Showing papers by "William W. Cooper published in 2011"

Handbook on data envelopment analysis

Abstract: -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.

Data Envelopment Analysis: History, Models, and Interpretations

Abstract: In about 30 years, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating the performance. DEA has been successfully applied to a host of many different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the basic DEA models and some of their extensions.

BAM: a bounded adjusted measure of efficiency for use with bounded additive models

Abstract: A decade ago the Range Adjusted Measure (RAM) was introduced for use with Additive Models. The empirical experience gained since then recommends developing a new measure with similar characteristics but with more discriminatory power. This task is accomplished in this paper by introducing the Bounded Adjusted Measure (BAM) in connection with a new family of Data Envelopment Analysis (DEA) additive models that incorporate lower bounds for inputs and upper bounds for outputs while accepting any returns to scale imposed on the production technology.

Returns to Scale in DEA

Abstract: This chapter discusses returns to scale (RTS) in data envelopment analysis (DEA). The BCC and CCR models described in Chap. 1 of this handbook are treated in input-oriented forms, while the multiplicative model is treated in output-oriented form. (This distinction is not pertinent for the additive model, which simultaneously maximizes outputs and minimizes inputs in the sense of a vector optimization.) Quantitative estimates in the form of scale elasticities are treated in the context of multiplicative models, but the bulk of the discussion is confined to qualitative characterizations such as whether RTS is identified as increasing, decreasing, or constant. This is discussed for each type of model, and relations between the results for the different models are established. The opening section describes and delimits approaches to be examined. The concluding section outlines further opportunities for research and an Appendix discusses other approaches in DEA treatment of RTS.

Sensitivity Analysis in DEA

Abstract: This chapter presents some of the recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (decision making units) into efficient and inefficient performers. Early work on this topic concentrated on developing algorithms for conducting such analyses after it was noted that standard approaches for conducting sensitivity analyses in linear programming could not be used in DEA. However, recent work has bypassed the need for such algorithms. It has also evolved from the early work that was confined to studying data variations in one input or output for one DMU. The newer methods described in this chapter make it possible to analyze the sensitivity of results when all data are varied simultaneously for all DMUs.

Choices and Uses of DEA Weights

Abstract: We review the literature of extensions and enhancements of the DEA basic methodology from the perspective of the problems that can be addressed by dealing with the dual multiplier formulation of the DEA models. We describe different approaches that allow incorporating into the analysis price information, reflecting meaningful trade-offs, incorporating value information and managerial goals, making a choice among alternate optima for the weights, avoiding non-zero weights, avoiding large differences in the values of multipliers, improving discrimination and ranking units. We confine attention to the methodological aspects of these approaches and show in many instances how others have used these approaches in applications in practise.

Avoiding Large Differences in Weights in Cross-Efficiency Evaluations: Application to the Ranking of Basketball Players

Abstract: Because of data envelopment analysis (DEA) flexibility in the choice of weights, assessment of decision-making units (DMUs) often involves weighting only a few inputs and outputs and ignoring the remaining variables by assigning them a zero weight. Widespread literature indicates the need to avoid zero weights, and some authors claim that the fact that a given DMU attaches very different weights to the variables involved in the assessments may be a concern (see, for example, Cooper, Seiford, & Tone, 2007). The aim of this paper was to prevent unrealistic weighting schemes in cross-efficiency evaluations through an extension of the multiplier bound approach (Ramon, Ruiz, & Sirvent, 2010a) based on “model” DMUs. The approach in that paper guarantees nonzero weights while at the same time it tries to avoid large differences in the values of multipliers. An application to the ranking of basketball players involved specifying a limit for allowable differences in the relative importance that players attach to different aspects of the game by reflecting those observed in the weight profiles of some model players, which are selected according to expert opinion. The approach provided results that are consistent with basketball expert opinion and illustrated why the classical approaches to cross-efficiency evaluation, which include the benevolent and aggressive formulations, may lead to unreasonable results.

Avoiding Large Differences in Weights in Cross-Efficiency Evaluations: Application to the Ranking of Basketball Players

Abstract: Because of data envelopment analysis (DEA) flexibility in the choice of weights, assessment of decisionmaking units (DMUs) often involves weighting only a few inputs and outputs and ignoring the remaining variables by assigning them a zero weight. Widespread literature indicates the need to avoid zero weights, and some authors claim that the fact that a given DMU attaches very different weights to the variables involved in the assessments may be a concern (see, for example, Cooper, Seiford, & Tone, 2007). The aim of this paper was to prevent unrealistic weighting schemes in cross-efficiency evaluations through an extension of the multiplier bound approach (Ramon, Ruiz, & Sirvent, 2010a) based on “model” DMUs. The approach in that paper guarantees nonzero weights while at the same time it tries to avoid large differences in the values of multipliers. An application to the ranking of basketball players involved specifying a limit for allowable differences in the relative importance that players attach to different aspects of the game by reflecting those observed in the weight profiles of some model players, which are selected according to expert opinion. The approach provided results that are consistent with basketball expert opinion and illustrated why the classical approaches to cross-efficiency evaluation, which include the benevolent and aggressive formulations, may lead to unreasonable results.

Efficiency in fundraising and distributions to cause-related social profit enterprises

Abstract: Managerial efficiency is as important in social profit enterprises (SPEs) as it is for more traditional financial-profit organizations. In this regard, both donors and SPE executives use efficiency information in making decisions. Here, we suggest a linked, two-stage Data Envelopment Analysis (DEA) methodology for assessing efficiency in both charitable fundraising and cause delivery, while empirically investigating results for international aid organizations. The model allows efficiency assessment for both the fundraising and utilization of generated funds when directed for cause-related purposes. This, in particular, allows for measurement of the organization’s managerial efficiency relative to both multiple phased goals and peer organizations. Additionally, the approach provides benchmarks for identifying sources of improved performance in fundraising and program/cause service delivery. It can also project the results of changes in inputs on the amount of resources available for the charitable organization’s cause. The proposed model(s) allow the examiner to assess performance while, at the same time, identifying those instances wherein the simple ratio measures commonly used in non-profit assessment are (1) deficient, and/or (2) misleading because of the use of ‘incorrect’ variables, or the ‘hiding’ of inefficiency if/when tax form categories are filed by an SPE. Importantly, the suggested two-stage DEA methodology can be useful for any organization with multiple-linked goals.

Congestion: Its Identification and Management with DEA

Abstract: Congestion is a term that is applicable in a variety of disciplines which range from medical science to traffic engineering. It has also many uses in practical everyday life. This brings with it a certain looseness in usage. We therefore expand (and refine) our discussion of congestion with reference to its use in economics where we have access to a precise meaning which we can develop in this chapter. This chapter covers the standard approaches used for treating congestion in data envelopment analysis.

Strategy and Implementation: Quantitative Evaluation of Strategic Performance with Application to Mutual Funds

Abstract: This paper applies specific quantitative methods to demonstrate a general theoretical model for measuring strategic performance. The theoretical concepts are universal and measurable for all types of strategic activity by applying the methodology through alternative quantitative analytical methods. The model distinguishes and quantifies three aspects of strategic performance: (i) strategy itself relative to its goals; (ii) individual executions relative to the strategy itself; and (iii) competing or alternative strategies. The methodology for quantifying strategic performance (i) estimates a strategy’s best potential from its actual best performances; (ii) evaluates individual executions by comparing their results to their strategies’ potentials; and (iii) compares different strategies per se based on their best potentials. Implementation of the method is illustrated by a two-stage data envelopment analysis and nonparametric two-way orthogonal contrast (Mann-Whitney) test applied to mutual fund investment strategies. The conceptual framework and derived methods address two problems. First, they provide practicable means for evaluating the three aspects of strategic performance in practice, diagnosing the causes of strategic success and failure, and comparing competing strategies. Second, they overcome a fundamental theoretical weakness of both practitioner and academic studies which evaluate either execution performance or strategy performance without controlling for the simultaneous effects of the other. Application of this method to mutual funds found the same “paradoxical” return/risk relationship Bowman found for industrial firms.