A universal solution for units-invariance in data envelopment analysis
AbstractThe directional distance function model is a generalization of the radial model in data envelopment analysis (DEA). The directional distance function model is appropriate for dealing with cases where undesirable outputs exist. However, it is not a units-invariant measure of efficiency, which limits its accuracy. In this paper, we develop a data normalization method for DEA, which is a universal solution for the problem of units-invariance in DEA. The efficiency scores remain unchanged when the original data are replaced with the normalized data in the existing units-invariant DEA models, including the radial and slack-based measure models, i.e., the data normalization method is compatible with the radial and slack-based measure models. Based on normalized data, a units-invariant efficiency measure for the directional distance function model is defined.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 41633.
Date of creation: 29 Sep 2012
Date of revision:
Data Envelopment Analysis; Data normalization; Units-invariance; Directional distance function;
Other versions of this item:
- Jin XU & Panagiotis D. ZERVOPOULOS & Zhenhua QIAN & Gang CHENG, 2012. "A Universal Solution For Units - Invariance In Data Envelopment Analysis," Theoretical and Practical Research in Economic Fields, ASERS Publishing, vol. 0(2), pages 124-131, January.
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
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