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异质性视角下的R&D企业投入、政府资助与创新绩效——基于微观面板的计数模型实证研究 被引量:31

Enterprise R&D Investment, Public Grants and Innovation Performance from the Perspective of Heterogeneity:An Empirical Study Based on Micro Panel Data with Models for Count Data
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摘要 从产权异质性视角出发,选择标准负二项回归为主的计数模型,运用山东省906家省级认定高新技术企业跨越4年的3624组微观非平衡面板数据,对R&D企业投入、政府投入与创新绩效的关系进行实证研究。结果表明:(1)企业R&D人员投入对专利产出存在显著的正效应,R&D经费投入的正效应不显著但对发明专利存在显著正向影响;(2)R&D政府资助对专利产出尤其是发明专利产出存在显著正效应,对实用新型专利和外观设计专利产出影响不显著;(3)R&D政府资助、企业年龄和规模对创新产出存在"倒U"型影响但都处于门槛值左侧;(4)政府激励政策强化对企业创新有显著影响,企业创新活动日趋活跃。 From the perspective of property heterogeneity,taking standard negative binomial regression as the main model for count data,and using 3624 sets of micro unbalanced panel data of 906 provincial high-tech firms spanning 4 years in Shandong Province,this paper empirically study the impact of enterprise R&D investment and public R&D grants on innovation performance.The results show that:(1)R&D personnel input of enterprises plays a significantly positive role in patent output,while R&D expenditure's positive effect is not significant but remarkably promotes the firm's output of invention patents;(2)Public R&D grants impose significantly positive effect on innovation output especially invention patent,but has no obvious impact on patent for utility models or designs;(3)Public R&D grants,firm age and scale have"inverted-U"effects on innovation output but still lie at the threshold value to the left;(4)Government incentive policies have significant impact on enterprise innovation and enterprise innovation activities become more and more active.
作者 张凤兵 王会宗 ZHANG Fengbing;WANG Huizong(School of Economics,Shandong Normal University,Jinan 250014,China;School of Marxism,Shandong University,Jinan 250100,China)
出处 《经济与管理评论》 CSSCI 北大核心 2019年第2期80-92,共13页 Review of Economy and Management
基金 国家自然科学基金面上项目"中国产业政策与竞争政策的有效性及协调机制研究"(71473151) 山东省软科学研究重大项目"促进山东省科技中介服务业发展的关键问题研究"(2015RZB01002)
关键词 异质性 R&D企业投入 R&D政府资助 创新绩效 标准负二项回归 Heterogeneity Enterprise R&D investment Public R&D grants Innovation performance Standard negative binomial regression
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