| 国家创新发展绩效格局分析方法与实证研究 |
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| 引用本文： | 穆荣平,张婧婧,陈凯华.国家创新发展绩效格局分析方法与实证研究[J].科研管理,2020,41(1):12-21. |
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| 作者姓名： | 穆荣平 张婧婧 陈凯华 |
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| 作者单位： | 1.中国科学院科技战略咨询研究院，北京100190；
2.中国科学院大学公共政策与管理学院，北京 100049 |
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| 基金项目： | 国家社会科学基金;国家自然科学基金 |
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| 摘 要： | 创新发展已经成为世界潮流，全球创新发展绩效格局正在经历深刻变革，科学监测创新发展绩效格局演进方向是政府和学术界共同关注的问题。本文界定了“国家格局”、“格局分析方法”、“国家格局分析方法”等基本概念，研究提出了国家创新发展绩效格局二维分析方法，并选择40个国家创新发展指数和创新能力指数及其子指数排名进行了实证研究。本文主要得出三个结论：（1）基于国家创新能力和国家创新发展水平指数排名，采用K-均值聚类法将40个国家分为四类，即创新引领型国家、创新先进型国家、创新追赶型国家和非常规创新追赶型国家；（2）增加辅助特征量“人均GDP”以气泡图形式分析国家创新发展绩效格局的研究发现，经济发展水平（以人均GDP衡量）较高的国家，其创新发展指数与创新能力指数通常排名也较高，说明经济发展水平与国家创新发展绩效高度正相关；（3）分别增加辅助特征量“GDP”和“人均GDP”以气泡图形式分析国家创新能力格局的研究发现，经济规模（以GDP衡量）较大的国家，其创新实力指数排名通常较高，经济发展水平（以人均GDP衡量）较高的国家，其创新效力指数排名通常较高。
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| 关 键 词： | 格局 格局分析方法 国家创新发展绩效格局 国家创新能力格局 |
Methodological and empirical research on structure analysis of national innovation development performance |
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| Mu Rongping,Zhang Jingjing,Chen Kaihua.Methodological and empirical research on structure analysis of national innovation development performance[J].Science Research Management,2020,41(1):12-21. |
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| Authors: | Mu Rongping Zhang Jingjing Chen Kaihua |
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| Institution: | 1.Institutes of Science and Development, CAS, Beijing 100190, China;
2. School of Public Policy and Management, UCAS, Beijing 100049, China |
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| Abstract: | Innovation development has become a world trend, while global innovation development structure is undergoing profound change. How to scientifically monitor the direction of structure evolution of innovation development performance has attracted increasingly more and more attention from both governments and academia. This paper clearly defines “the structure among countries” as the position of each country located within certain spatial scale or the system composed of countries in terms of some variable(s), and “the methods for analyzing the structure” as the methods for analyzing the structure of some type of subject (e.g. country, region, industry, and company) within certain spatial scale (e.g. the whole world, Asia, Yangtze River Delta Region, Jiangsu Province) or the system (e.g. manufacturing industry, automotive industry, household appliances industry) at a particular point in terms of some variable(s) (i.e. indexes such as innovation development level, innovation capacity, and indicators such as R&D expenditure, number of patents), therefore we define “the methods for analyzing the structure among countries” as the methods for analyzing the structure of certain system(e.g. the whole world, Asia, OECD) composed of countries in terms of some variable(s).
Methods for analyzing the structure among countries can be categorized into one-dimensional and two-dimensional methods based on the number of variable(s). Auxiliary variables such as GDP or GDP per capita can be introduced to broaden policy implication of the structure analysis. We propose the methods for analyzing the two-dimensional structure of innovation development performance among countries, including: (i) the two-dimensional structure of innovation development performance among countries in terms of the innovation development index and the innovation capacity index, and (ii) the two-dimensional structure of innovation capacity among countries in terms of the two sub-indexes of innovation capacity, namely, the innovation strength index and the innovation effectiveness index, based on the clustering method, bisection (or trisection) method, and the balance line method. The clustering method is the method for classifying countries in terms of two variables based on a certain clustering algorithm as shown in Figure 1. Bisection (or Trisection) method is the method for classifying countries in terms of two variables based on the area where every country is located, as shown in Figure 2a and Figure 2b. The balance line method is the method for classifying countries in terms of two variables based on the distance between each country’s position and the balance line as shown in Figure 3a and Figure 3b.
We analyze the structure based on the ranking of the innovation development index, and the innovation capacity index as well as the sub-indexes of the innovation capacity index among 40 countries, and conclude as following.
(1) 40 countries were divided into four categories by using K-means clustering method in terms of the ranking of the innovation capacity index and the innovation development index. The first category is the leading countries of innovation, which consist of countries with high innovation development level and strong innovation capacity, i.e. United Kingdom, France , Japan, Switzerland, Sweden, Singapore, Norway, Netherlands, Denmark, Belgium, and countries with relatively higher innovation development level and strong innovation capacity with large economic scale, i.e. United States, Germany and South Korea. The second category is the advanced countries of innovation, which consist of countries with relatively higher innovation development level and relatively stronger innovation capacity, i.e. Austria, New Zealand, Australia, Canada, Spain, Italy, Greece, Czech, Chile, Portugal and Hungary. The third category is the catching-up countries of innovation, which consist of countries with relatively lower innovation development level and relatively weaker innovation capacity, i.e. Malaysia, Poland, Slovakia, Russia, Argentina, Brazil, Mexico, Romania, South Africa, Thailand and India. The fourth category is the unconventional catching-up countries of innovation, which consist of countries with a large gap between the ranking of the innovation development index and the ranking of the innovation capacity index, i.e. China and Turkey.
(2) The empirical research on the structure analysis of innovation development performance among countries in terms of the ranking of the innovation capacity index and the innovation development index, by introducing the auxiliary variables such as GDP per capita and GDP, shows that countries with higher level of economic development (measured by GDP per capita) usually have higher ranking of the innovation capacity index and the innovation development index, which implies that the level of economic development is highly positively related to the national innovation development performance, while countries belonging to the first category with larger economic scale (measured by GDP) usually have higher ranking of the innovation capacity index than ranking of the innovation development index.
(3) The empirical research on the structure analysis of innovation capacity among countries, by introducing the auxiliary variables such as GDP and GDP per capita, shows that countries with larger economic scale (measured by GDP) usually have higher ranking of the innovation strength index, while countries with higher level of economic development (measured by GDP per capita) usually have higher ranking of the innovation effectiveness index. |
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| Keywords: | the structure the methods for analyzing the structure the structure of innovation development performance among countries the structure of innovation capacity among countries |
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