中国区域创新效率的收敛性研究:基于空间经济学视角

Research on Convergence of Regional Innovation Efficiency in China: Based on the Perspective of Spatial Econometric

基于随机前沿分析方法,从产出角度出发,构建效率函数对中国各地区的创新效率进行了测度,然后运用2000—2011年期间中国30个省份的面板数据,建立空间面板数据模型考察了中国区域创新效率的收敛性。研究结果表明:中国的区域创新效率在整体上呈现出不断上升的趋势,但也表现出较大的区域差异性;空间自相关Moran's I检验显示,中国的区域创新效率在空间上存在显著的空间自相关性,具有明显的集群趋势;加入空间效应后,2000—2011年期间中国的区域创新效率不仅存在着绝对?收敛,而且存在条件?收敛,并且人力资本、产业结构、金融发展水平和政府干预等均是影响区域创新效率收敛的重要因素,但外商直接投资对区域创新效率收敛的影响则表现为不明显。

Achieving the transition in economy development levels and promoting balanced economic development across regions are now important issues in China. China＇s economic development differences lie in that the economy in the east region is better than that in the west region, and competitive gap between the east and the west is magnifying. The reason behind the phenomena is caused by the issue of regional innovation capability differences. Regional innovation is the fundamental driving force which promotes economic development. However, regional innovation capability is mainly presented as a regional innovation efficiency issue. Nowadays, China＇s regional innovation efficiency has resulted in significant regional disparities. Regional innovation efficiency in the east is significantly higher than that in the central and the west. The innovation efficiency differences among the three regions are increasing, which has a very negative impact on the balanced development of the regional economy. Therefore, scholars in the field of scientific technological innovation focus on findings ways to improve innovation efficiency, explore intrinsic impact mechanism to narrow the gap between different regions, and promote the convergence of regional innovation efficiency. Firstly, the research adopts SFA method and production function model to measure the efficiency of regional innovation based on the panel data collected in China from year 2001 to 2012. Most data come from China Statistical Yearbook and the Yearbook of Science and Technology of China. The measurement results show that the efficiency of regional innovation in China presents not only an upward trend, but also a large regional disparity. The average of innovation efficiency in the east is the highest, followed by the west, and the central. Secondly, the paper uses spatial correlation index Moran＇s 1 to reflect the presence of spatial correlation in the regional innovation efficiency. Statistical results of Moran＇s ! show that Chinese regional innovation efficiency has a significant spatial autocorrelation in space and turns out to be in clusters. This finding indicates that there is a clear space overflows and diffusion effects on regional innovation behavior in China. Spatial effects problem should not be ignored when we analyze the convergence of regional innovation efficiency in China. At last, we build spatial convergence model to study the convergence of regional innovation efficiency in China. The diagnostic test results of econometric model show that compared with mix mode space fixed mode and time fixed effects model, and two-way fixed effects model is found to be better. Spatial autoregressive model （SAR） is more suitable for model estimation on the convergence of Chinese regional innovation efficiency than that of spatial error model （SEM）. The empirical results of SAR model show that after spatial effects are added, it not only has absolute β convergence, but also has conditional β convergence in China＇s regional innovation efficiency from year 2000 to 2011. Human capital, industrial structure, the level of financial development and government intervention are the important factors that influence the convergence of regional innovation efficiency in China, but FDI has no significant effect on it.