “race to the courthouse door” among cartel members. See Department of Justice, Antitrust Division, Leniency Policies http://www.justice.gov/atr/public/criminal/leniency.htm for the current corporate and individual amnesty policies of the DOJ. See Hammond (2005) for a discussion of these policies and their impact on antitrust enforcement.

  4. Spagnolo (2000, 2007) shows that the impact of leniency is less obvious than the treatment here suggests. In particular, partial amnesty can increase the set of collusive equilibria and the potential collusive profits that are available to a cartel, which makes collusion easier and therefore presumably more durable. Aubert et al. (2006) discuss the relationship between the design of leniency programs and their impact on both cartel stability and firm performance. Miller (2009) discusses strategic responses to leniency. Harrington and Chang (2009) discuss the impact of leniency on both breakup and formation.

  5. For early contributions to this literature, see, for example, Roth and Murnighan (1978), Murnighan and Roth (1983), and Holt (1985).

  6. There is a literature that focuses on the role of leaders (e.g., Saudi Arabia or US Steel) in facilitating collusion and another strand that focuses on “maverick” firms as impediments to collusion (Baker 1989, 2002). Both of these strands presume that there is heterogeneity that affects collusive stability.

  7. See Levenstein and Suslow (2011, p. 478) for an overview of empirical examinations of the impact of demand fluctuations on cartel breakup. Experimental analysis in Feinberg and Snyder (2002) finds that observable demand downturns have little impact on the ability to sustain collusion, while unobservable negative shocks undermine collusion.

  8. Part of the reason this remains a puzzle is that the theoretical literature does not generally address the question of when an industry would need to communicate explicitly in order to maintain a collusive equilibrium. Instead it asks whether collusion, either tacit or explicit, is an equilibrium. Our focus is on explicit collusive agreements.

  9. Alexander (1994) and Krepps (1997) examine the impact of changes in antitrust policy on cartel formation under the National Industrial Recovery Act. Levenstein and Suslow (2006a, p. 67) summarize: “Many studies report that a cartel was formed during a period of falling prices, but this is not always, or even usually, associated with falling demand (either for the particular product or in the general economy). Instead, falling prices were often the result of entry or the integration of previously distinct markets.” See also, Levenstein and Suslow (2011).

  10. Most prosecuted cartels have more than one CCH record because there are often separate convictions for different firms as well as individual defendants. We have consolidated these into cartel-level observations.

  11. We are extremely grateful to Bryant and Eckard for their willingness to share their data with us. Note that they treat no contest pleas as guilty pleas (Bryant and Eckard 1991, p. 532).

  12. The Byrant and Eckard sample excluded bid-rigging cases. We have gone back to 1961 to collect CCH records on bid rigging cases, as described below. We have also added additional cases from that period that had been appealed and therefore were not included in their original sample.

  13. As we have written previously about studies using these and similar samples, “these estimates … must be treated with caution, as they are not based on random samples. To the contrary, because cartels are often secretive and even illegal, sample selection reflects the legal regime in which the cartels operated. In some cases, samples are literally selected by prosecutors; in such cases we simply do not know whether cartels that run afoul of legal authorities are similar to or different from cartels that manage to escape unnoticed” (Levenstein and Suslow 2006a, p. 44). The same caution applies to the analysis in this paper.

  14. Our data set is, at least in principle, inclusive of all of the observations in Miller (2009).

  15. There are cases of earlier international cartel prosecutions (potash, shipping), but they are rare and mostly pre-date World War II.

  16. Other cartel datasets do include cause of cartel breakup. See, for example, Levenstein and Suslow (2011).

  17. Levenstein and Suslow (2006a, p. 60), “Another explanation for the prevalence of cartels in unconcentrated industries is the role played by trade associations. In the case studies and in contemporary international cartels, industry associations were involved whenever the number of cartel participants was large.”

  18. See Levenstein and Suslow (2006b, pp. 26–27), for discussion of the role of industry associations in US and European cartels over the last century.

  19. Calculated from data provide by Bryant and Eckard (1991).

  20. Kaplan–Meier curves are commonly used in demographic and bio-medical studies to display the probability of an event (e.g., death) in a population. See Rich et al. (2010).

  21. The first cartel in the full sample was formed in 1932, but was dropped for missing information.

  22. Cartels may also be discovered for non-economic reasons, such as when there is a whistleblower.

  23. We use these two measures in our previous study of the determinants of international cartel breakup (Levenstein and Suslow 2011) and find that they have a significant impact.

  24. Note that this differs somewhat from the measure used in Levenstein and Suslow (2011). Most of the firms in that study were publicly traded, so it was possible to obtain financial information for the specific firms in the cartel. That was not possible for this sample, so instead we rely on data for firms in the industry.

  25. The minimum value for this variable belongs to "Moving and Storage of Household Goods" (NAICS 4931) when some firm had negative cash equal 261.6 times its required interest payments. Presumably this reflects both large losses and small required interest payments. This one observation is clearly an outlier in the entire sample, as it is three times larger (in absolute value) than the closest observation in the sample.

  26. Recession years are defined by the NBER business cycle reference dates, from 1857 to the present, and can be found at US Business Cycle Expansions and Contractions, National Bureau of Economics Research, www.nber.org/cycles.html.

  27. The HP filter fits a smooth nonlinear trend curve to a time series by decomposing it into a nonstationary trend component and a stationary cyclical component.

  28. Given the insignificant coefficient on the number of members and concern that excluding cartels for which we do not have the number of members might bias our results, we have estimated the regressions in Tables 4 and 5 on a sample that includes the 18 cartels for which we do not have a measure of the number of member of firms (and drops that variable from the regression). The size and significance of the other coefficients do not change as a result of the change in sample.

  29. Harrington and Chang (2012) examine the impact of leniency on enforcement when the antitrust authority has limited resources.

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Acknowledgments

We are extremely grateful for the research assistance of Brian Apel, Danial Asmat, Sasha Brodsky, Yulia Chhabra, Anna Walker, and Nathan Wilson. The paper also benefitted greatly from comments by Robert Feinberg as well as discussants and participants at the 2013 conference in honor of Robert Lanzilotti, University of Florida, and the 2014 International Industrial Organization Conference.

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  1. University of Michigan Ross School of Business, 701 Tappan Street, Ann Arbor, MI, 48109-1234, USA

    Margaret C. Levenstein

  2. Johns Hopkins University Carey School of Business, 100 International Drive, Baltimore, MD, 21202, USA

    Valerie Y. Suslow

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  1. Margaret C. Levenstein
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  2. Valerie Y. Suslow
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Correspondence to Margaret C. Levenstein.

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Levenstein, M.C., Suslow, V.Y. Price Fixing Hits Home: An Empirical Study of US Price-Fixing Conspiracies. Rev Ind Organ 48, 361–379 (2016). https://doi.org/10.1007/s11151-016-9520-5

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