文章摘要
WANG Weiguo,WANG Xinpeng.Innovation Transformation Efficiency, Factor Endowments, and China's Economic Growth[J].The Journal of quantitative and technical economics,2022,(12):5-25
创新转化效率、要素禀赋与中国经济增长
Innovation Transformation Efficiency, Factor Endowments, and China's Economic Growth
  
DOI:
中文关键词: 创新转化效率  要素禀赋  三阶段  DEA动态面板门槛模型
英文关键词: Innovation Transformation Efficiency  Factor Endowments  Three-stage DEA  Dynamic Panel Threshold Model
基金项目:本文获得国家社会科学基金重大项目“供给侧结构性改革下东北地区创新要素结构分析与优化对策研究”(18ZDA042)、国家自然科学基金项目“省际能源消费的变系数非参空间面板数据模型研究”(71773012)、国家自然科学基金项目“基于大数据计量方法的中国人口政策评估与优化研究”(72273019)的资助。
Author NameAffiliation
WANG Weiguo School of Economics, Dongbei University of Finance and Economics 
WANG Xinpeng School of Economics, Dongbei University of Finance and Economics 
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中文摘要:
      党的二十大报告指出高质量发展是全面建设社会主义现代化国家的首要任务。研究中国各地区从“要素驱动”向“创新驱动”转换的动态分布特征,对于促进中国经济高质量发展具有重要的理论和现实意义。鉴于此,本文首先构建理论模型分析创新转化效率、要素禀赋对经济增长的影响机制,同时采用修正后的三阶段DEA模型和熵权—TOPSIS方法,分别测算2003~2016年中国281个城市创新转化效率和要素禀赋。在考虑内生性问题的情况下,使用动态面板门槛模型对理论分析得到的结论进行验证。研究发现,中国各城市创新转化效率差异较大,在考虑环境变量等因素后,综合技术效率下降。创新转化效率和要素禀赋对经济增长的影响存在门槛效应,要素驱动向创新驱动转换过程中,经济增长速度放缓。中国大部分城市动能转换的动态分布特征是从要素驱动型区域进入结构转换型区域,其中小部分城市最终到达创新驱动型区域。中国东部省份多数城市动能转换类型为创新引领型和创新突破型,大多数创新滞后型城市分布在中、西部省份。
英文摘要:
      The 14th Five-Year Plan period is the first five years of China's efforts to achieve its second century goal, and it is an important period for China to carry out the conversion of new and old driving forces and upgrade its economic structure. In the context of the innovation-driven development strategy, it is of great theoretical and practical significance to study the dynamic distribution characteristics of the conversion from “factor-driven” to “innovation-driven” in various regions of China to promote the high-quality development of China's economy. In view of this, this study first constructs a theoretical model to analyze the mechanism of the impact of innovation transformation efficiency and factor endowment on economic growth. The model covers factors of production, R&D factors, innovation transformation processes, factor flows and endowment structures, intersectoral utility levels, and output growth. The revised three-stage data envelopment analysis (DEA) model and entropy-TOPSIS method are used to measure the innovation transformation efficiency and factor endowment of 281 cities in China from 2003 to 2016. The conclusions obtained from the theoretical analysis are validated using a dynamic panel threshold model, considering endogeneity issues.
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