中国高技术产业动态效率测度与收敛性分析:基于产出导向DSBM模型的实证

The Dynamic Efficiency and Convergence Hypothesis of High-tech Industries in China: An Empirical Analysis by Output-Oriented DSBM Model

本文以2004-2012年中国28个省份的面板数据为样本,使用DSBM模型测度省际高技术产业的整体效率和时期效率,比较省际高技术产业动态效率的区域差异并对其进行收敛性检验。研究发现,我国高技术产业整体效率处于较低水平,东部高技术产业效率显著高于中部和西部,省际高技术产业效率差距较大;全国和东部、中部与西部三大经济区域高技术产业效率整体呈现上升趋势,但金融危机后上升趋势有所放缓;全国及东部和中部高技术产业效率存在俱乐部收敛特征,但西部高技术产业效率只存在条件收敛特征。

Efficiency is a concentrated embodiment of the core competitiveness of high-tech industry. The efficiency evolution of Chinese high-tech industry has become a focus of academic attention. At present, the academia primarily use two methods to measure the high-tech industry efficiency： parametric method and non-parametric method. Innovation of high-tech industry is a dynamic process, and the overall efficiency is determined by series of period efficiencies. However, the existing literature fail to deal properly with the changing of high-tech industry efficiency in continuous time. The dynamic slack-based measure（DSBM） approach was proposed by Tone and Tsutsui in 2010, and became an effective method to measure the high-tech industry efficiency in a specific period. The first part introduced the DSBM model and discussed the convergence test method. In this paper, the DSBM model is designed to measure the output-oriented dynamic efficiency of decision-making units in the condition of variable returns-to-scale. In order to analyze whether the industrial efficiency gap in different regions has gradually reduced over time, the existing literatures generally divided convergence analysis methods into three types： absolute σ convergence, absolute β convergence and conditional β convergence. In the second part, the selected research variables and the sample data source were described. The patent authorization number of China high-tech industry was selected as the intermediate products index, and the sales revenue of new products as economic results, the number of RD FTE staff as human capital input indicators, RD internal expenditure as research capital input indicators, RD investment in fixed assets as physical capital input indicators, and the number of high-tech industry companies as the degree of industry competition. In the third part, the high-tech industry innovation efficiencies were calculated and analyzed. The data were collected from 28 provinces and three major economic regions in China during 2004-2012. The analysis results showed that compared with the optimal efficiency, the overall efficiency of China＇s high-tech industry is at a low level and there is 64.4 percent room for promotion. The difference of high-tech industry efficiency in different provinces is obvious. The high-tech industry efficiency in the eastern region is significantly higher than that in the central and western regions. The evolution trends of high-tech industry efficiency in the nationwide and the three major economic regions are consistent and upward on the whole. However, after the impact of the financial crisis, the upward trend has slowed down. In the fourth part, the convergence of high-tech industry efficiencies in the three major economic regions was tested and analyzed. The results showed that the high-tech industry efficiencies in the nationwide, eastern region and central region have obvious characteristics of σ convergence, absolute β convergence and conditional β convergence, which indicates that there is a catch-up effect and common steady-state level of high-tech industry efficiencies in the nationwide, eastern region and central region. The high-tech industry efficiency in the western region has no obvious characteristics of σ convergence and absolute β convergence, but only characteristic of conditional β convergence. These findings indicate that the high-tech industry efficiency in the western region maintains its own steady state level. The last section of the paper made proposals about formulating different policies and measures according to the efficiency difference and convergence dynamics. These proposals can help promote the development of the western and central regions, as well as the revitalization of the northeast region. Moreover, these proposals can help enhance the cooperation of the high-tech industry in different regions in the areas of talent and technology management.