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Open AccessArticle

Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program

1
Department of Industrial and Management Engineering, Hanyang University, Seoul 04763, Korea
2
Department of Industrial and Management Engineering, Hanyang University ERICA, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(6), 1938; https://doi.org/10.3390/su10061938
Received: 30 April 2018 / Revised: 8 June 2018 / Accepted: 8 June 2018 / Published: 10 June 2018
(This article belongs to the Special Issue Risk Measures with Applications in Finance and Economics)
Cartels cause tremendous damage to the market economy and disadvantage consumers by creating higher prices and lower-quality goods; moreover, they are difficult to detect. We need to prevent them through scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. In particular, the probability of cartel penalization is a useful indicator for evaluating competition enforcement. This study estimates the probability of cartel penalization using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by a Bayesian approach from the cartel data of the Department of Justice in the United States between 1970 and 2009. The probability of cartel penalization is seen as sensitive to changes in competition law, and the results have implications for market efficiency and the antitrust authority’s efforts against cartel formation and demise. The result of policy simulation shows the effectiveness of the leniency program. Antitrust enforcement is evaluated from the estimation results, and can therefore be improved. View Full-Text
Keywords: Bayesian approach; conjugate prior; cartel; leniency program; policy simulation Bayesian approach; conjugate prior; cartel; leniency program; policy simulation
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Park, J.; Lee, J.; Ahn, S. Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program. Sustainability 2018, 10, 1938.

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