科技创新的要素投入及其绩效研究

发布时间：2018-08-06 08:20

【摘要】：创新是人类活力的源泉。在传统的粗放式要素投入驱动经济增长的动力日渐衰退的当下,以科技创新为内核的经济增长新动力的构筑愈发重要。如何提高科技进步对经济增长的贡献率是亟待解决的关键问题。因此,本文在总结相关科技创新研究成果的基础上,从科技创新要素投入出发,理清创新驱动阶段我国科技创新要素投入的组成并阐述其驱动经济增长的作用机理,进一步分析我国制造业层面及省域层面科技创新要素投入的绩效情况。本文首先以中国制造业行业为研究对象并按技术水平高低将其分类,基于投入-产出分析框架、采用数据包络分析法估算2008年至2015年各制造业行业科技创新要素投入的绩效值,并进行比较和趋势分析。其次,在运用Malmquist指数法测算中国30个省、直辖市和自治区的全要素生产率增长率的基础上,利用2004-2015年各省域面板数据,采用系统GMM方法,将科技创新要素投入细分为资源性要素和环境要素,具体分析其对经济增长质量的影响。本文研究发现,在研究期内制造业整体综合效率均值为0.787、纯技术效率均值为0.853、规模效率均值为0.911,距离完全效率值1有一定的差距,制造业整体科技创新要素投入的绩效水平不高,仍有较大的提升空间,而低水平的纯技术效率值阻碍了制造业整体综合效率水平的提升;在研究期内综合效率均值由高到低依次是低技术制造业、中低技术制造业、高技术制造业、中高技术制造业,低技术制造业科技创新要素投入的绩效水平保持领先,但高技术制造业绩效水平增长较快且攀升幅度大。在省域层面,资源性要素投入中研发资本存量对全要素生产率增长存在显著的促进作用而研发人力资本却存在负向影响;在环境要素中,软件环境要素中创新意识、技术市场发育程度、市场开放程度以及风险投资支持程度均对全要素生产率增长存在显著正面影响,而硬件环境要素以及软件环境要素中的教育水平、产学研合作发展水平则不利于全要素生产率的增长。基于中国科技创新发展的现实与上述分析,本文展望未来创新驱动经济增长动力的构筑,为科技创新要素投入绩效水平的进一步提升、创新能力的增强提出相关政策建议。
[Abstract]:Innovation is the source of human vitality. At a time when the traditional extensive factor input drives the economic growth, it is more and more important to construct the new driving force of economic growth with scientific and technological innovation as the core. How to improve the contribution rate of scientific and technological progress to economic growth is a key problem to be solved. Therefore, on the basis of summarizing the related scientific and technological innovation research results, this paper starts from the input of the scientific and technological innovation elements, clarifies the composition of the scientific and technological innovation elements input in the innovation driving stage and expounds its function mechanism of driving the economic growth. Further analysis of the performance of scientific and technological innovation factor investment at the manufacturing level and provincial level in China. Based on the input-output analysis framework, this paper uses the data envelopment analysis method to estimate the performance of scientific and technological innovation factors in the manufacturing industry from 2008 to 2015. Comparison and trend analysis were carried out. Secondly, on the basis of measuring the growth rate of total factor productivity of 30 provinces, municipalities and autonomous regions in China by using Malmquist index method, and using the panel data of provinces from 2004 to 2015, a systematic GMM method is used to measure the total factor productivity growth rate of 30 provinces and municipalities directly under the Central Government. The input of science and technology innovation is subdivided into resource factor and environment factor, and its influence on the quality of economic growth is analyzed in detail. In this paper, it is found that during the period of study, the average overall efficiency of manufacturing industry is 0.787, the average of pure technical efficiency is 0.853, the mean of scale efficiency is 0.911, and there is a certain gap between the total efficiency of manufacturing industry and the value of complete efficiency. The performance level of scientific and technological innovation elements in manufacturing industry is not high, and there is still a large room for improvement, while the low level of pure technical efficiency hampers the overall efficiency level of the manufacturing industry. In the period of study, the average value of comprehensive efficiency from high to low is low technology manufacturing industry, low and middle technology manufacturing industry, high technology manufacturing industry, middle and high technology manufacturing industry, low technology manufacturing industry, and the performance level of scientific and technological innovation factor input of low technology manufacturing industry keeps ahead. However, the performance level of high-tech manufacturing industry has increased rapidly and by a large extent. At the provincial level, R & D capital stock has a significant promoting effect on total factor productivity growth in resource factor input, while R & D human capital has a negative impact on total factor productivity growth, and innovation consciousness in software environment element in environmental element, R & D human capital in R & D human capital has a negative impact on total factor productivity growth. The degree of technology market development, the degree of market opening and the degree of venture capital support have significant positive effects on the total factor productivity growth, while the education level of hardware environment factor and software environment factor. The level of cooperation between industry, college and research is not conducive to the growth of total factor productivity. Based on the reality of the development of scientific and technological innovation in China and the above analysis, this paper looks forward to the construction of the driving force of economic growth driven by innovation in the future, and puts forward relevant policy recommendations for the further improvement of the investment performance level of scientific and technological innovation elements and the enhancement of innovation ability.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F224;F424.3
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本文编号：2167111