Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 April 2018 / Approved: 2 May 2018 / Online: 2 May 2018 (08:37:32 CEST)

A peer-reviewed article of this Preprint also exists.

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. Sustainability 2018, 10, 1938.

Journal reference: Sustainability 2018, 10, 1938
DOI: 10.3390/su10061938

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.

Subject Areas

Bayesian approach; conjugate prior; cartel; leniency program; policy simulation

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