›› 2017, Vol. 39 ›› Issue (2): 217-223.doi: 10.16507/j.issn.1006-6055.2017. 03.016

• 科技评价与评估 • 上一篇    

我国高端装备制造业创新绩效的测度及空间集聚性分析

于霞1 徐敏1,2   

  1. 1. 河海大学商学院,南京211100; 
    2. 中国制造业发展研究院,南京210044
  • 收稿日期:2016-07-07 修回日期:2016-09-18 出版日期:2017-04-25 发布日期:2017-04-26
  • 基金资助:

    中国制造业发展研究院开放课题( SK20140090-17) 资助

Measurement and Spatial Clustering Analysis of China's High-end Equipment Manufacturing Industry Innovation Performance

YU Xia1 XU Min1,2   

  1.  1. Business School of Hohai University,Nanjing 211100,China;
     2. China Institute of Manufacturing Development,Nanjing 210044,China
  • Received:2016-07-07 Revised:2016-09-18 Online:2017-04-25 Published:2017-04-26

摘要:

以2007 ~2014 年中国30 个省市的面板数据为样本,利用随机前沿面板模型测度我国高端装备制造业的创新绩效,并就其空间集聚性进行了进一步探讨。研究发现: 研发资本存量和人员对我国高端装备制造业有显著的正向影响,且资本存量有更高的产出弹性; 政府支持和外部技术获取与创新绩效负相关; 企业科技投入强度和对外开放度对创新绩效的提高有显著的促进作用; 我国高端装备制造业的创新绩效总体上呈现上升趋势,且表现出“东高西低”的集聚特征。这些研究结果可为中国由“制造大国”向“制造强国”的转变提供几点启示。

关键词: 高端装备制造业, 创新绩效, 随机前沿面板模型, 空间集聚性

Abstract:

Based on the panel data of China's 30 provinces during 2007 ~ 2014,the stochastic frontier panel production function is used to make an empirical analysis of China's high-end equipment manufacturing industry innovation performance,and its spatial clustering is further discussed.The study finds that: R&D funds expenditure has a higher output elasticity compared to the R&D personnel invest; government support and external technology acquisition have negative correlation with the innovation performance; enterprise investment in science and foreign open degree have positive relation with the innovation performance; Chinese high-end equipment manufacturing industry innovation performance overall presented an upward trend and revealed a“High in the east and low in the west”agglomeration characteristics.Relying to these results some enlightenment can be provided for transforming China from a manufacture of quantity to one of quality.

Key words: high-end equipment manufacturing industry, innovation performance, stochastic frontier panel model, spatial clustering

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