基于偏好DEA模型的中国纺织业效率评价

Abstract

Abstract: In this paper,a data envelopment analysis model with preference information about input and output targets is set up to evaluate the economic operational efficiency of the textile industry of Chinese 31 regions.A general framework embracing CCR and BCC models is put forth.The phenomenon that because of a lot of zero weights and overestimating DEA efficiency of decision making units in ordinary DEA model can be avoided by using the model.We also analyze what causes the differences among the efficiency of the textile industry of Chinese 31 regions.Then the stochastic production frontier function of Chinese textile industry is obtained with the data set of all decision making units’ projection points on the production frontier face by regression analysis model.Finally we find that three economic factors:sale incomes,asset per person,Foreign Direct Investment,have important effects on the efficiency of China textile industry by an econometric regressive model.

Key words: preference information, data envelopment analysis, stochastic production frontier, performance measurement, Chinese textile industry

CLC Number:

F723
C931

WANG Xin-yu, WU Rui-ming. Performance Measurement of Chinese Textile Industry Based on Data Envelopment Analysis Model with Preference Information[J]. Chinese Journal of Management Science, 2005, (2): 142-148.

References

[1] Charnes,A.,Cooper,W.W.and Rhodes,E.Measuring the efficiency of decision making units[J].European Journal of operational research,1978,2(6):429-444.
[2] Lawrence M.Seiford and Joe Zhu.Profitability and marketability of the top 55U.S.commercial banks[J].Management science,1999,45:1270-1288.
[3] Andreas Soteriou,Stavros A.Zenios.Operations,quality,and profitability in the provision of banking services[J].Management science,1999,45:1221-1238.
[4] Biresh Kumar Sahoo,Pratap K.J.Mohapatra and M.L.Trivrdi.A comparative application of data envelopment analysis and frontier translog production function for estimating returns to scale and efficiencies[J].International Journal of System Science,1999,30:379-394.
[5] Abdul Wadud and Ben White.Farmhousehold efficiency in bangladesh:a comparison of stochastic frontier and DEA methods[J].Applied economics,2000,32:1665-1673.
[6] Toshiyuki Sueyoshi.Stochastic DEA for restructure strategy:an application to a Japanese petroleum company[J].Omega,2000,28:385-398.
[7] Merja Halme,Tarja Joro,Pekka,Korhonen,Seppo Salo,Jyrki Wallenius.A value efficiency approach to incorporating preference information in data envelopment analysis[J].Management Science,1999,45:103-114.
[8] G Hao,QL Wei and H Yan.A game theoretical model of DEA efficiency[J].Journal of the operational research society,2000,51(11):1319-1329.
[9] Dyson,R.G.,E.Thanassoulis.Reducing weight flexibility in data envelopment analysis[J].Journal of the operational research society,1988,39(6):563-576.
[10] CS Sarrico,SM Hogan,RG Dyson and AD Athanassopoulos.Data envelopment analysis and university selection[J].Journal of the operational research society,1997,48(12):1163-1177.
[11] Charnes,A.,Cooper,W.W.,Q.L.Wei,Z.M.Huang.Cone ratio data envelopment analysis and multi-objective programming[J].International journal of system science,1989,20:1099-1118.
[12] Charnes,A.,Cooper,W.W.,Huang,Z.M.,Sun,D.B.Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks[J].Journal of Econometrics,1990,46:73-91.
[13] Thompson,R.G.,L.N.Langemeier,et al.The role of multiplier bounds in efficiency analysis with applications to Kansas farming[J].Journal of Econometrics,1990,46(1):93-108.
[14] Thompson,R.G.,Dharmapala,P.S.,Thrall,R.M.Linked-cone DEA profit ratios and technical efficiency with application to Illinois coal mines[J].International Journal of Production Economics,1995,39:99-115.
[15] Y.-H.B.Wong,J.E.Beasley.Restricting weight flexibility in data envelopment analysis[J].Journal of the operational research society,1990,41(9):829-835.
[16] 李春好.对W-B乘子置信域约束构造方法的改进及模型[J].中国管理科学,2003,11(3):51-55.
[17] Russell G.Thompson,P.S.Dharmapala,Louis J.Rothenberg and Robert M.Thrall.DEA/AR efficiency and profitability of 14 major oil companies in U.S. exploration and production[J].Computers and operations research,1996,23(4):357-373.
[18] Aigner,D.and S.F.Chu.On estimating the industry production function[J].American economic review,1968,58:826-839.
[19] Aigner D.,Lovell K.and Schmidt,P.,Formulation and estimation of stochastic frontier production models[J].Journal of Econometrics,1977,(6):21-37.
[20] Renuka Mahadevan.How technically efficient are Singapore's manufacturing industries?[J].Applied economics,2000,32:2007-2014.

Performance Measurement of Chinese Textile Industry Based on Data Envelopment Analysis Model with Preference Information

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