本文运用三阶段DEA(数据包络分析)模型对我国2009年工业生产效率进行实证研究。结果表明:三阶段DEA模型对工业生产效率进行测度比传统DEA方法更为合理;资源利用水平、人均居民消费、人均国内生产总值、财政支出占生产总值的比例、民营资本所占份额和对外开放水平等环境变量对工业生产效率有着显著影响,各省市应该努力改善环境要素,创造工业生产效率提高的良好环境;各省市应该根据自身所处工业生产效率类型采取提高技术效率和扩大工业生产规模等措施,增强工业生产效率发展内在动力。
Abstract
By using the three stage Data Envelopment Analysis (DEA) model, the industrial production efficiency of China in 2009 is studied. The results indicate that a three-stage DEA model is more reasonable than the traditional DEA method for measuring the efficiency of industrial production; the variables of the level of resource utilization, the per capita consumption, the per capita GDP, the ratio of financial expenditure to GDP, the ratio of private capital to total capital, and the level of openness to the outside world have significant effects on industrial production efficiency, and each province in China should strive to improve the environmental factors and create a favorable environment for improving the industrial production efficiency; each province should take the measurements to improve the technical efficiency and expand the industrial production scale, strengthening the internal driving force of industrial production efficiency according to their own types of industrial production efficiency.
关键词
工业生产效率 /
三阶段DEA模型 /
纯技术效率 /
规模效率
Key words
industrial production efficiency /
three-stage DEA model /
pure technical efficiency /
scale efficiency
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Farrell M.J., The Measurement of Productive Efficiency [J]. Journal of the Royal Statistical Society, 1957, (30):253-281.
[2] Surender, Kumar, A Decomposition of Total Productivity Growth: A Regional Analysis of Indian Industrial Manufacturing Growth [J]. International Journal of Productivity and Performance Management,2006,55 (3): 311-324.
[3] Frank S.T. Hsiao, Korean And Taiwanese Productivity Performance: Comparisons at Matched Manufacturing Levels [J]. Journal of Productivity Analysis, 2005,(23) :85-107.
[4] Gaofeng Han, Productivity and Economic Growth in East Asia Innovation , Efficiency and Accumulation [J]. Japan and the World Economy , 2002,(14):401-424.
[5] 郑京海,胡鞍钢.中国改革时期省际生产率增长变化的实证分析(1979-2001) [J].经济学(季刊),2005(1):263-296.Zheng Jinghai, Hu Angang. An Empirical Analysis of Provincial Productivity in China (1979—2001) [J].China Economic Quarterly, 2005,(1):263-296.
[6] 岳书敬,刘朝明.人力资本与区域全要素生产率分析[J].经济研究,2006(4):90-96.Yue Shujing, Liu Chaoming. Human Capital Accumulation and Regional Total Factor Productivity[J].Economic Research Journal, 2006(4):90-96.
[7] 沈能,刘凤朝,赵建强.中国地区工业技术效率差异及其变动趋势分析:基于Malmlquist生产率指数 [J].科研管理,2007(7):16-22.Shen Neng, Liu Fengchao, Zhao Jianqiang. An analysis on the Technical Efficiency Change and Trend in China’s Industry——A Malmquist Productivity Index Approach[J].Science Research Management, 2007(7):16-22.
[8] 陶洪,戴昌钧.中国工业劳动生产率增长率的省域比较——基于DEA的经验分析 [J].数量经济技术经济研究,2007(10):100-107.Tao Hong, Dai Changjun.Comparison of Provincial Industrial Labor Productivity in China Based on DEA[J].The Journal of Quantitative & Technical Economics, 2007(10):100-107.
[9] 宫俊涛,孙林岩,李刚.中国制造业省际全要素生产率变动分析——基于非参数Malmquist指数方法 [J].数量经济技术经济研究,2008(4):97-109.Gong Juntao, Sun Linyan, Li Gang.Provincial Productivity Analysis in China’s Manufacturing Industry[J].The Journal of Quantitative & Technical Economics, 2008(4):97-109.
[10] 杨文举,张亚云.中国地区工业的劳动生产率差距演变——基于DEA的经验分析 [J].经济与管理研究,2010(10):115-121.Yang Weiju, Zhang Yayun.The Evolution of Regional Disparity of Industrial Labor Productivity in China——An Empirical Analysis Based on DEA [J].Research on Economics and Management, 2010(10):115-121.
[11] 涂正革,肖耿.中国的工业生产力革命——用随机前沿生产模型对中国大中型工业企业全要素生产率增长的分解及分析 [J].经济研究,2005(3):4-15.Tu Zhengge, Xiao Geng, China’s Industrial Productivity Revolution A Stochastic Frontier Production Function Analysis of TFP Growth in China’s Large and Medium Industrial Enterprises [J].Economic Research Journal, 2005(3):4-15.
[12] Fried, Lovell, Schmidt and Yaisawarng, Accounting f or Environmental Effects and Statistical Noise in Data Envelopment Analysis [J]. Journal of Productivity Analysis, 2002(17), 121-136.
基金
本文获得中央高校基本科研业务费专项资金项目资助,项目编号:z109021002。
PDF(924 KB)
