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Estimación Máximo Verosímil y Bayesiana de la Probabilidad de Detección
[Maximum Likelihood and Bayesian Estimation of Detection Probabilities]

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  • Merino Troncoso, Carlos

Abstract

This article estimates the probability of detection of cartels using a sample of cartels discovered between 2011 and 2016. This type of study has been questioned for using a sample considered biased of all cartels. This article uses two alternative methodologies to conclude, like (Harrington and Wei 2017), that there is no such bias, and that the estimation of the probability of detection around 15% using the traditional Bryant-Eckard method is acceptable, although it must be interpreted as a probability of disappearance (or death) of the cartels, which only equals the probability of detection when we assume that all the cartels disappear by detection. We must therefore consider that the result is an upper limit below which the real probability of detection should be.

Suggested Citation

  • Merino Troncoso, Carlos, 2020. "Estimación Máximo Verosímil y Bayesiana de la Probabilidad de Detección [Maximum Likelihood and Bayesian Estimation of Detection Probabilities]," MPRA Paper 110264, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:110264
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    References listed on IDEAS

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    1. Bryant, Peter G & Eckard, E Woodrow, Jr, 1991. "Price Fixing: The Probability of Getting Caught," The Review of Economics and Statistics, MIT Press, vol. 73(3), pages 531-536, August.
    2. Joseph E. Harrington & Myong-Hun Chang, 2009. "Modeling the Birth and Death of Cartels with an Application to Evaluating Competition Policy," Journal of the European Economic Association, MIT Press, vol. 7(6), pages 1400-1435, December.
    3. Peter L Ormosi, 2011. "A tip of the iceberg? The probability of catching cartels," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2011-06, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    5. Harrington, Joseph E., 2014. "Penalties and the deterrence of unlawful collusion," Economics Letters, Elsevier, vol. 124(1), pages 33-36.
    6. Jihyun Park & Juhyun Lee & Suneung Ahn, 2018. "Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program," Sustainability, MDPI, vol. 10(6), pages 1-15, June.
    7. Yannis Katsoulacos & David Ulph, 2013. "Antitrust Penalties and the Implications of Empirical Evidence on Cartel Overcharges," Economic Journal, Royal Economic Society, vol. 123(11), pages 558-581, November.
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    More about this item

    Keywords

    cartel; detection; probabilities;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L4 - Industrial Organization - - Antitrust Issues and Policies

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