摘要
基于中国30个省份的面板数据,采用Malmqusit生产率指数法测算了2000—2015年产学研协同创新效率,在此基础上,利用核密度估计法和马尔可夫转移概率矩阵对产学研协同创新效率的地区差距及增长分布的动态演进特征进行分析。结果显示,中国产学研协同创新效率考察期内整体呈现明显的空间非均衡性特征,各省份效率水平差异明显,呈现东部、中部、西部地区依次递减的空间格局;核密度估计表明产学研协同创新效率水平差距整体呈现扩大趋势,东部、中部、西部三大区域有所区别;马尔科夫转移概率矩阵表明产学研协同创新效率水平分布相对稳定,未来高效率省份的引领作用会明显提高,产学研协同创新效率水平极大可能实现持续增长的发展趋势。
Based on the panel data of 30 provinces in China,the Malmqusit productivity index is used to calculate the collaborative innovation efficiency of industry-university-research from 2000 to 2015,and on this basis,the regional gap and the dynamic evolution characteristics of the growth distribution of collaborative innovation efficiency of industry-university-research are analyzed by using the kernel density estimation method and Markov transfer matrix.The results show that the overall characteristics of spatial non-equilibrium are obvious during the investigation period of the collaborative innovation efficiency of industry-university-research in China,and thatthe difference of efficiency level between provinces is obvious,and that the efficiency decreases from east to center to west.Kernel density estimation indicates that the gap between collaborative innovation efficiency of industry-university-research is expanding as a whole.There are differences among the eastern,central and western regions.Markov transfer probability matrix indicates that the distribution of collaborative innovation efficiency level of industry-university-research is relatively stable,and that the leading role of efficient provinces will be significantly improved in the future,and that the collaborative innovation efficiency of industry-university-research is likely to achieve the development trend of sustained growth.
作者
杨德玲
刘战伟
YANG Deling;LIU Zhanwei(Xuchang Electric Vocational College,Xuchang,Henan 461000,China;School of Business,Xuchang University,Xuchang,Henan 461000,China)
出处
《中州大学学报》
2019年第6期30-35,共6页
Journal of Zhongzhou University
基金
河南省高等学校重点科研项目(19A630027)
关键词
产学研协同创新效率
地区差距
核密度估计
马尔科夫链
industry-university-research collaborative innovation efficiency
regional gap
kernel density estimation
Markov chain