Abstract: Using the panel data of China’s 30 provinces between 2002 and 2014, this paper firstly employs super-efficiency DEA model to measure the R&D efficiency of each province. Secondly, selecting the R&D expenditure input intensity and government funds on R&D as indicators to quantify the regional R&D fairness, we establish Markov chain model and put China’s regional R&D fairness and efficiency into the united analysis framework where we compare the Matthew effect of them and build up implementing paths of their coordinated development. The results indicate that the Matthew effect of R&D fairness is more pronounced. Those districts which get heavy R&D funds constantly get ample funds, whereas most districts are invariably suffering from the embarrassment of lack of R&D expenditure. Therefore, more importance should be attached to the problem of R&D fairness. This paper concludes that the government should focus more on the district of low-input but high-efficiency and increase R&D expenditure on them which can not only weaken the Matthew effect of fairness but can also maintain the coordinated development of the regional R&D fairness and efficiency in China.

摘要： 文章使用2002-2014年我国30个省市的面板数据，首先利用超效率DEA模型测算出各地区的R&D效率，并以R&D经费投入强度和政府R&D投入作为量化区域R&D公平的指标，随后构建Markov链模型将我国区域R&D公平与效率纳入统一分析框架，比较其马太效应大小，并构建二者协调发展的实施路径。研究结果表明，R&D公平的马太效应程度更加明显，那些享有高额R&D资金的地区始终获取充足的资助金额，而大部分地区却一直陷入R&D资金不足的窘迫局面，因此R&D公平问题更应该优先调节。本文认为政府应重点关注那些低投入高效率的省份，加大其R&D投入力度，既可以调节R&D公平的马太效应问题，又可保持我国区域R&D公平与效率的协调发展。