我国钢铁主营业上市公司生产效率分析
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摘要
钢铁产业是国民经济的支柱性产业,作为国民经济产业链的重要一环,它上联煤炭、铁矿石等重要采掘业,下联机械、建筑、房地产、汽车等交通运输设备、造船、家电等重要行业,其发展对国民经济的影响非常大,是国民经济社会发展水平和综合实力的重要标志。我国钢铁行业发展迅猛,钢铁产出由一亿吨上升为两亿吨仅用了六年(1997—2003年)的时间,产量的急剧增加可能得益于生产率的提高,更可能是这些年钢铁工业投资增长过猛的产物。那么中国钢铁工业产量的大幅增长主要是得益于要素投入增加,还是全要素生产率的提高?哪些因素阻碍了全要素生产率进步?弄清楚这些问题,无疑具有很重要的经济价值,这对于中国钢铁工业部门以及钢铁企业制定科学的战略决策具有重要意义。
本文采取数据包络分析法(DEA),结合2004—2007年我国钢铁主营业上市公司的数据,选取生铁产量、钢产量、钢材产量、营业收入、总资产、营业成本以及企业在岗人员总数这7个指标来进行实证分析。
DEA方法是一种使用线形规划来评价效率的方法,特别适合于多投入多产出的边界生产函数的研究。本文运用DEA方法来测度我国23家钢铁主营业上市公司的规模效率、技术效率、全要素生产效率。分析结果发现,在这23家钢铁企业中,(1)在规模效率方面,规模有效率的上市钢铁公司由2004年的12家到2007年下降为10家;(2)在纯技术效率方面,纯技术有效率的上市钢铁公司由2004年的16家到2007年上升为21家;(3)通过横向、纵向比较分析,在全要素生产率方面,全要素生产率增长的企业由2005年的7家下降为2007年的3家。
已有研究成果大多以规模效率和技术效率为研究重点,本文在已有研究成果的基础上,考虑了全要素生产率对钢铁企业生产效率的影响,并进行了横向与纵向的分析,为我国钢铁行业改进生产效率提出了有益的政策建议:(1)提高整个钢铁行业的产业集中度,在不断通过内部扩张或外部整合重组以提高规模效率,获取规模经济;(2)为提高技术效率,钢铁企业应注重核心业务的整合。
由于本人学术水平及时间所限,本文只选了其中的23家钢铁上市公司,采用2004—2007这四年的数据,并且只选取了7个指标进行分析,因此,本文的研究结果可能一定程度上影响分析结果的准确性和代表性。
Iron and steel industry is pillar of the national economy. As one of the most important national economy link, it connects not only the industries of coal and Iron ore, but also of machinery, construction and real estate, traffic, shipbuilding and home appliances. The development of iron and steel industry is of much influence. It is the important logo of the national economy's level and strength. China's steel output roses to 200 million tons from 100 million tons in just six years (1997 -2003) .The dramatic output may be due to the increase in productivity but more likely due to the dramatic invest in the iron and steel industry these years. Which is the reason that caused China iron and steel industry production growth, increased factor inputs or total factor productivity? What are the factors that hindered the progress of total factor productivity? Clarify these issues is no doubt very important in economic value, and is of great significance for China's iron and steel industrial sector and enterprises to develop scientific and strategic decision-making.
In this paper, we apply the data envelopment analysis (DEA)to do empirical research. DEA is a productivity method of linear programming, which is appropriate for border production functions of multi-input and multi-output. This paper does an empirical analysis for representative 23~2 enterprises selected from listed companies in China's steel industry. This paper does production efficiency, pure technical efficiency and scale efficiency empirical analysis including both horizontal and vertical analysis of contrast by seven indicators of pig iron production, steel production, steel production, operating income, total assets, operating costs and the staff of enterprises. Selected data are from 2004 to 2007.
From the analysis we can see that among the 23 chosed companies there are 12 to 10 companies from 2004 to 2007 which are of scale efficiency; there are 16 to 21 companies from 2004 to 2007 which are of pure technology efficiency; there are 7 to 3 companies from 2004 to 2007 which are of total factor productivity through both horizontal and vertical analysis of contrast.
Existing research results have considered scale efficiency and technology efficiency as the important points. In this paper we consider TFP to analysis iron and steel productivity based on existing research results. We make several useful policy suggestions to help improve our country's productivity. They are as follows: (l)To improve the whole iron and steel industry concentration to improve scale efficiency by means of internal expansion and external integration;(2) Core business integration of iron and steel industry should be considered to improve technology efficiency.
Because of my limited academic level and time restrictions, this paper only choose 23 listed companies and use the data from 2004 to 2007,and only choose 7 indicators to do research. So the conclusions in this paper maybe affect the accuracy and representative of analysis results to some extent.
本文采取数据包络分析法(DEA),结合2004—2007年我国钢铁主营业上市公司的数据,选取生铁产量、钢产量、钢材产量、营业收入、总资产、营业成本以及企业在岗人员总数这7个指标来进行实证分析。
DEA方法是一种使用线形规划来评价效率的方法,特别适合于多投入多产出的边界生产函数的研究。本文运用DEA方法来测度我国23家钢铁主营业上市公司的规模效率、技术效率、全要素生产效率。分析结果发现,在这23家钢铁企业中,(1)在规模效率方面,规模有效率的上市钢铁公司由2004年的12家到2007年下降为10家;(2)在纯技术效率方面,纯技术有效率的上市钢铁公司由2004年的16家到2007年上升为21家;(3)通过横向、纵向比较分析,在全要素生产率方面,全要素生产率增长的企业由2005年的7家下降为2007年的3家。
已有研究成果大多以规模效率和技术效率为研究重点,本文在已有研究成果的基础上,考虑了全要素生产率对钢铁企业生产效率的影响,并进行了横向与纵向的分析,为我国钢铁行业改进生产效率提出了有益的政策建议:(1)提高整个钢铁行业的产业集中度,在不断通过内部扩张或外部整合重组以提高规模效率,获取规模经济;(2)为提高技术效率,钢铁企业应注重核心业务的整合。
由于本人学术水平及时间所限,本文只选了其中的23家钢铁上市公司,采用2004—2007这四年的数据,并且只选取了7个指标进行分析,因此,本文的研究结果可能一定程度上影响分析结果的准确性和代表性。
Iron and steel industry is pillar of the national economy. As one of the most important national economy link, it connects not only the industries of coal and Iron ore, but also of machinery, construction and real estate, traffic, shipbuilding and home appliances. The development of iron and steel industry is of much influence. It is the important logo of the national economy's level and strength. China's steel output roses to 200 million tons from 100 million tons in just six years (1997 -2003) .The dramatic output may be due to the increase in productivity but more likely due to the dramatic invest in the iron and steel industry these years. Which is the reason that caused China iron and steel industry production growth, increased factor inputs or total factor productivity? What are the factors that hindered the progress of total factor productivity? Clarify these issues is no doubt very important in economic value, and is of great significance for China's iron and steel industrial sector and enterprises to develop scientific and strategic decision-making.
In this paper, we apply the data envelopment analysis (DEA)to do empirical research. DEA is a productivity method of linear programming, which is appropriate for border production functions of multi-input and multi-output. This paper does an empirical analysis for representative 23~2 enterprises selected from listed companies in China's steel industry. This paper does production efficiency, pure technical efficiency and scale efficiency empirical analysis including both horizontal and vertical analysis of contrast by seven indicators of pig iron production, steel production, steel production, operating income, total assets, operating costs and the staff of enterprises. Selected data are from 2004 to 2007.
From the analysis we can see that among the 23 chosed companies there are 12 to 10 companies from 2004 to 2007 which are of scale efficiency; there are 16 to 21 companies from 2004 to 2007 which are of pure technology efficiency; there are 7 to 3 companies from 2004 to 2007 which are of total factor productivity through both horizontal and vertical analysis of contrast.
Existing research results have considered scale efficiency and technology efficiency as the important points. In this paper we consider TFP to analysis iron and steel productivity based on existing research results. We make several useful policy suggestions to help improve our country's productivity. They are as follows: (l)To improve the whole iron and steel industry concentration to improve scale efficiency by means of internal expansion and external integration;(2) Core business integration of iron and steel industry should be considered to improve technology efficiency.
Because of my limited academic level and time restrictions, this paper only choose 23 listed companies and use the data from 2004 to 2007,and only choose 7 indicators to do research. So the conclusions in this paper maybe affect the accuracy and representative of analysis results to some extent.
引文
[1] Banker R.D. Estimating Most Productive Scale Size Using Data Envelopment Analysis [J].European Journal of Operations Research, 1984, (17).
[2] Banker R.D. and Thrall R.M. Estimation of Returns to Scale Using Data Envelopment Analysis [J].European Journal of Operations Research, 1992, (62).
[3] Chares A, Cooper.W.W. and Rhodes E. Measuring the efficiency of DMU[J].European Journal of Operation Research. 1978,(2).
[4] Golany B, Roll Y. An Application Procedure for Data Envelopment Analysisi[J].Omega,1989,(3).
[5] Halme M, et al. A value efficiency approach to incorporating preference information in data envelopment analysis[J].Management Science, 1999,(1).
[6] Wu Y R. The Chinese steel industry: recent development and prospects[J].Resource Policy,2000,(26).
[7] Yao Chen, Liang Liang, Feng Yang and Joe Zhu. Evaluation of information technology investment: a data envelopment analysis approach[J].Computer& Operations Research, 2006(11).
[8] The International Iron and Steel Institute [EB/OL].http: //www.world-steel.org.
[9] Stephen M. Miller, Mukti P. Upadhyay. Total factor productivity and the convergence hypothesis. Journal of Macroeconomics, 2002, (24).
[10] Mika Hanula. Total productivity measurement based on partial productivity ratios. Int. J.Production Economics 2002, (78).
[11] Hean Tat Keh, Singfat Chu. Retail Productivity and scale economies at the firm level: a DEA approach .Omega. 2003, (31).
[12] Chris Dubelaar, Mukesh Bhargava, David Ferrarin. Measuring retail productivity what really matter? Journal of Business Research, 2002, (55).
[13] Hirofumi Fukuyama. Returns to scale and scale elasticity in data envelopment analysis,European Journal of Operational research, 2000, (125).
[14] Nikolaos Maniadakis, Emmanuel Thanassoulis. A cost Malmquist productivity index, European Journal of Operational research,2004,(154).
[15]Peter Forsyth.Total factor productivity in Australian domestic aviation.Transport Policy,2001,8.
[16]Marcello Braglia,Simone Zanoni,Lucio Zavanella.Measuring and benehmarking productive systems[J].Production Planning &Control,2003,(14).
[17]Subhash C.Ray,Hiung Joon Kim.Cost efficiency in the US steel industry A nonparametric analysis using data envelopment analysis[J].European Journal of Operational Research,1995,(80).
[18][日]小宫隆太郎,奥野正宽等,日本产业政策[M],北京:国际文化出版公司,1988。
[19]瞿国忠,张妍,我国钢铁企业规模效益比较分析[J],《冶金管理》,2003,(8)。
[20]盛昭瀚,朱乔,吴广谋,DEA理论、方法与应用[M],北京:科学出版社,1996。
[21]孙保全,基于DEA模型的11家钢铁企业铁路运输效率的实证分析[J],《冶金丛刊》,2006,(2)。
[22]魏建新,提高产业集中度是钢铁业走向成熟的必由之路[J],《冶金经济与管理》,2006,(5)。
[23]魏权龄,数据包络分析[M],北京:科学出版社,2004。
[24]吴文江,数据包络分析法[M],中国统计出版社,2002。
[25]徐康宁,韩剑,中国钢铁产业的集中度、布局与结构优化研究[J],《中国工业经济》,2006,(2)。
[26]徐二明,高怀,中国钢铁企业竞争力评价及其动态演变规律分析[J],《中国工业经济》,2004,(11)。
[27]杨家兵,吴利华,基于DEA的钢铁行业上市公司效率评价[J],《工业技术经济》,2006,(2)。
[28]杨成,邢宗辉,郭新有,钢铁业上市公司的业绩评价[J],《统计与决策》,2005,(10)。
[29]叶琪,我国钢铁行业规模经济的实证分析[J],《湖南工业职业技术学院学报》,2005,(5)。
[30]赵国杰,郝清民,中国钢铁企业规模经济的数据包络分析[J],《中国钢铁》,2003,(2)。
[31]于淞楠,中国重点钢铁企业的规模经济分析[J],《哈尔滨商业大学学(自然科学版)》,2004,(5)。
[32]张爱华,中国钢铁工业集中度比较分析[J],《中国钢铁》,2005,(3)。
[33]张治河,谢忠泉,我国钢铁产业创新与发展的问题及管理措施[J],《中国软科学》,2006, (2)。
[34]中国钢铁工业协会,中国行业分析报告2006钢铁工业[M],北京:中国经济出版社,2005。
[35]北京奇正凌格企业管理顾问有限公司,《地方钢铁企业关键效益指标调查报告》[EB/OL],http://www,glinkage,net/
[36]陈凌,如何提高中国钢铁工业的产业集中度[J],《冶金管理》.2003,(4)。
[37]戴铁军,陆钟武,钢铁生产流程铁资源效率与工序铁资源效率关系的分析[J],《金属学报》,2006,(2)。
[38]黄喧仁,利用包络分析法决策标杆目标与标杆伙伴:以全球32大钢铁厂为例[D],台湾国立成功大学硕士论文,2003。
[39]江飞涛,陈伟刚,等,投资规制政策的缺陷与不良效应-基于中国钢铁工业的考察[J],《工业经济》,2007,(6)。
[40]焦国华,江飞涛,中国钢铁企业的相对效率与规模效率[J],《中国工业经济》,2007(10)。
[41]李亮,崔进川,DEA方法中输入输出项的选择和数据处理[J],《系统工程学报》,2003(6)。
[42]李拥军,杜立辉,高学东,中国钢铁产业为何出现逆集中化现象[J],《经济纵横》,2006,(9)。
[43]刘桂萍,中国钢铁工业差距的国际比较[J],《技术经济》,2000,(6)。
[2] Banker R.D. and Thrall R.M. Estimation of Returns to Scale Using Data Envelopment Analysis [J].European Journal of Operations Research, 1992, (62).
[3] Chares A, Cooper.W.W. and Rhodes E. Measuring the efficiency of DMU[J].European Journal of Operation Research. 1978,(2).
[4] Golany B, Roll Y. An Application Procedure for Data Envelopment Analysisi[J].Omega,1989,(3).
[5] Halme M, et al. A value efficiency approach to incorporating preference information in data envelopment analysis[J].Management Science, 1999,(1).
[6] Wu Y R. The Chinese steel industry: recent development and prospects[J].Resource Policy,2000,(26).
[7] Yao Chen, Liang Liang, Feng Yang and Joe Zhu. Evaluation of information technology investment: a data envelopment analysis approach[J].Computer& Operations Research, 2006(11).
[8] The International Iron and Steel Institute [EB/OL].http: //www.world-steel.org.
[9] Stephen M. Miller, Mukti P. Upadhyay. Total factor productivity and the convergence hypothesis. Journal of Macroeconomics, 2002, (24).
[10] Mika Hanula. Total productivity measurement based on partial productivity ratios. Int. J.Production Economics 2002, (78).
[11] Hean Tat Keh, Singfat Chu. Retail Productivity and scale economies at the firm level: a DEA approach .Omega. 2003, (31).
[12] Chris Dubelaar, Mukesh Bhargava, David Ferrarin. Measuring retail productivity what really matter? Journal of Business Research, 2002, (55).
[13] Hirofumi Fukuyama. Returns to scale and scale elasticity in data envelopment analysis,European Journal of Operational research, 2000, (125).
[14] Nikolaos Maniadakis, Emmanuel Thanassoulis. A cost Malmquist productivity index, European Journal of Operational research,2004,(154).
[15]Peter Forsyth.Total factor productivity in Australian domestic aviation.Transport Policy,2001,8.
[16]Marcello Braglia,Simone Zanoni,Lucio Zavanella.Measuring and benehmarking productive systems[J].Production Planning &Control,2003,(14).
[17]Subhash C.Ray,Hiung Joon Kim.Cost efficiency in the US steel industry A nonparametric analysis using data envelopment analysis[J].European Journal of Operational Research,1995,(80).
[18][日]小宫隆太郎,奥野正宽等,日本产业政策[M],北京:国际文化出版公司,1988。
[19]瞿国忠,张妍,我国钢铁企业规模效益比较分析[J],《冶金管理》,2003,(8)。
[20]盛昭瀚,朱乔,吴广谋,DEA理论、方法与应用[M],北京:科学出版社,1996。
[21]孙保全,基于DEA模型的11家钢铁企业铁路运输效率的实证分析[J],《冶金丛刊》,2006,(2)。
[22]魏建新,提高产业集中度是钢铁业走向成熟的必由之路[J],《冶金经济与管理》,2006,(5)。
[23]魏权龄,数据包络分析[M],北京:科学出版社,2004。
[24]吴文江,数据包络分析法[M],中国统计出版社,2002。
[25]徐康宁,韩剑,中国钢铁产业的集中度、布局与结构优化研究[J],《中国工业经济》,2006,(2)。
[26]徐二明,高怀,中国钢铁企业竞争力评价及其动态演变规律分析[J],《中国工业经济》,2004,(11)。
[27]杨家兵,吴利华,基于DEA的钢铁行业上市公司效率评价[J],《工业技术经济》,2006,(2)。
[28]杨成,邢宗辉,郭新有,钢铁业上市公司的业绩评价[J],《统计与决策》,2005,(10)。
[29]叶琪,我国钢铁行业规模经济的实证分析[J],《湖南工业职业技术学院学报》,2005,(5)。
[30]赵国杰,郝清民,中国钢铁企业规模经济的数据包络分析[J],《中国钢铁》,2003,(2)。
[31]于淞楠,中国重点钢铁企业的规模经济分析[J],《哈尔滨商业大学学(自然科学版)》,2004,(5)。
[32]张爱华,中国钢铁工业集中度比较分析[J],《中国钢铁》,2005,(3)。
[33]张治河,谢忠泉,我国钢铁产业创新与发展的问题及管理措施[J],《中国软科学》,2006, (2)。
[34]中国钢铁工业协会,中国行业分析报告2006钢铁工业[M],北京:中国经济出版社,2005。
[35]北京奇正凌格企业管理顾问有限公司,《地方钢铁企业关键效益指标调查报告》[EB/OL],http://www,glinkage,net/
[36]陈凌,如何提高中国钢铁工业的产业集中度[J],《冶金管理》.2003,(4)。
[37]戴铁军,陆钟武,钢铁生产流程铁资源效率与工序铁资源效率关系的分析[J],《金属学报》,2006,(2)。
[38]黄喧仁,利用包络分析法决策标杆目标与标杆伙伴:以全球32大钢铁厂为例[D],台湾国立成功大学硕士论文,2003。
[39]江飞涛,陈伟刚,等,投资规制政策的缺陷与不良效应-基于中国钢铁工业的考察[J],《工业经济》,2007,(6)。
[40]焦国华,江飞涛,中国钢铁企业的相对效率与规模效率[J],《中国工业经济》,2007(10)。
[41]李亮,崔进川,DEA方法中输入输出项的选择和数据处理[J],《系统工程学报》,2003(6)。
[42]李拥军,杜立辉,高学东,中国钢铁产业为何出现逆集中化现象[J],《经济纵横》,2006,(9)。
[43]刘桂萍,中国钢铁工业差距的国际比较[J],《技术经济》,2000,(6)。