摘要
利用超效率DEA模型对2012—2018年中国高技术产业的创新效率进行评价,在此基础上,建立面板数据模型探讨其影响因素。结果表明:东北三省创新效率较低,西部各省区市创新效率两极化明显;北方创新效率呈缓“N”形波动,2017年前低于南方,但近年来上升态势显著,两者间的差距越来越小;科学技术投入力度、教育投入比重、对外开放度、高素质人口比重及城市化率对南北方高技术产业创新效率有不同的影响。最后根据上述结果从政府支持、对外开放及人才引育等方面的分区调节提出对策建议。
The super-efficiency DEA model is used to evaluate the innovation efficiency of China’s high-tech industry from 2012 to 2018. On this basis, a panel data model is established to explore its influencing factors. The results show that the innovation efficiency of the three northeastern provinces is low, and the innovation efficiency of the western provinces(regions,cities) is obviously polarized. The innovation efficiency of the north shows a slow N-shaped fluctuation, which was lower than that of the south before 2017, but in recent years it has increased significantly and the gap between them is getting smaller and smaller. The investment in science and technology, the proportion of education investment, the degree of opening up,the proportion of high-quality population, and the urbanization rate have different effects on the innovation efficiency of high-tech industries in the south and in the north. Based on this,reasonable suggestions are made for regional adjustment on government support, opening up and talent introduction.
作者
和军
张勇之
HE Jun;ZHANG Yongzhi
出处
《上海商学院学报》
2022年第2期21-32,共12页
Business Economic Review
基金
国家社会科学基金重大项目“振兴东北老工业基地重大体制机制问题及对策研究”(17ZDA060)
辽宁省教育厅智库项目“发挥辽宁在东北全面振兴中的领头羊作用研究”(LZK201903)。
关键词
高技术产业
创新效率
投入产出
超效率DEA
固定效应面板模型
high-tech industry
innovation efficiency
input-output
super-efficiency DEA
fixed-effect panel model