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A Structural Break Cartel Screen for Dating and Detecting Collusion

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Abstract

In this article, a new empirical screen for detecting cartels is developed. It can also be used to date the beginning of known conspiracies, which is often difficult in practice. Structural breaks that are induced by cartels in the data-generating process of industry prices are detected by testing reduced-form price equations for structural instability. The new screen is applied to three European markets for pasta products, in which it successfully reports the cartels that were present in the Italian and Spanish markets, but finds no suspicious patterns in the French market, which was not cartelised.

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Notes

  1. This is not to be confused with price dispersion between firms at a point in time (see, e.g., Connor 2005).

  2. The interested reader is referred to Aue and Horváth (2013), Andreou and Ghysels (2009), and Perron (2006) for more general recent overviews over the literature.

  3. Another estimates-based test is the Recursive Estimates (RE) test of Sen (1980) and Ploberger et al. (1989). Here, the ME test is preferred, as unlike the RE test it provides non-parametric estimates of the corresponding mean functions (Kuan and Hornik 1995, p. 136). Further, it usually has higher power than the RE test when there are multiple structural breaks (Chu et al. 1995b, pp. 713–714).

  4. Carlton (2004) and Boswijk et al. (2016) apply the methodology of Bai and Perron (1998, 2003) to date cartels as well, albeit based on different econometric approaches and to answer different research questions. I am indebted to Maarten Pieter Schinkel for pointing this out.

  5. See, e.g., Sect. 4: Without controlling for significant input cost changes that are faced by the French pasta industry, price increases in the industry under competition would wrongly be detected as structural breaks.

  6. This implies that, e.g., the cartel does not strategically alter its input costs (see, e.g., Mueller and Parker, 1992), which would lead to endogeneity of some of the variables. I am thankful to Daniel Rubinfeld for pointing this out.

  7. Further differencing of the variables needs to be conducted when a first-difference is nonstationary.

  8. Tests for unit roots have to be chosen carefully. Structural breaks in the time series can be misinterpreted as nonstationarity by Augmented Dickey Fuller and Phillips–Perron tests. An often suitable unit root test is proposed by Zivot and Andrews (1992), which tests for unit roots against the alternative of a structural break.

  9. This approach is suitable because the p value comparisons are few in numbers only and complementary. Further, as the tested p values are strongly and positively correlated with each other, many approaches to adjust p values to address multiple testing are likely to produce misleading results by being overly conservative. The fluctuation tests that are used here tend to be conservative, which reduces the risk of Type I errors (Kuan and Hornik 1995).

  10. For the purpose of the screen, the BIC is best. The BIC does not perform well when there are no structural breaks present by overstating the true number of breaks in the data; but it performs well in the presence of structural breaks.

  11. For Italy, only the periods prior to the cartelisation of the industry in October 2006 are used. Yet, this exclusion restriction has no effects on results, as the cartel did not influence market prices before June 2007.

  12. This does not affect the results of the structural break tests in Sect. 4.3. Using unadjusted covariance matrices for the structural break tests in the Spanish test rather than HAC consistent matrices provides p values of: 0.006 for the OLS-CUSUM test; 0.010, 0.013, 0.021, 0.021, and 0.023 for the OLS-MOSUM tests with window widths of 15, 20, 25, and 30%, respectively; and p values of 0.01 for all ME tests that are based on the same window widths.

  13. I am indebted to Achim Zeileis for providing access to a developer version of strucchange that allows the use of HAC covariance matrix estimators for the estimation of empirical fluctuation processes.

  14. Graphical results of the OLS-CUSUM tests can be found in Fig. 6 in “Appendix”.

  15. The following results are robust to reasonable alternative specifications of the tested periods.

  16. This approach is very similar to the price-change based cartel screen of Hüschelrath and Veith (2013).

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Acknowledgements

The views expressed in this article represent the personal opinion of the author, and do not represent positions of the Bundeskartellamt. I thank the editor Lawrence White, two anonymous referees, Rosa Abrantes-Metz, Giuliana Battisti, Farasat Bokhari, Steve Davies, Andreas Gerster, Nils Gutacker, Franco Mariuzzo, Peter Ormosi, George Papadopoulos, Daniel Rubinfeld, Maarten Pieter Schinkel, and Achim Zeileis, as well as participants at the Workshop of the Law and Economics of Antitrust 2016 in Zurich, RGS Doctoral Conference 2016, NIE-Doctoral Student Colloquium 2015, CCP-UEA 2014 and 2015, CLEEN 2014, and CLaSF 2014 for helpful comments. Any remaining errors are my own.

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  1. Bundeskartellamt (Federal Cartel Office), Kaiser-Friedrich-Str. 16, 53113, Bonn, Germany

    Carsten J. Crede

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Appendix

Appendix

See Tables 7, 8, 9 and Fig. 6.

Table 7 Data sources and definition of variables
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Table 8 Descriptive statistics
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Table 9 Variance test: GARCH regressions
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Crede, C.J. A Structural Break Cartel Screen for Dating and Detecting Collusion. Rev Ind Organ 54, 543–574 (2019). https://doi.org/10.1007/s11151-018-9649-5

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