INNOVATION, INNOVATION POLICIES, AND REGIONAL DEVELOPMENT IN CHINA

Abstract

Innovation policies have become an important element in national and regional innovation systems. This study presents a framework to analyze innovation policies and outputs, in terms both of knowledge and economic benefits, in China, with a focus on Jiangsu. We classify local innovation policies into seven major categories and twenty-one subcategories as well as five categories of national policies based on their characteristics. We find that main local policies vary largely depending on the level of economic development, and the spatial evolution of national policies is not homogenous, concentrated in the relatively more -developed region of southern Jiangsu (Sunan). Our analysis using a vector error correction model shows that the time lag effect of various policies on economic output is longer than on knowledge output, and cumulative local policies have a significant impact on both types of outputs. The model also confirms the important roles of innovation subjects, intellectual property, and talents related policies on outputs. Last, the questionnaire identifies the reasons for unsatisfactory implementation of policies, such as low level of policy awareness, insufficient financial support, and imperfect innovation and entrepreneurial ecological environment. Our study reveals innovation policies in China favor more-developed regions, which reinforces the digital divide and spatial polarization. Our study suggests that China’s regional innovation policies need to be better integrated, should pay more attention to less-developed regions, and improve the role of the market in the allocation of innovation resources.

Keywords:

• technological innovation
• innovation policies
• regional innovation system
• regional development
• China

Acknowledgment

We would like to acknowledge the funding of the National Natural Science Foundation of China (No. 41701136, No. 41671133, No. 41471103, No. 41501138), and the 60th batch of China Postdoctoral Science Foundation (2016M602233). We would also like to thank Feng Yuan, Jinlong Gao and Honglian Xu for their research assistance.