我国“一流大学”建设高校科研创新效率测度及影响因素分析——基于三阶段DEA模型的实证研究

The Measurement and Influence Factors of Scientific Research Innovation Efficiency of“First-class”University in China——An Empirical Study Based on Three-stage DEA Model

准确测度我国“一流大学”建设高校科研创新效率对于进一步提升其科研管理水平有着重要的指导意义。本文基于三阶段DEA模型构建高校科研创新效率测度框架,选取教育部直属的32所“一流大学”建设高校开展实证研究。研究结果显示,高校科研创新效率测度会受到外部环境和随机误差的干扰,除人均GDP指标对科研创新效率的影响不显著外,城镇非私营单位在岗职工平均工资、政府科研拨款占科研经费总额比重会制约高校科研创新效率的提高,而城市R&D经费投入强度则有助于提升高校科研创新效率。剔除外部环境和随机误差干扰后,32所“一流大学”建设高校2020年科研创新综合效率、技术效率、规模效率均值分别为0.732、0.821、0.900,处于效率前沿的高校仅有6所,17所高校处于规模收益递减状态,多数“一流大学”建设高校需采取措施进一步提升技术效率或规模效率。最后,利用Tobit回归分析“一流大学”建设高校内部因素对科研创新效率的影响。

The measurement of the efficiency of scientific research innovation in universities is of great significance for further improving the level of scientific research management.Based on the three-stage DEA model,the research and innovation efficiency measurement framework of universities was constructed,and 32“first-class”universities directly under the Ministry of Education were selected to carry out empirical research.The results show that the measurement of scientific research innovation efficiency in universities was affected by external environment and random error.Except that GDP per capita had no significant impact on the efficiency of scientific research innovation,the average wage of employees in urban non-private units and the proportion of government scientific research allocation in the total scientific research funds restricted the improvement of scientific research innovation efficiency in universities.The intensity of urban R&D investment was helpful to improve the efficiency of scientific research and innovation in universities.After excluding the external environment and random error interference,the mean values of the comprehensive efficiency of scientific research,technical efficiency and scale efficiency of the 32“first-class”universities in 2020 were 0.732,0.821 and 0.900,respectively.There were only 6 universities in the frontier of efficiency,and 17 universities in the state of diminishing returns to scale.Most“first-class”universities need to take measures to further improve the technical efficiency or scale efficiency.Finally,Tobit regression was used to analyze the influence of internal factors of“first-class”universities on the efficiency of scientific research innovation.