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2001 | Buch

Introduction to the Theory and Application of Data Envelopment Analysis

A Foundation Text with Integrated Software

verfasst von: Emmanuel Thanassoulis

Verlag: Springer US

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1 DATA ENVELOPMENT ANALYSIS Data Envelopment Analysis (DEA) was initially developed as a method for assessing the comparative efficiencies of organisational units such as the branches of a bank, schools, hospital departments or restaurants. The key in each case is that they perform feature which makes the units comparable the same function in terms of the kinds of resource they use and the types of output they produce. For example all bank branches to be compared would typically use staff and capital assets to effect income generating activities such as advancing loans, selling financial products and carrying out banking transactions on behalf of their clients. The efficiencies assessed in this context by DEA are intended to reflect the scope for resource conservation at the unit being assessed without detriment to its outputs, or alternatively, the scope for output augmentation without additional resources. The efficiencies assessed are comparative or relative because they reflect scope for resource conservation or output augmentation at one unit relative to other comparable benchmark units rather than in some absolute sense. We resort to relative rather than absolute efficiencies because in most practical contexts we lack sufficient information to derive the superior measures of absolute efficiency. DEA was initiated by Charnes Cooper and Rhodes in 1978 in their seminal paper Chames et al. (1978). The paper operationalised and extended by means of linear programming production economics concepts of empirical efficiency put forth some twenty years earlier by Farrell (1957).

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction to Performance Measurement
Abstract
This book is on Data Envelopment Analysis (DEA). In its most traditional form DEA is one of the methods which we can use to assess the comparative efficiency of homogeneous operating units such as schools, hospitals, utility companies or sales outlets. In such contexts resources and/or environmental factors are converted into useful outcomes and DEA helps us to measure the comparative efficiency with which individual units carry out this transformation process. In less traditional contexts DEA can be used to choose from a set of competing multi-attribute alternatives such as selecting a most preferred site for locating some major facility or a sales outlet.
Emmanuel Thanassoulis
Chapter 2. Definitions of Efficiency and Related Measures
Abstract
DEA is a method for measuring comparative or relative efficiency. We speak of relative efficiency because its measurement by DEA is with reference to some set of units we are comparing with each other. We cannot in general derive by means of DEA some absolute measure of efficiency unless we make additional assumptions that the units being compared include a ‘sufficient’ number of units which are efficient in some absolute sense. Thus in a practical setting units which we may find efficient by DEA may in fact be capable of improving their performance even further.
Emmanuel Thanassoulis
Chapter 3. Data Envelopment Analysis under Constant Returns to Scale: Basic Principles
Abstract
This chapter introduces the basic principles underpinning DEA and derives special case DEA models. Chapter 4 will generalise the models introduced in this chapter.
Emmanuel Thanassoulis
Chapter 4. Data Envelopment Analysis under Constant Returns to Scale: General Models
Abstract
This chapter presents the basic DEA models for assessing technical input and output efficiency in the most general multi-input multi-output contexts. The models are referred to as basic to distinguish them from further DEA models developed in later chapters which can be seen as extensions to the models presented in this chapter.
Emmanuel Thanassoulis
Chapter 5. Using Data Envelopment Analysis in Practice
Abstract
In this chapter we focus on the practicalities of carrying out an assessment of comparative performance by means of DEA. In this context we cover the principles governing the choice of input-output variables, the type of information on performance most often retrieved in the course of a DEA assessment, the use of DEA software for carrying out assessments of performance and some additional advice on carrying out DEA assessments.
Emmanuel Thanassoulis
Chapter 6. Data Envelopment Analysis under Variable Returns to Scale
Abstract
In this chapter we relax the assumption of constant returns to scale (CRS) which we have maintained up to this point. It is recalled (see Chapter 3) that under CRS we assumed that if (x, y) is a feasible input-output correspondence then so is (αx, αy), where α is a non-zero positive constant. The implication of the CRS assumption can be seen readily if we consider a single-input single-output situation. If the input is x and the output is y, x being non-zero, the CRS assumption means that average productivity, denoted by the ratio y/x is not dependent on scale of production. For example we may be assessing a set of tax offices using their operating expenditure as the sole input and the number of accounts each tax office administers as the sole output. Then if we assume that CRS hold a Pareto-efficient tax office A which administers half the number of accounts another Pareto-efficient tax office B administers should incur half the operating expenditure of tax office B. (This assumes both tax offices face the same prices for their inputs).
Emmanuel Thanassoulis
Chapter 7. Assessing Policy Effectiveness and Productivity Change Using DEA
Abstract
The notion of assessing policy effectiveness by means of DEA was first introduced by Charnes et al. (1981). At issue is the disentangling of managerial and policy efficiency. A simple example will illustrate what we mean here. The author was involved in an assessment of the “market efficiency” of a set of public houses (pubs) in England, part of which is reported in Athanassopoulos and Thanassoulis (1995). The market efficiency of an outlet was intended to reflect its ability to attract custom, controlling for its environment and other factors beyond managerial control. The pubs to be assessed could be clearly separated into two categories: Pubs located so as to benefit from passing trade and pubs aimed mostly at local residents. In the context of this chapter we would say that each category of pubs was operating under a different ‘policy’ in terms of where the pubs were sited, the style of their management, internal decor and so on. Many real life contexts comprise operating units which perform similar tasks but could be said to operate under different policies. For example Golany and Storbeck (1999) in an assessment of bank branches identify those with “Personal Investment Centres” as one “programme” (policy in our context) and those without such centres as another programme or policy.
Emmanuel Thanassoulis
Chapter 8. Incorporating Value Judgements in DEA Assessments
Abstract
We saw in Chapter 4 that we can give the DEA efficiency measure equivalently a production or value interpretation. In value-based interpretations the DEA efficiency measure of a DMU is the ratio of the sum of its weighted outputs to the sum of its weighted inputs. The weights used are DMU-specific and they are chosen so as to maximise its efficiency rating, subject only to the restriction that they should be positive. The imputation of input-output values in this way has a number of practical advantages. One such advantage is that the user need not identify prior relative values for inputs and outputs, permitting instead such values to be determined by the model solved.
Emmanuel Thanassoulis
Chapter 9. Extensions to Basic DEA Models
Abstract
The basic DEA models (e.g. models [M4.1] and [M6.1] for input efficiency under CRS and VRS respectively) have been extended in a number of ways so that they may become appropriate for assessing efficiency in more specialised contexts. The literature on modifications of the basic DEA models is extensive and growing. We shall cover in this chapter three of the earliest extensions to basic DEA models which address the following situations:
  • Some of the input and/or output variables are exogenously fixed;
  • The DM does not have uniform preferences over improvements to input-output levels which would render a DMU Pareto-efficient;
  • Some of the input and/or output variables can only take categorical values.
Emmanuel Thanassoulis
Chapter 10. A Limited User Guide for Warwick DEA Software
Abstract
This book is accompanied by a limited version of Warwick DEA Software. The author is grateful to the University of Warwick, Coventry CV4 7AL in England for permission to incorporate the software with the book. The version is limited and capable of assessing only 10 DMUs. Please note:
  • The software accompanying the book is subject to the restrictions within the license document included with the software files.
  • Additional guidance on using the software can be obtained using the Help menu of the software.
Emmanuel Thanassoulis
Backmatter
Metadaten
Titel
Introduction to the Theory and Application of Data Envelopment Analysis
verfasst von
Emmanuel Thanassoulis
Copyright-Jahr
2001
Verlag
Springer US
Electronic ISBN
978-1-4615-1407-7
Print ISBN
978-1-4613-5538-0
DOI
https://doi.org/10.1007/978-1-4615-1407-7