关键词: 创新体系效能, Dagum基尼系数, β收敛, 时空演化

Abstract: Under the new development pattern, China is faced with huge challenges such as international instability, frequent health incidents, and fading demographic dividend. The economic development model driven by traditional factors is not sustainable. Innovation-driven development has gradually become an important source of high-quality economic development. How to effectively accelerate the construction of innovation system is the fundamental way to improve national innovation ability and competitiveness. Since the CPC Central Committee formally put forward the resolution to comprehensively promote the construction of the national innovation system with Chinese characteristics, the construction of the national innovation system has achieved considerable results. According to the statistics of the World Intellectual Property Indicators, China ranks the first in the world in the total number of R&D personnel and applications for patents, trademarks and industrial appearance. However, the rapid growth of individual innovation indicators does not mean that the national innovation system efficiency will be improved simultaneously. China still has a series of problems, such as poor coordination of innovation entities, scattered allocation of innovation resources and low quality of innovation output. Therefore, how to evaluate national innovation system efficiency scientifically has become a practical problem to be solved urgently. Unfortunately, the academic circle's understanding of this issue is still very limited, and the current evaluation of national innovation system efficiency is still lagging behind the practical needs of the construction of an innovative country. Under such circumstances, systematically constructing the index of the national innovation system efficiency and accurately describing its evolution trend, spatial distribution, regional differences and convergence characteristics are of great practical significance for objectively understanding the operation status of the national innovation system, systematically improving the overall efficiency of the innovation system, and accelerating the construction of an innovation-oriented country.
Based on the framework of “foundation-process-result”, this paper constructs a comprehensive index system of national innovation system efficiency from five aspects: innovation basis, innovation environment, innovation synergy, innovation openness and innovation quality. And this paper uses the global principal component analysis method and relevant data from 2009 to 2020 to measure the index of China's innovation system efficiency at the provincial level. Furthermore, the Dagum Gini coefficient and β convergence model are used to empirically analyze the spatial-temporal evolution trend, spatial differences and sources, convergence characteristics and speed of innovation system efficiency in China and the three regions. The results show that: First, the comprehensive level of national innovation system efficiency is not high. And there are serious problems of unbalanced, inadequate and uncoordinated spatial distribution, which is manifested as the gradient characteristics of “eastern region>central region>western region”. There is still a large room for improving national innovation system efficiency in the country and the three regions, and all of them show a steadily rising growth trend. Second, the spatial differences of national innovation systems efficiency show a fluctuating upward trend, and the intra-group differences and inter-group differences also present different increases in the three regions. The inter-group differences are always the main source of spatial differences, and the intra-group differences in the eastern region and the inter-group differences between the eastern and western regions are relatively large. Third, innovation system efficiency in China and the three regions all have typical characteristics of absolute and conditional β convergence, and the convergence speed in the western region is higher than that in the eastern and central regions. In the conditional β convergence after the introduction of control variables, the convergence rate of innovation system efficiency in the country and the three regions is significantly improved, and the convergence rate of the central and western regions is obviously faster than that of the eastern regions. Based on the research results, this paper puts forward policy implications from strengthening the construction of the national innovation system, paying attention to the spatial imbalance of innovation system development, and grasping the driving role of economic and social factors.

Key words: innovation system efficiency, Dagum Gini coefficient, β convergence, space-time evolution