中国一流大学建设高校科研差异及态势
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摘要
为有针对性地推动中国高校走向世界一流大学的目标,以41所中国一流大学建设高校为研究对象,应用数据包络分析(DEA)和Malmquist指数,从静态和动态两角度测度其2013—2017年间的科研效率。研究发现:静态上,这些高校整体科研水平较高,区域间科研效率呈现东部>西部>中部>东北的差异,不同高校属性分类中综合类科研效率最高、理工类次之;动态上,研究期限内这些高校的科研全要素生产率受技术进步指标影响,总体呈现小幅上升趋势,东部与中部和西部的科研效率区域差距拉大、与东北部的缩小,农林类高校科研效率稳步提升。总之,中国一流大学建设高校的内部科研差距继续存在,但整体上差异将不断缩小。
In order to aim at promoting Chinese universities to be world-class universities, this paper takes 41 firstclass universities in China as research objects, and applies data envelopment analysis(DEA) and Malmquist index(DEA)to measure the efficiency of scientific research in 2013-2017 from both static and dynamic perspectives. The results show that, in static state, the overall scientific research level of these universities is high, and the efficiency of regional scientific research presents the difference of east > west > central > northeast, and the efficiency of comprehensive scientific research is the highest in the attribute classification of different universities, followed by the science and technology; dynamically, the total factor productivity of scientific research in these universities during the research period is affected by the index of technological progress, showing a slight upward trend as a whole, the regional gap in the efficiency of scientific research between the eastern and central and western regions is widening, while the regional disparity is narrowing between the east and the northeast, and the scientific research efficiency in agricultural and forestry universities is steady improvement. In short, the internal scientific research gap of China's first-class universities continues to exist, but the overall differences will continue to narrow.
In order to aim at promoting Chinese universities to be world-class universities, this paper takes 41 firstclass universities in China as research objects, and applies data envelopment analysis(DEA) and Malmquist index(DEA)to measure the efficiency of scientific research in 2013-2017 from both static and dynamic perspectives. The results show that, in static state, the overall scientific research level of these universities is high, and the efficiency of regional scientific research presents the difference of east > west > central > northeast, and the efficiency of comprehensive scientific research is the highest in the attribute classification of different universities, followed by the science and technology; dynamically, the total factor productivity of scientific research in these universities during the research period is affected by the index of technological progress, showing a slight upward trend as a whole, the regional gap in the efficiency of scientific research between the eastern and central and western regions is widening, while the regional disparity is narrowing between the east and the northeast, and the scientific research efficiency in agricultural and forestry universities is steady improvement. In short, the internal scientific research gap of China's first-class universities continues to exist, but the overall differences will continue to narrow.
引文
[1]邱均平,王菲菲.中国高校建设世界一流大学与学科进展[J].重庆大学学报(社会科学版),2014,20(1):97-103.
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[3]沙巨山.高等院校科研投入产出效率评价:基于DEA和Malmquist指数[J].财会通讯,2016(16):16-19.
[4]张惠琴,尚甜甜.高校科研创新效率对比分析:基于全国30个省份的面板数据[J].科研管理,2015,36(增刊1):181-186.
[5]古川.“一流学科”建设背景下农林类高校科研效率的动态变化及差异比较:基于EBM和Metafrontier-Malmquist模型的分析[J].系统工程,2017,35(12):105-112.
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[7]邱泠坪,郭明顺,张艳,等.基于DEA和Malmquist的高等农业院校科研效率评价[J].现代教育管理,2017(2):50-55.
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[11]王惠,王树乔,李小聪.基于DEA方法的江苏省高校科研效率评价研究[J].科技管理研究,2015,35(7):92-96.
[12]陈俊生,周平,张明妍.高校人文社会科学科研投入产出效率评价:基于DEA二次相对效率和超效率模型的实证分析[J]. 黑龙江高教研究,2015(12):44-46.
[13]李经路,闫维艳.DEA视窗分析的高校科研效率:2009—2015年间的A区数据[J].科技管理研究,2017,37(23):142-151.
[14]靳丹霞.江苏省战略性新兴产业创新效率评价研究[D].南京:南京审计大学,2018.
[15]梁文艳,袁玉芝,胡咏梅.研究型大学自然科学学科科研生产效率测算及影响因素分析:基于DEA-Tobit两阶段模型[J].国家教育行政学院学报,2014(10):70-76.
[16]胡咏梅,范文凤.“211工程”高校科研生产效率评估:基于DEA方法的经验研究[J].重庆高教研究,2014,2(3):1-14.
[17]苏为华,罗刚飞,曾守桢.高等学校科研效率评价研究:以浙江省为例[J].科研管理,2015,36(9):141-148.
[18]EAGAN K, McBAIN L, JONES K C. Student veteran data in higher education: new directions for institutional research[M]. New York: Jossey-Bass, 2017.
[19]FU C, ZHANG W. Research on the total factor productivity of the public library industry in China: analysis of dea-malmquist model based on provincial panel data[J].Journal of Central China Normal University, 2018,57(3):81-89.
[2]程肇基,张桂香.江西地方高校科研投入与产出效率评价[J].教师教育研究,2016,28(2):51-58.
[3]沙巨山.高等院校科研投入产出效率评价:基于DEA和Malmquist指数[J].财会通讯,2016(16):16-19.
[4]张惠琴,尚甜甜.高校科研创新效率对比分析:基于全国30个省份的面板数据[J].科研管理,2015,36(增刊1):181-186.
[5]古川.“一流学科”建设背景下农林类高校科研效率的动态变化及差异比较:基于EBM和Metafrontier-Malmquist模型的分析[J].系统工程,2017,35(12):105-112.
[6]胡德鑫,王轶玮.基于DEA的“985”高校科研竞争力评价[J].北京理工大学学报(社会科学版),2017,19(4):163-168.
[7]邱泠坪,郭明顺,张艳,等.基于DEA和Malmquist的高等农业院校科研效率评价[J].现代教育管理,2017(2):50-55.
[8]胡公启.江苏文科第一批次高校人文社会科学研究效率评价研究[J].科学管理研究,2018,36(3):52-55.
[9]李瑛,任珺楠.高校人文社会科学科研效率评价研究[J].科研管理,2016,37(增刊1):571-577.
[10]王惠.基于DEA方法的江苏省高校图书馆面向科研服务效率评价[J].情报探索,2018(1):37-41.
[11]王惠,王树乔,李小聪.基于DEA方法的江苏省高校科研效率评价研究[J].科技管理研究,2015,35(7):92-96.
[12]陈俊生,周平,张明妍.高校人文社会科学科研投入产出效率评价:基于DEA二次相对效率和超效率模型的实证分析[J]. 黑龙江高教研究,2015(12):44-46.
[13]李经路,闫维艳.DEA视窗分析的高校科研效率:2009—2015年间的A区数据[J].科技管理研究,2017,37(23):142-151.
[14]靳丹霞.江苏省战略性新兴产业创新效率评价研究[D].南京:南京审计大学,2018.
[15]梁文艳,袁玉芝,胡咏梅.研究型大学自然科学学科科研生产效率测算及影响因素分析:基于DEA-Tobit两阶段模型[J].国家教育行政学院学报,2014(10):70-76.
[16]胡咏梅,范文凤.“211工程”高校科研生产效率评估:基于DEA方法的经验研究[J].重庆高教研究,2014,2(3):1-14.
[17]苏为华,罗刚飞,曾守桢.高等学校科研效率评价研究:以浙江省为例[J].科研管理,2015,36(9):141-148.
[18]EAGAN K, McBAIN L, JONES K C. Student veteran data in higher education: new directions for institutional research[M]. New York: Jossey-Bass, 2017.
[19]FU C, ZHANG W. Research on the total factor productivity of the public library industry in China: analysis of dea-malmquist model based on provincial panel data[J].Journal of Central China Normal University, 2018,57(3):81-89.