The Biennial Malmquist Index in the of Negative Data

Volume 12, Issue 1, pp 1 - 11 http://dx.doi.org/10.22436/jmcs.012.01.01

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 2456 Downloads
• 3493 Views

Authors

Narjes Mohammadi - Science and Research branch, Islamic Azad university, Mazandaran, Iran. Alireza Yousefpour - Department of IT & Computer, Islamic Azad University of Qaemshahr branch, Iran.

Abstract

We purpose the range directional model (RDM), a particular case of the directional distance function, is used for computing efficiency in the presence of negative data. We use RDM efficiency measures to arrive at a Malmquist-type index which can reflect productivity change. We illustrate how the biennial Malmquist index can be used, not only for comparing the performance of a unit in two time periods, but also for comparing the performance of two different units at the same or different time periods. The proposed approach is then applied to a sample of bank branches where negative data were involved. In this paper, we introduce a biennial Malmquist index of productivity change that can be used with negative data.

Share and Cite

Keywords

• biennial Malmquist index
• negative data

MSC

•  90B50
•  91B82

References

• [1] DW. Caves, LR. Christensen, WE. Diewert, The economic theory of index numbers and the measurement of inputs, outputs and productivity, , (1982)
  • View Article
  • Google Scholar


• [2] R. G. Chambers, Y. Chung, R. Fare, Benefit and distance functions, , 407-419 (1996)
  • View Article
  • Google Scholar


• [3] R. G. Chambers, Y. Chung, R. Fare, Profit, directional distance functions, and Nerlovian efficiency , , (1998)
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [4] R. Färe, S. Grosskopf, M. Norris, Z. Zhang , Productivity growth, technical progress, and efficiency change in industrialized countries, , 66-83 (1994)
  • Google Scholar


• [5] R. Fare, S. Grosskopf, F. Hernadez-Sancho , Environmental performance: An index number approach, Resource and Energy Economics , 26 (2004), 343–352
  • View Article
  • Google Scholar


• [6] M. Nishimizu, J. M. Page, Total factor productivity growth, technological progress and efficiency change , , 920-936 (1982)
  • View Article
  • Google Scholar


• [7] K. H. Park, W. L. Weber , A note on efficiency and productivity growth in the Korean banking industry, 1992–2002, Journal of Banking and Finance , 30 (2006), 2371–2386
  • View Article
  • Google Scholar


• [8] J. T. Pastor, C. A. K. Lovell, A global Malmquist productivity index , Economics Letters 88, 266– 271 (2005)
  • View Article
  • MathSciNet
  • Google Scholar


• [9] J. T. Pastor, C. A. K. Lovell , the biennial Malmquist productivity change index, , 10-15 (2011)
  • View Article
  • Google Scholar


• [10] M. C. A. S. Portela, E. Thanassoulis, G. P. M. Simpson, Negative data in DEA: A directional distance approach applied to bank branches, Journal of the Operational Research Society , 55 (2004), 1111–1121
  • View Article
  • Google Scholar
  • MATH


• [11] M. C. A. S. Portela, E. Thanassoulis, Comparative efficiency analysis of Portuguese bank branches , European Journal of Operational Research , 177 (2007), 1275–1288
  • View Article
  • Google Scholar
  • MATH


• [12] M. C. A. S. Portela, E. Thanassoulis, A circular Malmquist-type index for measuring productivity, AstonWorking Paper RP08-02., Aston University Birmingham B4 7ET, U (2008)
  • Google Scholar