Intensified law enforcement and firm innovation: Evidence from China's antitrust consolidation

IF 5.2 1区 经济学 Q1 ECONOMICS
Jialiang Zhu , Jinnan Gu
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引用次数: 0

Abstract

This paper examines the significant transformation in China's antitrust regulatory framework following the consolidation of its three primary antitrust agencies the Ministry of Commerce (MOFCOM), the National Development and Reform Commission (NDRC), and the State Administration for Industry & Commerce (SAIC) into the State Administration for Market Supervision (SAMR). Utilizing data from publicly listed firms across various industries, we analyze how firms in highly concentrated industries have adapted to this consolidation, by increasing the R&D investment, patent filing, and total factor productivity (TFP). Our mechanism channel indicates that stricter enforcement actions and fines by SAMR have spurred competition and innovation, with effects that are comparably significant across both state-owned enterprises (SOEs) and private firms. The paper contributes to the literature by providing an empirical assessment of the effects of this major restructuring within China's Anti-Monopoly Law (AML) enforcement. Our findings shed light on the broader implications of regulatory consolidations on firm innovation, offering insights into the efficacy of such transformations in antitrust policy.

强化执法与企业创新:中国反垄断整顿的证据
中国的三个主要反垄断机构--商务部(MOFCOM)、国家发展和改革委员会(NDRC)以及国家工商行政管理总局(SAIC)合并为国家市场监督管理总局(SAMR)后,中国的反垄断监管框架发生了重大转变。利用各行业上市公司的数据,我们分析了高度集中行业的企业如何通过增加研发投入、专利申请和全要素生产率(TFP)来适应这种整合。我们的机制渠道表明,国家知识产权局更严格的执法行动和罚款刺激了竞争和创新,其效果在国有企业和私营企业中都相当显著。本文对中国《反垄断法》(AML)执法范围内的这一重大调整所产生的影响进行了实证评估,为相关文献做出了贡献。我们的研究结果揭示了监管整合对企业创新的广泛影响,为反垄断政策中此类转型的有效性提供了启示。
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来源期刊
中国经济评论
中国经济评论 ECONOMICS-
CiteScore
10.60
自引率
4.40%
发文量
380
期刊介绍: The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.
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