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引用本文:潘宁、陈浩邦:创新视角下的反垄断执法改进——基于反垄断处罚决定书与公司专利及财务数据的实证分析,载《交大法学》2023年第4期,第95~118页。
Pan Ning & Chen Haobang, The Improvement of Anti Monopoly Enforcement from the Perspective of Innovation, (4) SJTU LAW REVIEW 95-118 (2023).
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创新视角下的反垄断执法改进——基于反垄断处罚决定书与公司专利及财务数据的实证分析
潘宁、陈浩邦1,2
1.北京大学法学院;2.清华大学经济管理学院
摘要:
发展导向的“新发展法治”要求竞争法治也应有助于促进发展。创新是引领发展的第一动力,因此,为完善竞争法治,需要从创新视角审视反垄断执法并思考如何改进。通过实证分析反垄断调查和处罚对上市公司创新的影响,可发现反垄断立案调查和调查结束后做出处理决定都对涉事企业的创新构成了负向冲击,即便其中有部分企业最后并不构成《反垄断法》意义上的违法行为,这种负效应依然存在;对那些被决定处罚的企业来说,被处罚没数额越大,创新支出和产出受影响越大。这意味着反垄断执法可能“误伤”涉案企业的创新,为了降低反垄断执法过程对涉案企业创新的负外部性,做出立案调查决定的公开范围和程序需有所规范,整个执法程序需进一步简化,反垄断执法机构裁量罚款时应注意节制。
关键词:  反垄断创新、实证分析、倾向得分匹配、双重差分法
DOI:
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基金项目:
The Improvement of Anti Monopoly Enforcement from the Perspective of Innovation
Pan Ning & Chen Haobang
Abstract:
The development-oriented “new development rule of law” requires that competition law should also contribute to promoting development. Innovation is the first driving force of development, so in order to improve competition law, we need to examine anti-monopoly enforcement from the perspective of innovation and think about how to improve it. Through the empirical analysis of the impact of anti-monopoly investigations and penalties on the innovation of listed companies, it is found that both the registration of anti-monopoly cases and the final decisions made after formal investigations have a negative impact on the innovation of the companies involved, even if some of them do not constitute violations within the meaning of the anti-monopoly law; for the companies that are penalized, the more they are penalized, the more their innovation is affected, implying that the anti-monopoly law enforcement may have excessively “hurt by mistake” their innovation. In view of this, the open scope and procedures of the investigation decision need to be standardized, the anti monopoly law enforcement procedures need to be further simplified and the penalty should be limited cautiously in discretion in the practice of antitrust enforcement.
Key words:  Anti-Monopoly, Innovation, Empirical Analysis, Propensity Score Matching, Difference in Difference Model
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