Park, J.; Lee, J.; Ahn, S. Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program. Sustainability2018, 10, 1938.
Park, J.; Lee, J.; Ahn, S. Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program. Sustainability 2018, 10, 1938.
Park, J.; Lee, J.; Ahn, S. Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program. Sustainability2018, 10, 1938.
Park, J.; Lee, J.; Ahn, S. Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program. Sustainability 2018, 10, 1938.
Abstract
Cartels cause tremendous damage to the market economy and disadvantage consumers by causing higher prices and lower quality; moreover, they are difficult to detect. We need to prevent them by scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. Particularly, the probability of cartel penalization is a useful indicator for the evaluation of the competition enforcement. This study is to estimate the probability of cartel penalization by using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by Bayesian approach from cartel data of Department of Justice in United States from 1970 to 2009. The probability of cartel penalization is seen to be sensitive to change of competition law and the results shows the usefulness of higher interpretation than other research. The result of the policy simulation shows how effective the leniency program is. From this estimation, antitrust enforcement is evaluated, and thereby, can be improved.
Business, Economics and Management, Econometrics and Statistics
Copyright:
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