IDEAS home Printed from https://ideas.repec.org/a/oup/jcomle/v10y2014i1p63-86..html
   My bibliography  Save this article

Cartel Overcharges And The Deterrent Effect Of Eu Competition Law

Author

Listed:
  • Florian Smuda

Abstract

This article examines cartel overcharges for the European market. Using a sample of 191 overcharge estimates and several parametric and semiparametric estimation procedures, the impact of different cartel characteristics and the market environment on the magnitude of overcharges is analyzed. The mean and median overcharge rates are found to be 20.70 percent and 18.37 percent of the selling price and the average cartel duration is 8.35 years. Certain cartel characteristics and the geographic region of cartel operation influence the level of overcharges considerably. Furthermore, empirical evidence reveals that from an ex-post perspective the currently existing fine level of the EU Guidelines is insufficient for optimal cartel deterrence, suggesting further adjustments.

Suggested Citation

  • Florian Smuda, 2014. "Cartel Overcharges And The Deterrent Effect Of Eu Competition Law," Journal of Competition Law and Economics, Oxford University Press, vol. 10(1), pages 63-86.
  • Handle: RePEc:oup:jcomle:v:10:y:2014:i:1:p:63-86.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/joclec/nht012
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Emmanuel Combe & Constance Monnier & Renaud Legal, 2008. "Cartels: The Probability of Getting Caught in the European Union," Bruges European Economic Research Papers 12, European Economic Studies Department, College of Europe.
    2. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    3. Leonardo J. Basso & Thomas W. Ross, 2010. "Measuring The True Harm From Price‐Fixing To Both Direct And Indirect Purchasers," Journal of Industrial Economics, Wiley Blackwell, vol. 58(4), pages 895-927, December.
    4. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    5. Cento Veljanovski, 2011. "Deterrence, Recidivism, And European Cartel Fines," Journal of Competition Law and Economics, Oxford University Press, vol. 7(4), pages 871-915.
    6. Yuliya Bolotova & John M. Connor & Douglas J. Miller, 2007. "Factors influencing the magnitude of cartel overcharges: An empirical analysis of food-industry cartels," Agribusiness, John Wiley & Sons, Ltd., vol. 23(1), pages 17-33.
    7. Bolotova, Yuliya V., 2009. "Cartel overcharges: An empirical analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 321-341, May.
    8. Kenneth Y. Chay & James L. Powell, 2001. "Semiparametric Censored Regression Models," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 29-42, Fall.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arno Rasek & Florian Smuda, 2018. "Ex-Post Evaluation of Competition Law Enforcement Effects in the German Packaging Waste Compliance Scheme Market," De Economist, Springer, vol. 166(1), pages 89-109, March.
    2. Comisión Nacional de los Mercados y la Competencia (CNMC), 2023. "Guide. Competition against inflation: How competition and efficient regulation help protect the purchasing power of consumers," Colección Estudios de Mercado G-2022-02_ENG, Comisión Nacional de los Mercados y la Competencia (CNMC).
    3. Kai Huschelrath & Sebastian Peyer, 2013. "Public and Private Enforcement of Competition Law A Differentiated Approach," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2013-05, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    4. 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.
    5. Hüschelrath, Kai & Peyer, Sebastian, 2013. "Public and private enforcement of competition law: A differentiated approach," ZEW Discussion Papers 13-029, ZEW - Leibniz Centre for European Economic Research.
    6. Hoang, Cung Truong & Hüschelrath, Kai & Laitenberger, Ulrich & Smuda, Florian, 2014. "Determinants of self-reporting under the European corporate leniency program," International Review of Law and Economics, Elsevier, vol. 40(C), pages 15-23.
    7. 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..
    8. Harrington, Joseph E., 2014. "Penalties and the deterrence of unlawful collusion," Economics Letters, Elsevier, vol. 124(1), pages 33-36.
    9. 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.
    10. Hüschelrath, Kai & Laitenberger, Ulrich, 2015. "The settlement procedure in EC cartel cases: An empirical assesment," ZEW Discussion Papers 15-064, ZEW - Leibniz Centre for European Economic Research.
    11. Moritz Birgit & Becker Martin & Schmidtchen Dieter, 2018. "Measuring the Deterrent Effect of European Cartel Law Enforcement," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 18(3), pages 1-27, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    2. Pradhan, Jaya Prakash, 2010. "R&D strategy of small and medium enterprises in India: Trends and determinants," MPRA Paper 20951, University Library of Munich, Germany.
    3. Anil Kumar, 2012. "Nonparametric estimation of the impact of taxes on female labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 415-439, April.
    4. 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..
    5. Wocken, Meike & Kneib, Thomas, 2012. "Tobit regression to estimate impact of EU market intervention in dairy sector," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122528, European Association of Agricultural Economists.
    6. Andrés Langebaek R. & Diego Vásquez E., 2007. "Determinantes de la actividad innovadora en la industria manufacturera colombiana," Borradores de Economia 433, Banco de la Republica de Colombia.
    7. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    8. Barton Hughes Hamilton, 1997. "Racial discrimination and professional basketball salaries in the 1990s," Applied Economics, Taylor & Francis Journals, vol. 29(3), pages 287-296.
    9. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    10. Alejandro Cid & Daniel Ferres & Máximo Rossi, 2008. "Subjective Well-Being in the Southern Cone: Health, Income and Family," Documentos de Trabajo (working papers) 1308, Department of Economics - dECON.
    11. Robert F. Engle & Simone Manganelli, 1999. "CAViaR: Conditional Value at Risk by Quantile Regression," NBER Working Papers 7341, National Bureau of Economic Research, Inc.
    12. Mark Ottoni Wilhelm, 2008. "Practical Considerations for Choosing Between Tobit and SCLS or CLAD Estimators for Censored Regression Models with an Application to Charitable Giving," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 559-582, August.
    13. Liu, Yang & Jiang, Zhigao & Guo, Bowei, 2022. "Assessing China’s provincial electricity spot market pilot operations: Lessons from Guangdong province," Energy Policy, Elsevier, vol. 164(C).
    14. P. Čížek & S. Sadikoglu, 2018. "Bias-corrected quantile regression estimation of censored regression models," Statistical Papers, Springer, vol. 59(1), pages 215-247, March.
    15. Jiang, Rong & Qian, Weimin & Zhou, Zhangong, 2012. "Variable selection and coefficient estimation via composite quantile regression with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 308-317.
    16. Kenneth Y. Chay & James L. Powell, 2001. "Semiparametric Censored Regression Models," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 29-42, Fall.
    17. Heejung Bang & Anastasios A. Tsiatis, 2002. "Median Regression with Censored Cost Data," Biometrics, The International Biometric Society, vol. 58(3), pages 643-649, September.
    18. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2020. "Linear censored quantile regression: A novel minimum‐distance approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1275-1306, December.
    19. Koch, Alexander K. & Nafziger, Julia, 2020. "Motivational goal bracketing: An experiment," Journal of Economic Theory, Elsevier, vol. 185(C).
    20. Khan, Shakeeb & Powell, James L., 2001. "Two-step estimation of semiparametric censored regression models," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 73-110, July.

    More about this item

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jcomle:v:10:y:2014:i:1:p:63-86.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/jcle .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.