Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization

The performance of a decision making unit (DMU) can be evaluated in either a cross-sectional or a time-series manner, and data envelopment analysis (DEA) is a useful method for both types of evaluation. In order to eliminate the inconsistency caused by using different frontier facets to calculate efficiency, common-weights DEA models have been developed, under which a group of DMUs can be ranked for a specific period. This study proposes a common-weights DEA model for time-series evaluations to calculate the global Malmquist productivity index (MPI) so that the productivity changes of all DMUs have a common basis for comparison. The common-weights global MPI not only has sound properties, but also produces reliable results. The case of Taiwan forests after reorganization shows that the MPIs calculated from the conventional DEA model produce misleading results. The common-weights global MPI approach, on the other hand, correctly identifies districts with unsatisfactory performance before the reorganization and those with unsatisfactory productivity improvement after the reorganization.

[1]  G. D. Ferrier,et al.  Do mergers improve hospital productivity? , 2004, J. Oper. Res. Soc..

[2]  Toshiyuki Sueyoshi,et al.  Extended DEA-Discriminant Analysis , 2001, Eur. J. Oper. Res..

[3]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[4]  L. R. Christensen,et al.  THE ECONOMIC THEORY OF INDEX NUMBERS AND THE MEASUREMENT OF INPUT, OUTPUT, AND PRODUCTIVITY , 1982 .

[5]  William W. Cooper,et al.  Measures of inefficiency in data envelopment analysis and stochastic frontier estimation , 1997 .

[6]  Zilla Sinuany-Stern,et al.  Academic departments efficiency via DEA , 1994, Comput. Oper. Res..

[7]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[8]  Joe Zhu.  Robustness of the efficient DMUs in data envelopment analysis , 1996 .

[9]  John E. Beasley,et al.  Restricting Weight Flexibility in Data Envelopment Analysis , 1990 .

[10]  Joe Zhu,et al.  Measuring performance of two-stage network structures by DEA: A review and future perspective , 2010 .

[11]  T. Coelli,et al.  Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries, 1980-2000 * , 2003 .

[12]  Finn R. Førsund,et al.  Malmquist Indices of Productivity Growth during the Deregulation of Norwegian Banking, 1980-89 , 1992 .

[13]  William W. Cooper,et al.  Using Malmquist Indexes to measure changes in the productivity and efficiency of US accounting firms before and after the Sarbanes–Oxley Act , 2009 .

[14]  C. Kao,et al.  Data envelopment analysis with common weights: the compromise solution approach , 2005, J. Oper. Res. Soc..

[15]  Toshiyuki Sueyoshi,et al.  Privatization of nippon telegraph and telephone: Was it a good policy decision? , 1998, Eur. J. Oper. Res..

[16]  J. Pastor,et al.  A global Malmquist productivity index , 2005 .

[17]  J. Doyle,et al.  Multiattribute Choice for the Lazy Decision Maker: Let the Alternatives Decide! , 1995 .

[18]  Lawrence M. Seiford,et al.  INFEASIBILITY OF SUPER EFFICIENCY DATA ENVELOPMENT ANALYSIS MODELS , 1999 .

[19]  B. Golany,et al.  Alternate methods of treating factor weights in DEA , 1993 .

[20]  Toshiyuki Sueyoshi,et al.  DEA-discriminant analysis in the view of goal programming , 1999, Eur. J. Oper. Res..

[21]  Joe Zhu,et al.  A discussion of testing DMUs' returns to scale , 1995 .

[22]  Chiang Kao,et al.  Reorganization of forest districts via efficiency measurement , 1992 .

[23]  Isidoro Guzmán,et al.  Productivity and efficiency change and shareholder value: evidence from the Spanish banking sector , 2008 .

[24]  L. R. Christensen,et al.  MULTILATERAL COMPARISONS OF OUTPUT, INPUT, AND PRODUCTIVITY USING SUPERLATIVE INDEX NUMBERS* , 1982 .

[25]  C. Kao.  Measuring the performance improvement of Taiwan forests after reorganization. , 2000 .

[26]  A. Charnes,et al.  The non-archimedean CCR ratio for efficiency analysis: A rejoinder to Boyd and Färe☆ , 1984 .

[27]  M. Odening,et al.  Efficiency and total factor productivity in Ukrainian agriculture in transition , 2005 .

[28]  Zilla Sinuany-Stern,et al.  Scaling units via the canonical correlation analysis in the DEA context , 1997, Eur. J. Oper. Res..

[29]  Zilla Sinuany-Stern,et al.  DEA and the discriminant analysis of ratios for ranking units , 1998, Eur. J. Oper. Res..

[30]  Zilla Sinuany-Stern,et al.  Review of ranking methods in the data envelopment analysis context , 2002, Eur. J. Oper. Res..

[31]  B. Golany,et al.  Controlling Factor Weights in Data Envelopment Analysis , 1991 .

[32]  Lawrence M. Seiford,et al.  Models for performance benchmarking: Measuring the effect of e-business activities on banking performance , 2004 .

[33]  Zhicheng Liang,et al.  Financial Development, Economic Efficiency and Productivity Growth: Evidence from China , 2006 .

[34]  A. Q. Nyrud,et al.  Production efficiency and productivity growth in Norwegian sawmilling , 2003 .

[35]  Donna L. Retzlaff-Roberts.  Relating discriminant analysis and data envelopment analysis to one another , 1996, Comput. Oper. Res..

[36]  K. Tone,et al.  Dynamic DEA: A slacks-based measure approach , 2010 .

[37]  Gabriel R. Bitran,et al.  Productivity measurement at the micro level , 1984 .

[38]  R. Färe,et al.  Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries , 1994 .

[39]  Cláudia S. Sarrico,et al.  Restricting virtual weights in data envelopment analysis , 2004, Eur. J. Oper. Res..

[40]  A. Charnes,et al.  Classifying and characterizing efficiencies and inefficiencies in data development analysis , 1986 .