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

Title
Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program
Author
안선응
Keywords
Bayesian approach; conjugate prior; cartel; leniency program; policy simulation
Issue Date
2018-06
Publisher
MDPI AG
Citation
SUSTAINABILITY, v. 10, No. 6, Article no. 1938
Abstract
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.
URI
https://www.mdpi.com/2071-1050/10/6/1938/htmhttp://repository.hanyang.ac.kr/handle/20.500.11754/81128
ISSN
2071-1050
DOI
10.3390/su10061938
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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