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How Much Do Cartel Overcharge?

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Abstract

Connor and Lande (Issues in competition law and policy, pp 2203–2218, 2008) conducted a survey of cartels and found a mean overcharge estimate in the range of 31–49 %. By examining more sources, Connor (Price-fixing overcharges, 2nd edn. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1610262, 2010) finds a mean of 50.4 % for successful cartels. However, the data that are used in those studies are estimates that are obtained from different methodologies, sources, and contexts rather than from direct observation. We conduct a meta-analysis of cartel overcharge estimates that provides a sound treatment of these matters and other data problems. We find a bias-corrected mean and median overcharge estimate of 15.47 and 16.01 %. Our results have significant antitrust policy implications.

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Notes

  1. The database actually describes cartel episodes. Each episode is treated as a different observation. There is no formal proof or admission of guilt for approximately one-third of the observations. Hence, the data include convicted cartels as well as alleged ones. We sincerely thank Professor John Connor for generously making his database available to us.

  2. Source: "The EU competition Rules on Cartels," document published by the law firm Slaughter and May in 2012. Publically available on the website of the company.

  3. Boyer (2013) discusses this recent literature.

  4. The methodology that was used in Boyer and Kotchoni (2011, 2012) predicts a bias-corrected OE of 13.62 % for cartels with raw OEs that lie in the range (0–50 %).

  5. Connor (2014) finds a long-run median overcharge of 23.0 % and a mean of at least 49.0 % for all cartels of all times. The skewness problem is pointed out by Connor and Lande (2008) and Connor (2010). It should be emphasized that Connor has been conservative in recording some of the OE, notably by tagging and excluding peak estimates from the sample (see Connor 2010, pp. 48 and following).

  6. Ehmer and Rosati (2009) point out that many of the estimates that are in Connor’s sample are obtained from “a simple calculation of the difference between prices charged during the operation of the cartel and in other periods or other markets that are believed to be competitive. By completely neglecting all other factors that can cause prices to change, the authors of these estimates simplistically attribute the entire price variation to the effects of the cartel”.

  7. In addition to Y1 through Y5, the vector Y contains the interactions of the US geographical market with the periods P1, P2, and P3.

  8. An overcharge calculation approach that is based on the Lerner index can fall within the family of econometric methods or cost-based methods, depending on how this index is estimated.

  9. Table 2 of the appendix to Connor (2010) provides a “Summary of Price-Fixing Damages, Social-Science Studies”. The table presents 280 cartels, their OEs, and a description of the estimation method. Of the 280 OEs, 51 have been obtained by conversion of a Lerner index. Connor (2014) reports Lerner indices as OEs without conversion, thereby subject to upward biases.

  10. For instance, see White (2001) for a critique of the before-and-after methods that were employed by Connor (1997) in the ADM-lysine case.

  11. We choose not to go beyond θ = 70 % as our previous analysis concluded that the overall data quality is low for the range OE > 65 %.

  12. That is, unconditionally on the truncation \( {\hat{\uptheta }}_{\text{i}} \in \left( {0,\uptheta} \right] \). As zero overcharges are excluded from the sample, the results are still conditional on \( {\hat{\uptheta }}_{\text{i}} > 0 \).

  13. Note that a cartel can fail at raising its price while being effective along other competitive dimensions (e.g., entry, capacity, innovation, advertizing, credit terms, etc.). The database that is available to us does not permit us to address such issues.

  14. The link between the overcharge and the optimal fine is also studied by Nieberding (2006), Houba et al. (2014), Ginsburg and Wright (2010) and Harrington (2010, 2014).

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Acknowledgments

We are very grateful to the Editor (Lawrence White) for his patience as well as for his generous and challenging comments and suggestions. We also benefited from remarks by Jimmy Royer and René Garcia and by two anonymous referees. We remain solely responsible for its content.

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Authors and Affiliations

  1. Toulouse School of Economics, CIRANO, Université de Montréal, 1400-1130 Sherbrooke St. West, Montreal, QC, H3A 2M8, Canada

    Marcel Boyer

  2. African School of Economics, 02BP372, Cotonou, Benin

    Rachidi Kotchoni

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  1. Marcel Boyer
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  2. Rachidi Kotchoni
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Correspondence to Rachidi Kotchoni.

Additional information

Preliminary versions of this paper circulated as working papers under the titles “The Econometrics of Cartel Overcharge” and “How Much Do Cartels Typically Overcharge?”

Appendix: Summary Statistics of the Database

Appendix: Summary Statistics of the Database

See Tables 13 and 14.

Table 13 Cartels with raw OE in the interval (0, 49 %]
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Table 14 Cartels with raw OEs = 0 % and OEs > 49 %
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Boyer, M., Kotchoni, R. How Much Do Cartel Overcharge?. Rev Ind Organ 47, 119–153 (2015). https://doi.org/10.1007/s11151-015-9472-1

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