| dc.creator (作者) | 王智賢;侯文婷;林玫吟 | zh_TW |
| dc.creator (作者) | Wang, Jue-Shyan;Hou, Wen-Ting;Lin, Mei-Yin | - |
| dc.date (日期) | 2016-04 | - |
| dc.date.accessioned | 16-八月-2016 14:04:35 (UTC+8) | - |
| dc.date.available | 16-八月-2016 14:04:35 (UTC+8) | - |
| dc.date.issued (上傳時間) | 16-八月-2016 14:04:35 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/100226 | - |
| dc.description.abstract (摘要) | 聯合行為的存在，可能會傷害市場的競爭公平，因此，各國紛紛制定反壟斷政策，以打擊聯合行為。寬恕政策便是其中一種方法，而其效果為何，是本文所要探討的重點。本文延伸Silbye (2012)之設定，建立反托拉斯法主管機關開始調查前及開始調查後兩個階段的賽局基本模型。先將台灣、日本、歐盟、美國四個經濟體的寬恕政策法規內容應用至基本模型裡，並得出各階段之下廠商所會採取的子賽局完全均衡。本研究發現，美國的寬恕政策，最能促進廠商提早申報誘因。 | - |
| dc.description.abstract (摘要) | The cartels may injure the fairness of market competition and most of the authorities around the world have implemented Antitrust Law to eliminate them. The leniency policy is a regulation which can eliminate the cartels. The purpose of this article is to investigate the effect of this policy. We extend the model of Silbye (2012) and establish a two-stage game theoretical model. The first stage is before the beginning of antitrust authority’s detection, and the second stage is after the beginning of antitrust authority’s detection. First, we apply the leniency policy in Taiwan, Japan, the U.S.A. and the E.U. into the basic model. And then we get the subgame perfect equilibrium of firms in each stage. We find the leniency policy in the U.S.A. has the most effective impact on promoting the incentive of the firms to report early. | - |
| dc.format.extent | 952590 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | 社會科學論叢, 10(1), 65-87 | - |
| dc.relation (關聯) | Journal of Social Sciences | - |
| dc.subject (關鍵詞) | 寬恕政策; 反托拉斯; 卡特爾; 聯合行為; 子賽局完全均衡 | - |
| dc.subject (關鍵詞) | Leniency Policy; Antitrust; Cartel; Concerted Action; Subgame Perfect Equilibrium | - |
| dc.title (題名) | 聯合行為寬恕政策之跨國比較 | zh_TW |
| dc.title.alternative (其他題名) | TheCross-Country Comparison of Leniency Policy | - |
| dc.type (資料類型) | article | - |
