基于理想窗宽的DEA视窗分析模型的我国高校科研评价
发布时间：2018-08-26 10:21
【摘要】：高等学校是我国科研活动的重要力量，尤其在基础研究活动中占有重要地位，我国政府对高校的科研创新一直都很重视。作为一个发展中国家，在国内纷纷提出建设世界一流大学的时代背景下，尤其在当前国家急需创建科技创新体系的形势下，如何充分利用我国高校有限的科研资源，提高我国高校在全球的学术竞争力，建设世界一流的大学，对高校的科研效率进行科学合理的评价显得尤为重要和迫切。 科研评价方法有很多种，考虑到高校科研活动具有多投入多产出的特征，以及数据包络分析方法（Data Envelopment Analysis, DEA）在解决多投入多产出的综合评价问题所具有的优点，国内外许多学者都选择了DEA方法进行研究，取得了许多研究成果。但是，已有的研究成果都忽视了一个问题——大部分对高校的科研效率评价都是基于传统DEA模型，而传统DEA模型则隐含假设“投入产出发生在同一个时间段”，即高校当年投入当年得到产出，这显然与“高校科研投入产出分布在多个时间段”的现实情况不相符，即高校的科研经费是分多个年份拨入高校，并且高校的科研成果在连续多年里按照科研进度分阶段发表。此外，在现实的许多研究中，需要经常对决策单元（Decision MakingUnit, DMU）进行多个时期的考察，在此基础上进行面板数据分析来研究DMU的效率的波动和变化情况，一些学者选择应用DEA视窗分析模型（WindowAnalysis, WA）来进行动态分析。然而，在应用DEA视窗分析模型进行动态分析的研究过程中，仍然存在一些问题：学者基于主观选择的不同窗宽所得出的结果存在比较大的偏差；不同窗宽的结果存在偏差，如何确定理想的窗宽使得偏差最小；投入产出之间存在一定的滞后期，而滞后期对于视窗分析结果存在比较大的影响，这一点在已有的研究成果中都没有引起重视。针对上述这些问题，引出了本文所研究的问题。 由于已有研究成果的科研评价指标比较零散，并且主观选择的随意性比较大，不能够较全面的涵盖高校科研活动，具有一定的片面性。所以，在第一章介绍国内外DEA模型研究现状的基础上，本文在第二章基于科研评价指标体系的建立标准和构建步骤，建立了我国高校的科研评价指标体系；其次，在第三章，基于我国高校的科研评价指标体系，应用DEA视窗分析模型，对我国高校在2003-2007年、2003-2008年和2003-2009年的科研效率进行动态评价，并对不同窗宽的分析结果进行了对比分析；在第四章，通过对比不同窗宽的分析结果，揭示了“窗宽的确定”对DEA视窗分析模型结果有重要影响，先从理论上进行分析，，研究确定理想窗宽，建立理想窗宽的DEA视窗分析模型，对我国高校多年的科研效率进行动态分析；最后在第五章，增加考虑高校科研投入产出的滞后期，基于理想窗宽的DEA视窗分析模型，定量分析滞后期对我国高校科研效率的影响。 研究结果表明：随着窗宽的增加，我国各省市高校的技术效率（TechnicalEfficiency, TE）和纯技术效率（Pure Technical Efficiency, PE）呈现递减的变化趋势，而规模效率（Scale Efficiency, SE）呈不规律的变化；科研效率（包括TE，PE和SE）在不同年份对窗宽变化的敏感程度不同，即在某些年份，科研效率值对窗宽的变化比较敏感，表现为不同窗宽下的效率值相差比较大，在某些年份，科研效率值对窗宽的变化不敏感，表现为不同窗宽下的效率值相差不大；选择不同的窗宽会使得研究者对科研效率的长期变化趋势得出大相径庭的结论。通过对比理想窗宽的DEA视窗分析模型与传统DEA模型的分析结果，发现两者之间存在很大的偏差，这进一步说明了建立新的DEA视窗分析模型，对我国高校科研效率进行动态分析是很有必要的，此DEA视窗分析模型的分析结果更符合科研工作的实际情况。考虑滞后期与不考虑滞后期的分析结果存在很大的偏差，说明滞后期对我国高校科研效率评价具有很大的影响，在高校科研评价中必须引起重视。本文基于理想窗宽的DEA视窗分析模型的分析结果，对我国高校的科研评价提出了一些对策建议。
[Abstract]:Colleges and universities are an important force in our country's scientific research activities, especially in basic research activities. Our government has always attached great importance to the scientific innovation of colleges and universities. Under the situation, how to make full use of the limited scientific research resources of our universities, improve the academic competitiveness of our universities in the world, and build a world-class university, it is particularly important and urgent to evaluate the scientific research efficiency of universities scientifically and rationally.
There are many evaluation methods for scientific research. Considering the characteristics of multi-input and multi-output of scientific research activities in Colleges and universities, and the advantages of Data Envelopment Analysis (DEA) in solving the problem of multi-input and multi-output comprehensive evaluation, many scholars at home and abroad have chosen DEA method for their research, and have made a lot of research. However, the existing research results have ignored a problem - most of the evaluation of scientific research efficiency in universities are based on the traditional DEA model, while the traditional DEA model implicitly assumes that "input and output occur in the same period of time", that is, the University input in the same year is output, which is obviously in line with "the distribution of university scientific research input and output in the same period of time." The reality of "multiple time periods" is not consistent, that is, the research funds of universities are allocated to universities in different years, and the scientific research achievements of universities are published in stages according to the progress of scientific research for many years in succession. On the basis of panel data analysis to study the fluctuation and change of DMU efficiency, some scholars choose to use the DEA window analysis model (WA) for dynamic analysis. The results obtained from the same window width have larger deviations; the results from different window widths have deviations; how to determine the ideal window width to minimize the deviation; there is a certain lag period between input and output, and lag period has a greater impact on the analysis results of the window, which has not been paid attention to in the existing research results. In view of the above problems, the problems in this paper are introduced.
Because of the scattered evaluation indexes of existing research results and the arbitrariness of subjective selection, it can not cover the scientific research activities of colleges and universities comprehensively, so it has certain one-sidedness. Secondly, in the third chapter, based on the scientific research evaluation index system of Chinese universities, the research efficiency of Chinese universities in 2003-2007, 2003-2008 and 2003-2009 is dynamically evaluated by using DEA window analysis model, and the results of different window widths are analyzed. In the fourth chapter, by comparing the analysis results of different window widths, it is revealed that the determination of window widths has an important influence on the results of DEA window analysis model. Finally, in the fifth chapter, considering the lag period of the input and output of university scientific research, the influence of lag period on the efficiency of university scientific research in China is analyzed quantitatively based on the DEA window analysis model with ideal window width.
The results show that with the increase of window width, the technical efficiency (TE) and pure technical efficiency (PE) of universities in various provinces and cities in China show a decreasing trend, while the scale efficiency (SE) shows an irregular change; the research efficiency (including TE, PE and SE) varies with the window width in different years. In some years, the efficiency of scientific research is more sensitive to the change of window width. In some years, the efficiency of scientific research is not sensitive to the change of window width. In some years, the efficiency of scientific research is not sensitive to the change of window width. By comparing the results of the DEA window analysis model with the traditional DEA model, it is found that there is a great deviation between the two models. This further shows that it is necessary to establish a new DEA window analysis model for the dynamic analysis of scientific research efficiency in Chinese universities. The results of this DEA window analysis model are more in line with the actual situation of scientific research. Considering the lag period and not considering the lag period, there is a great deviation in the analysis results, which shows that the lag period has a great impact on the evaluation of scientific research efficiency in China's universities and must be paid attention to in the evaluation of scientific research in universities. The analysis results of the model provide some countermeasures and suggestions for the scientific research evaluation of universities in China.
【学位授予单位】：哈尔滨工业大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：G644
本文编号：2204561
[Abstract]:Colleges and universities are an important force in our country's scientific research activities, especially in basic research activities. Our government has always attached great importance to the scientific innovation of colleges and universities. Under the situation, how to make full use of the limited scientific research resources of our universities, improve the academic competitiveness of our universities in the world, and build a world-class university, it is particularly important and urgent to evaluate the scientific research efficiency of universities scientifically and rationally.
There are many evaluation methods for scientific research. Considering the characteristics of multi-input and multi-output of scientific research activities in Colleges and universities, and the advantages of Data Envelopment Analysis (DEA) in solving the problem of multi-input and multi-output comprehensive evaluation, many scholars at home and abroad have chosen DEA method for their research, and have made a lot of research. However, the existing research results have ignored a problem - most of the evaluation of scientific research efficiency in universities are based on the traditional DEA model, while the traditional DEA model implicitly assumes that "input and output occur in the same period of time", that is, the University input in the same year is output, which is obviously in line with "the distribution of university scientific research input and output in the same period of time." The reality of "multiple time periods" is not consistent, that is, the research funds of universities are allocated to universities in different years, and the scientific research achievements of universities are published in stages according to the progress of scientific research for many years in succession. On the basis of panel data analysis to study the fluctuation and change of DMU efficiency, some scholars choose to use the DEA window analysis model (WA) for dynamic analysis. The results obtained from the same window width have larger deviations; the results from different window widths have deviations; how to determine the ideal window width to minimize the deviation; there is a certain lag period between input and output, and lag period has a greater impact on the analysis results of the window, which has not been paid attention to in the existing research results. In view of the above problems, the problems in this paper are introduced.
Because of the scattered evaluation indexes of existing research results and the arbitrariness of subjective selection, it can not cover the scientific research activities of colleges and universities comprehensively, so it has certain one-sidedness. Secondly, in the third chapter, based on the scientific research evaluation index system of Chinese universities, the research efficiency of Chinese universities in 2003-2007, 2003-2008 and 2003-2009 is dynamically evaluated by using DEA window analysis model, and the results of different window widths are analyzed. In the fourth chapter, by comparing the analysis results of different window widths, it is revealed that the determination of window widths has an important influence on the results of DEA window analysis model. Finally, in the fifth chapter, considering the lag period of the input and output of university scientific research, the influence of lag period on the efficiency of university scientific research in China is analyzed quantitatively based on the DEA window analysis model with ideal window width.
The results show that with the increase of window width, the technical efficiency (TE) and pure technical efficiency (PE) of universities in various provinces and cities in China show a decreasing trend, while the scale efficiency (SE) shows an irregular change; the research efficiency (including TE, PE and SE) varies with the window width in different years. In some years, the efficiency of scientific research is more sensitive to the change of window width. In some years, the efficiency of scientific research is not sensitive to the change of window width. In some years, the efficiency of scientific research is not sensitive to the change of window width. By comparing the results of the DEA window analysis model with the traditional DEA model, it is found that there is a great deviation between the two models. This further shows that it is necessary to establish a new DEA window analysis model for the dynamic analysis of scientific research efficiency in Chinese universities. The results of this DEA window analysis model are more in line with the actual situation of scientific research. Considering the lag period and not considering the lag period, there is a great deviation in the analysis results, which shows that the lag period has a great impact on the evaluation of scientific research efficiency in China's universities and must be paid attention to in the evaluation of scientific research in universities. The analysis results of the model provide some countermeasures and suggestions for the scientific research evaluation of universities in China.
【学位授予单位】：哈尔滨工业大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：G644
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