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The Econometrics of Cartel Overcharges

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  • Marcel Boyer
  • Rachidi Kotchoni

Abstract

Connor and Lande (2006) conducted a survey of cartel overcharge estimates and found an average in the range of 31% to 49%. By examining more sources, Connor (2010b) finds a median of 23.3% for all type of cartels and a mean of 50.4% for successful cartels. However, the data used in these studies are estimates rather than true observations, since the true illegal profits of cartels are rarely observable. Therefore, these data are subject to model error, estimation error and publication bias. A quick glance at the Connor database reveals that the universe of overcharge estimates is asymmetric, heterogenous and contains a number of influential observations. Beside the fact that overcharge estimates are potentially biased, fitting a linear OLS model to the data without providing a carefull treatment of the problems raised by the publication bias, outliers, asymmetry, and heterogeneity will necessarily produce distorted results. We conduct a meta-analysis of cartel overcharge estimates in the spirit of Connor and Bolotova (2006), but providing a sound treatment of the matters raised above. We find a mean bias-corrected overcharge estimate of 13.8% for all cartels, and of 13.6% for cartels with initial estimates lying between 0% and 50%. Connor et Lande (2006) survolent la littérature sur les majorations de prix imposées par les cartels et concluent à une augmentation moyenne variant entre 31 % et 49 %. Considérant un échantillon plus grand, Connor (2010b) trouve une médiane de 23,3 % pour tous les types de cartel et une moyenne de 50,4 % pour les cartels dont les majorations de prix estimées sont positives. Cependant, les échantillons utilisés dans ces études sont constitués d'estimations et non pas d'observations directes. De ce fait, ces échantillons héritent possiblement d'erreurs de modélisation et d'estimation, ainsi que d'un biais de publication. Une analyse sommaire des majorations dans l'échantillon de Connor révèle une distribution asymétrique, de l'hétérogénéité et la présence d'observations aberrantes. Ainsi, au-delà du fait que les estimations d'augmentation de prix par les cartels sont potentiellement biaisées, l'estimation d'un modèle par MCO avec de telles données sans un traitement adéquat de l'asymétrie, de l'hétérogénéité et des données aberrantes produirait des résultats déformés. Nous réalisons une nouvelle méta-analyse dans le même esprit que celui de Connor et Bolotova (2006), mais en proposant une prise en compte adéquate des problèmes mentionnés ci-dessus. Après correction du biais d'estimation, nos résultats suggèrent que la moyenne des majorations de prix estimées est de l'ordre de 13,8 % pour tous les types de cartels et de 13,6 % pour les cartels dont les estimations de majoration de prix se situaient initialement entre 0 % et 50 %.

Suggested Citation

  • Marcel Boyer & Rachidi Kotchoni, 2011. "The Econometrics of Cartel Overcharges," CIRANO Working Papers 2011s-35, CIRANO.
  • Handle: RePEc:cir:cirwor:2011s-35
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    1. Marcel Boyer & Rachidi Kotchoni, 2015. "How Much Do Cartel Overcharge?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 47(2), pages 119-153, September.
    2. John M. Connor & Robert H. Lande, 2017. "Comment on “The Empirical Basis for Antitrust: Cartels, Mergers, and Remedies”," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 24(3), pages 329-338, September.
    3. Salvatore Piccolo & Giancarlo Spagnolo, 2014. "Debt, Managers and Cartels," CSEF Working Papers 365, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    4. Iwan Bos & Stephen Davies & Peter L. Ormosi, 2014. "The deterrent effect of anti-cartel enforcement: A tale of two tails," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2014-06v2, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    5. Katsoulacos, Yannis & Motchenkova, Evgenia & Ulph, David, 2015. "Penalizing cartels: The case for basing penalties on price overcharge," International Journal of Industrial Organization, Elsevier, vol. 42(C), pages 70-80.
    6. Vasiliki Bageri & Yannis Katsoulacos, 2023. "Measuring the effects of information exchange on incentives to collude using calibrated simulations (with an example of the South African oil industry)," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1846-1875, June.
    7. Piccolo, Salvatore & Pignataro, Aldo, 2018. "Consumer loss aversion, product experimentation and tacit collusion," International Journal of Industrial Organization, Elsevier, vol. 56(C), pages 49-77.
    8. Marie-Laure Allain & Marcel Boyer & Rachidi Kotchoni & Jean-Pierre Ponssard, 2011. "The Determination of Optimal Fines in Cartel Cases The Myth of Underdeterrence," Working Papers hal-00631432, HAL.
    9. Panayiotis Agisilaou, 2013. "Collusion in Industrial Economics and Optimally Designed Leniency Programmes - A Survey," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2013-03, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    10. Marcel Boyer & Rachidi Kotchoni, 2015. "How Much Do Cartel Overcharge? (The "Working Paper" Version)," CIRANO Working Papers 2015s-37, CIRANO.
    11. Stephen Davies & Peter Ormosi, 2013. "The Impact of Competition Policy: What are the Known Unknowns?," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2013-07, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    12. Shastitko, Andrey (Шаститко, Андрей) & Golovanova, Svetlana (Голованова, Светлана), 2015. "'Comparable Markets' as a Tool of Antitrust Policy: Design, Application Experience, Development Directions [«Сопоставимые Рынки» Как Инструмент Антимонопольной Политики: Дизайн, Опыт Применения, На," Published Papers mn31, Russian Presidential Academy of National Economy and Public Administration.

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    Keywords

    Cartels; overcharges; Cartels; majoration de prix.;
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