Estimation and inference in two-stage, semi-parametric models of production processes

A slacks-based measure of super-efficiency in data envelopment analysis

In most models of Data Envelopment Analysis (DEA), the best performers have the full efficient status denoted by unity (or 100%), and, from experience, we know that usually plural Decision Making Units (DMUs) have this “efficient status”. To discriminate between these efficient DMUs is an interesting subject. This paper addresses this “super-efficiency” issue by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498]. The method differs from the traditional one based on the radial measure, e.g. Andersen and Petersen model, in that the former deals directly with slacks in inputs/outputs, while the latter does not take account of the existence of slacks. We will demonstrate the rationality of our approach by comparing it with the radial measure of super-efficiency. The proposed method will be particularly useful when the number of DMUs are small compared with the number of criteria employed for evaluation.

A Slacks-based Measure of Super-Efficiency in Data Envelopment Analysis

Matrix Differential Calculus with Applications in Statistics and Econometrics

Data envelopment analysis (DEA) - Thirty years on

-Preface W.W. Cooper, L.M. Seiford, J. Zhu. -1. Data Envelopment Analysis: History, Models and Interpretations W.W. Cooper, L.M. Seiford, J. Zhu. -2. Returns to Scale in DEA: R.D. Banker, W.W. Cooper, L.M. Seiford, J. Zhu. -3. Sensitivity Analysis in DEA: W.W. Cooper, Shanling Li, L.M. Seiford, J. Zhu. -4. Incorporating Value Judgments in DEA: E. Thanassoulis, M.C. Portela, R. Allen. -5. Distance Functions with Applications to DEA R. Fare, S. Grosskopf, G. Whittaker. -6. Qualitative Data in DEA W.D. Cook. -7. Congestion: Its Identification and Management with DEA W.W. Cooper, Honghui Deng, L.M. Seiford, J. Zhu. -8. Malmquist Productivity Index: Efficiency Change Over Time K. Tone. -9. Chance Constrained DEA: W.W. Cooper, Zhimin Huang, S.X. Li. -10. Performance of the Bootstrap for DEA Estimators and Iterating the Principle: L. Simar, P.W.Wilson. -11. Statistical Tests Based on DEA Efficiency Scores R.D. Banker, R. Natarajan. -12. Performance Evaluation in Education: Modeling Educational Production J. Ruggiero. -13. Assessing Bank and Bank Branch Performance: Modeling Considerations and Approaches J.C. Paradi, S. Vela, Zijiang Yang. -14. Engineering Applications of Data Envelopment Analysis: Issues and Opportunities: K.P. Triantis. -15. Benchmarking in Sports: Bonds or Ruth: Determining the Most Dominant Baseball Batter Using DEA T.R. Anderson. -16. Assessing the Selling Function in Retailing: Insights from Banking, Sales forces, Restaurants & Betting shops A.D. Athanassopoulos. -17. Health Care Applications: From Hospitals to Physicians, From Productive Efficiency to Quality Frontiers: J.A. Chilingerian, H.D. Sherman. -18. DEA Software Tools and Technology: A State-of-the-Art Survey R. Barr. -Notes about Authors. Author Index. Subject Index.

http://www.deafrontier.net/TOC_DEAhandbook1.pdf

Handbook on data envelopment analysis

Modeling undesirable factors in efficiency evaluation

Utilizing recent developments in data envelopment analysis (DEA), this paper examines the performance of the top 55 U.S. commercial banks via a two-stage production process that separates profitability and marketability. Substantial performance inefficiency is uncovered in both dimensions. Relatively large banks exhibit better performance on profitability, whereas smaller banks tend to perform better with respect to marketability. New contextdependent performance measures are defined for profitability and marketability which employ a DEA stratification model and a DEA attractiveness measure. When combined with the original DEA measure, the context-dependent performance measure better characterizes the profitability and marketability of 55 U.S. commercial banks. The new approach identifies areas for improved bank performance over the two-stage production process. The effect of acquisition on efficiency and attractiveness is also examined.

Profitability and Marketability of the Top 55 U.S. Commercial Banks

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.

http://www.deafrontier.net/papers/OMEGAPriori.pdf

Data envelopment analysis: Prior to choosing a model

Basic DEA Models Measure-specific DEA Models Returns-to-Scale DEA with Preference Modelling Undesirable Measures Context-dependent Data Envelopment Analysis Benchmarking Models Models for Evaluating Value Chains Congestion Super Efficiency Sensitivity Analysis and Its Uses DEA Excel Solver.

Joe Zhu

Papers

Handbook on data envelopment analysis

Modeling undesirable factors in efficiency evaluation

Profitability and Marketability of the Top 55 U.S. Commercial Banks

Data envelopment analysis: Prior to choosing a model

Quantitative models for performance evaluation and benchmarking : data envelopment analysis with spreadsheets and DEA Excel Solver