科研管理

科研管理 ›› 2018, Vol. 39 ›› Issue (4): 55-63.

• 论文 • 上一篇    下一篇

中国31个省市科技创新效率评价及投入冗余比较

郭淑芬,张俊   

  1. 山西财经大学资源型经济转型协同创新中心，山西 太原030006
  • 收稿日期:2015-12-28 修回日期:2017-04-19 出版日期:2018-04-20 发布日期:2018-04-13
  • 通讯作者: 张俊
  • 基金资助:

    教育部新世纪优秀人才支持计划项目（教技函\[2012\]80号）。

Evaluation and Comparison of input redundancy for science-Technological innovation efficiency about Chinese 31 Provinces

Guo Shufen, Zhang Jun   

  1. Cooperative Innovation Center for Transition of Resource-based Economics, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China
  • Received:2015-12-28 Revised:2017-04-19 Online:2018-04-20 Published:2018-04-13

摘要： 深入分析科技创新资源投入的冗余情况，了解我国整体的科技创新效率及31个省市之间的差异，有利于科技创新资源的整合及科技创新效率的提高。文章基于道格拉斯生产函数，构建了包含科技劳动投入、资本投入、技术状况等的区域科技创新效率评价指标体系，并运用CCR-DEA、BCC-DEA和Malmquist-DEA模型测度了2009-2013年中国31个省市的科技创新效率及其年份变动情况，对31个省市的投入冗余进行了比较分类。研究发现，研究期完全有效省份的数量呈增多趋势，但总数仍远不及一半；5年来，基于技术进步各省市的科技创新效率有所提高；只有8个省份属于零冗余地区。当前阶段国家仍需进一步改革科技管理政策，引导区域科技资源优化配置；各省市则应基于当地科技资源冗余情况及资源禀赋，实施具有地方特色的科技创新政策。

关键词: 科技创新效率, Malmquist-DEA, 投入冗余, 省市差异

Abstract: Analyzing the redundancy of investment in S&T innovation deeply, and having a good understand of technological innovation efficiency and its differences of 31 provinces, can help us better use technological innovation resource and improve efficiency. This paper constructs technological innovation efficiency index system considering labor input，capital investment and technical conditions basing on Cobb-Douglas production function. And measures the technological innovation efficiency of 2009-2013 S&T innovation employing CCR-DEA、BCC-DEA and Malmquist-DEA models. And this paper also divides 31 provinces into several types according to the input redundancy. The results show that in our research period, the quantity of fully effective provinces are increasing but still less than a half. The efficiency of S&T innovation based on technological progress has been increased within 5years. What’s more, only 8 provinces are areas which have no inputs redundancy. For the nation, the further reformation of S&T management policies and optimization of S&T resources allocation are important. 31 provinces should conduct different policies of S&T innovation with local characteristics basing on S&T resources and its redundancy.

Key words: S&T innovation efficiency, Malmquist-DEA, input redundancy, provincial difference