Spatial Differences In Innovation Activities In China And Its Influencing Factors

Based on previous research, we had a system on spatial differences in innovation activities in China. According to quantitatively analysis innovation status in each province, the main factors affecting innovation activities and efficiency, we can provide a strong empirical basis to decision-makers for developing appropriate technology policy and regional development planning, enhancing China's innovation level, especially improving the central and western areas of innovation to speed up the pace of development and narrow the gap with the eastern areas. We also provide an effective means for completion of our innovation-oriented country, and achieving the change from "Made in China" to "Created in China". This paper is composed by six parts.Chapter 1 mainly explains the background of this article, status, and the main content, research methods, innovations and limitations. Chapter 2 focuses on the meaning of innovation, innovation theory of the development process, as well as the source of innovation and tool: Knowledge Production Function (KPF). Chapter 3 firstly discusses the optimal measure of innovation output, and then compares the differences of innovation activities among regions from inputs and output. Chapter 4 firstly extends the KPF, and then analyzes the impacts of the East, Central and Western innovation, using 1998-2007 panel data. Chapter 5 analyzes the regional differences in efficiency of innovation, using Stochastic Frontier Analysis (SFA) model. Chapter 6 firstly summarizes empirical results of this article, and then gives advice on how to improve the regions' innovation capability.The innovation of this paper is as follows:firstly, Differently from the previous literature, this paper covers all the main factors (such as R&D and personnel inputs, knowledge stocks, industry clusters, and foreign technology spillovers, etc.) in KPF to study how they affect the innovation. The conclusions may be more objective in this way. Secondly, in order to more accurately describe some factors, we make a improvement in designing indicator. Thirdly, the number of patents should be used rather than patent applications as a measure of innovation output. Finally, combined panel data model with SFA, we analyzes the impact of regional innovation factors, as well as innovation efficiency differences.