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全面客观评价区域创新系统的绩效是分析和制定创新政策的基础。区域创新系统是把创新人力资源和财力资源投入转化为创新产出的经济系统,数据包络分析(DEA)是评价区域创新系统(称为决策单元)相对有效性的常用评价方法。传统DEA模型将决策单元分为有效的和无效的两类,但存在两个主要问题:(1)由于有效决策单元的效率均为1,故不能进一步区分有效决策单元;(2)不再对无效决策单元进一步分层。为解决这两个问题,本文结合超效率DEA模型和背景依赖DEA模型,在对有效前沿面进行分层的基础上,引入超效率、吸引值和改进值这三个评价准则,全方位评价我国31个省市的区域创新系统的绩效。其中,超效率是基于同一层次有效前沿面来评价各决策单元的,对产出型超效率模型,决策单元的超效率(≤1)越小,绩效越好。吸引值是选择较低层次的有效前沿面作为评价背景,吸引值越大,决策单元距离评价背景越远,表明其绩效更好。改进值是基于较高层次有效前沿面作为评价背景,决策单元的改进值越大,距离评价背景越远,预期需要做更大的改进,因此,较小的改进值意味着较好的绩效。本文假定区域创新系统从投入到产出的延迟时间为一年,评价采用的具体投入数据为:(1)2012年区域创新系统R&D支出(亿元);(2)2012年地方财政科技拨款(亿元);(3)2012年区域创新系统R&D人员(千人年)。具体产出数据为:(1)2013年国内中文期刊科技论文数(篇);(2)2013年高科技产业主营业务收入(亿元);(3)2013年高技术产品出口额(百万美元);(4)2013年发明专利申请授予量(项)。实证分析结果显示:(1)4个层次的有效前沿面构成了我国31个区域创新系统的基准结构;(2)同一有效前沿面层次上的区域创新系统可根据超效率相互比较其竞争优势;(3)领先有效前沿面层次上的区域创新系统可依据较低的改进值来识别未来的竞争者;(4)具有较高吸引值的区域创新系统可用作后进有效前沿面层次上的区域创新系统的学习目标,从而设立逐步改进的最佳路径。本研究结果有助于相关决策者对区域创新系统做出准确的定位,进而制定出正确的区域创新的战略与政策。
A comprehensive and objective assessment of the performance of a regional innovation system is the basis for analyzing and formulating an innovation policy. Regional innovation system is an economic system that transforms the input of innovative human resources and financial resources into innovative output. Data envelopment analysis (DEA) is a commonly used evaluation method to evaluate the relative validity of regional innovation system (called decision-making unit). The traditional DEA model divides the decision-making units into two types: effective and ineffective. However, there are two main problems: (1) because the effective decision-making units are all 1, the effective decision-making unit can not be further distinguished; (2) Invalid decision-making unit further stratified. In order to solve these two problems, based on the super efficiency DEA model and the background dependent DEA model, this paper introduces three evaluation criteria of super efficiency, attraction value and improvement value based on the stratification of the effective frontier. Regional Innovation System Performance in 31 Provinces and Cities. Among them, super-efficiency is based on the same level of effective frontier to evaluate decision-making unit. For super-efficiency model, the super efficiency (≤1) of decision-making unit is smaller, and the performance is better. The attractive value is to select the effective frontier of the lower level as the evaluation background. The larger the attraction value is, the farther the decision-making unit is from the background of the evaluation, indicating that the performance is better. The improvement value is based on the higher-level effective frontier as the evaluation background. The greater the improvement value of the decision-making unit, the greater the distance from the evaluation background and the greater improvement is expected. Therefore, the smaller improvement value means better performance. This paper assumes that the delay of the regional innovation system from input to output is one year. The specific input data used in the evaluation are as follows: (1) R & D expenditures of regional innovation system in 2012 (100 million yuan); (2) 100 million yuan); (3) Regional innovation system R & D personnel in 2012 (thousands of years). The specific output data are as follows: (1) The number of scientific papers in domestic Chinese journals in 2013 (articles); (2) The revenue from main business of high-tech industries in 2013 (100 million yuan); (3) The export of high-tech products in 2013 Million US dollars); (4) The amount of invention patent applications granted in 2013 (items). The results of empirical analysis show that: (1) The effective frontier of 4 levels constitutes the benchmark structure of 31 regional innovation systems in China; (2) The regional innovation systems at the same effective frontier level can compare their competitive advantages with each other according to super efficiency; (3) A leading innovation frontier level regional innovation system can identify future competitors based on lower improvement values; (4) A regional innovation system with higher attractiveness value can be used as a backward efficient frontier level area Innovative system of learning objectives, so as to establish the best way to gradually improve. The results of this study will help the relevant decision makers to accurately locate the regional innovation system and formulate the correct strategies and policies for regional innovation.