Showing papers on "Data envelopment analysis published in 1999"

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software

Abstract: List of Tables. List of Figures. Preface. 1. General Discussion. 2. The Basic CCR Model. 3. The CCR Model and Production Correspondence. 4. Alternative DEA Models. 5. Returns to Scale. 6. Models with Restricted Multipliers. 7. Discretionary, Non-Discretionary and Categorical Variables. 8. Allocation Models. 9. Data Variations. Appendices. Index.

Statistical inference in nonparametric frontier models: the state of the art

Abstract: Efficiency scores of firms are measured by their distance to an estimated production frontier. The economic literature proposes several nonparametric frontier estimators based on the idea of enveloping the data (FDH and DEA-type estimators). Many have claimed that FDH and DEA techniques are non-statistical, as opposed to econometric approaches where particular parametric expressions are posited to model the frontier. We can now define a statistical model allowing determination of the statistical properties of the nonparametric estimators in the multi-output and multi-input case. New results provide the asymptotic sampling distribution of the FDH estimator in a multivariate setting and of the DEA estimator in the bivariate case. Sampling distributions may also be approximated by bootstrap distributions in very general situations. Consequently, statistical inference based on DEA/FDH-type estimators is now possible. These techniques allow correction for the bias of the efficiency estimators and estimation of confidence intervals for the efficiency measures. This paper summarizes the results which are now available, and provides a brief guide to the existing literature. Emphasizing the role of hypotheses and inference, we show how the results can be used or adapted for practical purposes.

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

Abstract: 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.

RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA

Abstract: Generalized Efficiency Measures (GEMS) for use in DEA are developed and analyzed in a context of differing models where they might be employed. The additive model of DEA is accorded a central role and developed in association with a new measure of efficiency referred to as RAM (Range Adjusted Measure). The need for separately treating input oriented and output oriented approaches to efficient measurement is eliminated because additive models effect their evaluations by maximizing distance from the efficient frontier (in l1, or weighted l1, measure) and thereby simultaneously maximize outputs and minimize inputs. Contacts with other models and approaches are maintained with theorems and accompanying proofs to ensure the validity of the thus identified relations. New criteria are supplied, both managerial and mathematical, for evaluating proposed measures. The concept of “approximating models” is used to further extend these possibilities. The focus of the paper is on the “physical” aspects of performance involved in “technical” and “mix” inefficiencies. However, an Appendix shows how “overall,” “allocative” and “technical” inefficiencies may be incorporated in additive models.

Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea

Abstract: Data Envelopment Analysis (DEA) is a nonparametric approach to evaluating the relative efficiency of decision making units (DMUs) that use multiple inputs to produce multiple outputs An assumption underlying DEA is that all the data assume the form of specific numerical values In some applications, however, the data may be imprecise For instance, some of the data may be known only within specified bounds, while other data may be known only in terms of ordinal relations DEA with imprecise data or, more compactly, the Imprecise Data Envelopment Analysis (IDEA) method developed in this paper permits mixtures of imprecisely- and exactly-known data, which the IDEA models transform into ordinary linear programming forms This is carried even further in the present paper to comprehend the now extensively employed Assurance Region (AR) concepts in which bounds are placed on the variables rather than the data We refer to this approach as AR-IDEA, because it replaces conditions on the variables with transformations of the data and thus also aligns the developments we describe in this paper with what are known as cone-ratio envelopments in DEA As a result, one unified approach, referred to as the AR-IDEA model, is achieved which includes not only imprecise data capabilities but also assurance region and cone-ratio envelopment concepts

Efficiency measurement of health care: a review of non-parametric methods and applications.

Abstract: There has been increasing interest in measuring the productive performance of health care services, since the mid-1980s. This paper reviews this literature and, in particular, the concept and measurement of efficiency and productivity. Concerning measurement, we focus on the use of Data Envelopment Analysis (DEA), a technique particularly appropriate when multiple outputs are produced from multiple inputs. Applications to hospitals and to the wider context of general health care are reviewed and the empirical evidence from both the USA and Europe (EU) is that public rather than private provision is more efficient.

Infeasibility of super efficiency data envelopment analysis models

Abstract: The paper investigates the infeasibility of super-efficiency data envelopment analysis (DEA) models in which the unit under evaluation is excluded from the reference set. Necessary and sufficient conditions are provided for infeasibility of the super-efficiency DEA measures. By the returns to scale (RTS) classifications obtained from the standard DEA model, we can further locate the position of the unit under evaluation when infeasibility occurs. It is shown that the ranking of the total set of efficient DMUs is impossible because of the infeasibility of super-efficiency DEA models. Also we are able to identify the endpoint positions of the extreme efficient units. The results are useful for sensitivity analysis of efficiency classifications.

Consolidation and efficiency in the US life insurance industry

Abstract: This paper examines the relationship between mergers and acquisitions, efficiency, and scale economies in the US life insurance industry. We estimate cost and revenue efficiency over the period 1988–1995 using data envelopment analysis (DEA). The Malmquist methodology is used to measure changes in efficiency over time. We find that acquired firms achieve greater efficiency gains than firms that have not been involved in mergers or acquisitions. Firms operating with non-decreasing returns to scale (NDRS) and financially vulnerable firms are more likely to be acquisition targets. Overall, mergers and acquisitions in the life insurance industry have had a beneficial effect on efficiency.

A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis

Abstract: We develop a procedure and the requisite theory for incorporating preference information in a novel way in the efficiency analysis of Decision Making Units. The efficiency of Deci sion Making Units is defined in the spirit of Data Envelopment Analysis (DEA), complemented with Decision Maker's preference information concerning the desirable structure of inputs and outputs. Our procedure begins by aiding the Decision Maker in searching for the most preferred combination of inputs and outputs of Decision Making Units (for short, Most Preferred Solution) which are efficient in DEA. Then, assuming that the Decision Maker's Most Preferred Solution maximizes his/her underlying (unknown) value function, we approximate the indifference contour of the value function at this point with its possible tangent hyperplanes. Value Efficiency scores are then calculated for each Decision Making Unit comparing the inefficient units to units having the same value as the Most Preferred Solution. The resulting Value Efficiency scores are optimistic approximations of the true scores. The procedure and the resulting efficiency scores are immediately applicable to solving practical problems.

Organizational Form and Efficiency: The Coexistence of Stock and Mutual Property-Liability Insurers

Abstract: This article introduces a new approach, cross-frontier analysis, for estimating the relative efficiency of alternative organizational forms in an industry. The technique is illustrated by analyzing a sample of stock and mutual property-liability insurers using nonparametric frontier efficiency methods. Cross-frontier analysis measures the relative efficiency of each organizational form by computing the efficiency of each stock (mutual) firm relative to a reference set consisting of all mutual (stock) firms. We test agency-theoretic hypotheses about organizational form, including the managerial discretion and expense preference hypotheses. The results indicate that stocks and mutuals are operating on separate production and cost frontiers and thus represent distinct technologies. Consistent with the managerial discretion hypothesis, the stock technology dominates the mutual technology for producing stock outputs and the mutual technology dominates the stock technology for producing mutual outputs. However, consistent with the expense preference hypothesis, the stock cost frontier dominates the mutual cost frontier. Our findings thus suggest a richer interpretation of organizational form than provided by previous researchers.

A study of the efficiency of spanish port authorities using data envelopment analysis

Abstract: This paper presents the results of a study of the relative efficiency of all Spanish Port Authorities during the 1993-1997 period, using the Data Envelopment Analysis technique. The ports are divided into 3 groups according to their complexity. A database was also built with the information coming from the 26 ports using 5 observations for each port; this permitted the comparison among ports in each group as well as their evolution during the period. Results obtained show a different evolution of every group in terms of relative efficiency. Thus, the ports of high complexity offered higher comparative efficiency levels, having gone closer to the frontier over time. The same cannot be said of the medium complexity group where the growth of the efficiency levels during the 5 years was smaller. Ports of low complexity showed a negative evolution in global efficiency levels.

Frontier Production Functions and Technical Efficiency Measures

Abstract: The concept of technical efficiency is central to measuring the firm performance. The measurement of technical efficiency has proved difficult and complex, and the literature provides a range of methodologies. This paper reviews the various methodologies for measuring technical efficiency and offers a comparison between established methods of measurements. The discussion is literary with less mathematical jargons and equations. The objective of this paper is not to be exhaustive, but to be up-to-date and to provide a significant discussion on some of the core methods of measuring technical efficiency.

Measuring maintenance performance: a holistic approach

Abstract: Performance measures should be linked to an organization’s strategy in order to provide useful information for making effective decisions and shaping desirable employee behaviour. The pitfalls relating to the indiscriminate use of common maintenance performance indicators are discussed in this paper. It also reviews four approaches to maintenance performance measures. The value‐based performance measure evaluates the impact of maintenance activities on the future value of the organization. The Balanced Scorecard (BSC) provides a framework for translating strategy into operational measures that collectively capture the critical requirements for sustaining the organization’s success. System audits are the tool for measuring organizational culture, which in turn determines the appropriate approach to the organization of maintenance functions. The operational efficiency of an organization’s maintenance function can be benchmarked with those of its counterparts in other organizations by using Data Envelopment Analysis (DEA). Among these approaches, the one which builds on the BSC embraces the design principles of a good performance measurement system. To smooth the adoption of the BSC approach to managing maintenance operations, a related research agenda is proposed in the concluding section.

An application reference for data envelopment analysis in branch banking: helping the novice researcher

Abstract: Demonstrates the application of data envelopment analysis (DEA) in examining the efficiency of bank branches relative to other branches Shows that accounting variables can be complemented by non‐accounting variables controllable by management The process of generating and interpreting relative efficiency scores and potential improvements is discussed in a language that will be particularly appreciated by managers, consultants and novice researchers The theory needed to use DEA and the key model design considerations are addressed The discussion includes such issues as appropriate sample size, strengths and weaknesses of DEA, analysis options, and an application checklist DEA can be applied to benchmarking best practice branches Management can use DEA to test the established knowledge of branches and initiate investigations when contradictions arise

On estimation of monotone and concave frontier functions

Abstract: When analyzing the productivity of firms, one may want to compare how the firms transform a set of inputs x (typically labor, energy or capital) into an output y (typically a quantity of goods produced). The economic efficiency of a firm is then defined in terms of its ability to operate close to or on the production frontier, the boundary of the production set. The frontier function gives the maximal level of output attainable by a firm for a given combination of its inputs. The efficiency of a firm may then be estimated via the distance between the attained production level and the optimal level given by the frontier function. From a statistical viewpoint, the frontier function may be viewed as the upper boundary of the support of the population of firms density in the input and output space. It is often reasonable to assume that the production frontier is a concave monotone function. Then a famous estimator in the univariate input and output case is the data envelopment analysis (DEA) estimato...

Mutual fund performance appraisals: a multi-horizon perspective with endogenous benchmarking

Abstract: With over 6500 mutual funds available to investors, industry data show that consumers pay a great deal of attention to the ratings of mutual funds. In spite of this attention, however, much controversy surrounds the various industry approaches to the rating of mutual funds. Many industry rating approaches use subjective weights to integrate fund performances over different time horizons; this can give rise to quite different ratings, depending upon the relative importances assigned to different horizons. In this paper, we present two basic quadratic programming approaches for identifying those funds that are strictly dominated, regardless of the weightings on the different time horizons being considered, relative to their mean returns and risks. This effort can be viewed as a novel application of the philosophy of data envelopment analysis, a relatively new, non-parametric frontier estimation technique which focuses on estimating `radial' contraction/expansion potentials. These approaches eliminate any need for subjective tradeoffs, vis-a-vis the importance or meaningfulness of performances over the different horizons. Finally, much useful sensitivity information is automatically provided. Also, in contrast to many studies of mutual fund performance, our approaches endogenously determine a custom-tailored benchmark portfolio to which each mutual fund's performance is compared. All of our approaches are illustrated on a sample of twenty-six actual mutual funds.

An investigation of returns to scale in data envelopment analysis

Abstract: This paper discusses the determination of returns to scale (RTS) in data envelopment analysis (DEA). Three basic RTS methods and their modifications are reviewed and the equivalence between these different RTS methods is presented. The effect of multiple optimal DEA solutions on the RTS estimation is studied. It is shown that possible alternate optimal solutions only affect the estimation of RTS on DMUs which should be classified as constant returns to scale (CRS). Modifications to the original RTS methods are developed to avoid the effects of multiple optimal DEA solutions on the RTS estimation. The advantages and disadvantages of these alternative RTS methods are presented so that a proper RTS method can be selected within the context of different applications.

A Complete Efficiency Ranking of Decision Making Units in Data Envelopment Analysis

Abstract: The efficiency measures provided by DEA can be used for ranking Decision Making Units (DMUs), however, this ranking procedure does not yield relative rankings for those units with 100% efficiency. Andersen and Petersen have proposed a modified efficiency measure for efficient units which can be used for ranking, but this ranking breaks down in some cases, and can be unstable when one of the DMUs has a relatively small value for some of its inputs. This paper proposes an alternative efficiency measure, based on a different optimization problem that removes the difficulties.

A Data Envelopment Analysis of the Operational Efficiency of Bank Branches

Abstract: We performed a multiperiod data envelopment analysis (DEA) study of the efficiencies of selected branches of a large US bank (which we will call Big Bank) over six consecutive quarters (second quarter of 1992 to the third quarter of 1993). We developed a number of innovative application tools, including budgeting and target-setting modules, within a DEA framework that we subsequently customized for Big Bank. Within the performance-evaluation framework, we developed the ability to identify different potential groupings of branches that supported the multiple user views of the system. We also developed procedures to evaluate trends over time and differences in performance across user-defined aggregations of branches. We paid attention to the interface with the end users and, in particular, developed presentation tools to make the outcomes of the analysis available to managers at different levels of the bank.

On the quality of the data envelopment analysis model

Abstract: The user of data envelopment analysis (DEA) has little available guidance on model quality. The technique offers none of the misspecification tests or goodness of fit statistics developed for parametric statistical methods. Yet, if a DEA model is to guide managerial policy, the quality of the model is of crucial importance. This paper suggests four alternative purposes of DEA modelling, and offers four measures of the quality of a DEA model which reflect those purposes. Using Monte Carlo simulation methods, it explores the performance of DEA under a wide variety of assumptions. It notes that four issues will have an important influence on model results: the distribution of true efficiencies in the study sample; the size of the sample; the number of inputs and outputs included in the analysis; and the degree of correlation between inputs and outputs. The paper concludes that any judgement about the reliability of model results must be dependent on the objective of the analysis.