指導老師 : 張菽萱 教授 研究生 : 鄭雯涵 (94312039)

1. Comparing regional innovative capacities of PR China-based on data analysis of the national patents 指導老師:張菽萱 教授 研究生:鄭雯涵 (94312039)

2. Abstract 1.The significant inequality of innovative capacities among regions will be a hindrance to the harmonious development of the overall national Science and Technology and Economy of PR china. 2.The results indicate that the R&D activities and engineers in enterprises, the governmental R&D funds for enterprises, are all inefficient to different degrees in the regional innovation systems of PR China.

3. Abstract 3.Firms’ spontaneous R&D investment on their own contributes substantially to their regional innovative capacities. 4.The results suggest that the methodology is a useful and relatively reliable way of measuring regional innovative capacities for specifically Chinese conditions.

4. Introduction 1.The critical issues facing policy-makers of regional governments, as well as of central government, are how to construct and improve the regional innovation systems, to enhance the harmonious development of the regional innovative capacities and further to impel the economic development with the benefit of progress in science and technology.

5. Introduction 2.Technological innovation is widely admitted to be indispensable for regional development. 3.Many studies found that technological innovation could made positive impacts on enhancing the competitiveness of firms.

6. Introduction 4.According to the concept of system of innovation, the regional innovative capacity is not only determined by innovative inputs, but to a great extent by the interaction among the actors involved in technology development. 5.We use the viewpoint of comparison and co-evolution to study the characteristics of regional innovation systems in PR China.

7. Indicators We use the number of national invention patent applications examined as indicators to measure the regional innovative capacity for PR China’s 30 provinces, autonomous regions and province-level municipalities. a. the outcome is closest b. temporal and geographical distribution c. advantages

8. Determinants 1.Inputs on innovation infrastructure a. scientists and engineers b. governments’ investment 2.Regional innovation milieu a. bank R&D loans to research institutes and enterprises 3.Cooperation among different institutions a. universities b. research institutions

9. Clusters We use the sum of national invention patent applications examined for the different regions over 16 years (from 1985 to 2000) to measure the regional innovative capacity. Using K-Means Cluster Analysis 1.High innovative capacity Beijing, Shandong, Shanghai, Guangdong,… 2.Middle innovative capacity Hunan, Henan, Shaanxi, Zhejiang,… 3.Low innovative capacity Shanxi, Yunnan, Hainan, Fujian,…

10. Synthesis determinant factors Taking principle component analysis (PCA): the principle component : the number of scientists and engineers in the research institute.

11. The relationship between regional innovative capacity and its determinants Taking ridge regression analysis 1.R&D personal inputs in universities contribute most substantially to the innovative capacity of RIS. 2.Research institutes and enterprises’ R&D personnel have the least contribution to RIS.

12. The relationship between regional innovative capacity and its determinants 3.The essential human resources of innovation are from universities and research institutes, not from industry. 4. The investment return of governmental R&D funds for universities and research institutes is much higher than that of governmental R&D funds for enterprises.

13. The relationship between regional innovative capacity and co-evolution of RIS 1.The regions for which the factor scores of the basic innovation resource factor are larger than those of enterprise innovation resource factor include 14 regions, namely, Beijing, Shannxi, Jilin, Tianjin, Guangxi,…… 2.The regions for which the factor scores of the enterprise innovation resource factor are larger than those of the basic innovation resource factor, include 8 regions, namely Liaoning, Shandong, Jiangsu, Guangdong, Sichuan,…

14. The relationship between regional innovative capacity and co-evolution of RIS 3.The regions for which the factor scores of the enterprise innovation resource factor become larger and larger with time elapsed and eventually exceed those of the basic innovation resource factor include 6 regions, namely Shanghai, Heilongjiang, Zhejiang, Anhui, Fujian and Shanxi. 4.Tne regions for which the factor scores of the basic innovation resource factor are larger than those of the enterprise innovation resource factor, include Hunan and Jiangxi.

15. conclusions 1.Governments at all levels should change their emphasis from direct R&D investment in enterprises to such indirect stimulation as tax credits; reduce the intervention in enterprises’ R&D activities and encourage them with market and economical levers. 2.It is also important to improve the regional innovation milieu, to inspire R&D cooperation among enterprises, research institutes and universities and make the transfer of knowledge and S&T results from research institutes and universities to enterprises in more efficient and effective manners.

16. conclusions 3.It is urgent to turn bank R&D loans into true venture investment funds. Banks will share risks with research institutes and enterprises. 4.In summary, reducing direct government investment in enterprises and fostering the sound innovation milieu are essential to regional governments, particularly to those governments of underdeveloped regions, in order for them to constitute the regional economic and S&T policies.