Showing papers on "Data envelopment analysis published in 2008"
TL;DR: In this paper, the concept of a metafrontier is used to compare the technical efficiencies of firms that may be classified into different groups. And the authors present the basic analytical framework necessary for the definition of a meta-frontier, shows how a meta-frontiers can be estimated using non-parametric and parametric methods, and presents an empirical application using cross-country agricultural sector data.
Abstract: This paper uses the concept of a metafrontier to compare the technical efficiencies of firms that may be classified into different groups. The paper presents the basic analytical framework necessary for the definition of a metafrontier, shows how a metafrontier can be estimated using non-parametric and parametric methods, and presents an empirical application using cross-country agricultural sector data. The paper also explores the issues of technological change, time-varying technical inefficiency, multiple outputs, different efficiency orientations, and firm heterogeneity.
1,162 citations
TL;DR: The relational model developed in this paper is more reliable in measuring the efficiencies and consequently is capable of identifying the causes of inefficiency more accurately.
1,112 citations
TL;DR: A literature survey on the application of data envelopment analysis (DEA) to E&E studies is presented and an introduction to the most widely used DEA techniques is introduced.
1,068 citations
TL;DR: An extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as "real-world" applications from inception to the year 2007 is presented.
Abstract: Since the original Data Envelopment Analysis (DEA) study by Charnes et al. [Measuring the efficiency of decision-making units. European Journal of Operational Research 1978;2(6):429–44], there has been rapid and continuous growth in the field. As a result, a considerable amount of published research has appeared, with a significant portion focused on DEA applications of efficiency and productivity in both public and private sector activities. While several bibliographic collections have been reported, a comprehensive listing and analysis of DEA research covering its first 30 years of history is not available. This paper thus presents an extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as “real-world” applications from inception to the year 2007. A listing of the most utilized/relevant journals, a keyword analysis, and selected statistics are presented.
994 citations
TL;DR: The results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity.
Abstract: A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision-making unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology.
700 citations
TL;DR: Cautious conclusions are that public provision may be potentially more efficient than private, in certain settings, and some criteria for assessing the use and usefulness of efficiency studies are established, to help both researchers and those assessing whether or not to act upon published results.
Abstract: The measurement of efficiency and productivity of health service delivery has become a small industry. This is a review of 317 published papers on frontier efficiency measurement. The techniques used are mainly based on non-parametric data envelopment analysis, but there is increasing use of parametric techniques, such as stochastic frontier analysis. Applications to hospitals and other health care organizations and areas are reviewed and summarised, and some meta-type analysis undertaken. Cautious conclusions are that public provision may be potentially more efficient than private, in certain settings. The paper also considers conceptualizations of efficiency, and points to dangers and opportunities in generating such information. Finally, some criteria for assessing the use and usefulness of efficiency studies are established, with a view to helping both researchers and those assessing whether or not to act upon published results.
695 citations
TL;DR: A software package for computing non-parametric efficiency estimates, making inference, and testing hypotheses in frontier models, as well as computation of some new, robust estimators of efficiency, etc.
Abstract: This paper describes a software package for computing non-parametric efficiency estimates, making inference, and testing hypotheses in frontier models. Commands are provided for bootstrapping as well as computation of some new, robust estimators of efficiency, etc.
482 citations
TL;DR: The pure measures under different situations and a mixed measure under the VRS environmental DEA technology for measuring environmental performance are proposed and the measures that deal with nonlinear programming models are given.
466 citations
TL;DR: In this article, the authors presented several DEA-type linear programming models for measuring economy-wide energy efficiency performance, which treated different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency.
440 citations
TL;DR: In this paper, the authors examined and extended these models using game theory concepts and showed that the non-cooperative approach yields a unique efficiency decomposition under multiple intermediate measures, while the centralized approach is likely to yield multiple decompositions.
Abstract: Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). This tool has been utilized by a number of authors to examine two-stage processes, where all the outputs from the first stage are the only inputs to the second stage. The current article examines and extends these models using game theory concepts. The resulting models are linear, and imply an efficiency decomposition where the overall efficiency of the two-stage process is a product of the efficiencies of the two individual stages. When there is only one intermediate measure connecting the two stages, both the noncooperative and centralized models yield the same results as applying the standard DEA model to the two stages separately. As a result, the efficiency decomposition is unique. While the noncooperative approach yields a unique efficiency decomposition under multiple intermediate measures, the centralized approach is likely to yield multiple decompositions. Models are developed to test whether the efficiency decomposition arising from the centralized approach is unique. The relations among the noncooperative, centralized, and standard DEA approaches are investigated. Two real world data sets and a randomly generated data set are used to demonstrate the models and verify our findings. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 55: 643-653, 2008
433 citations
TL;DR: Wang et al. as mentioned in this paper conducted an eco-efficiency analysis for regional industrial systems in China by developing data envelopment analysis (DEA) based models, which showed that Tianjing, Shanghai, Guangdong, Beijing, Hainan and Qinghai are relatively eco-efficient.
TL;DR: In this article, a framework for the design and evaluation of sustainable logistic networks, in which profitability and environmental impacts are balanced, is presented, and the expected computational difficulties of using the MOP approach in the design of sustainable networks, and a technique, based on the commonalities between data envelopment analysis (DEA) and MOP, is introduced to evaluate the efficiency of existing logistic network.
21 Feb 2008
TL;DR: This chapter deals with the measurement of efficiency through the nonparametric, mathematical programming-based technique better known as data envelopment analysis (DEA), which can be used to address a large variety of questions about the transformation of inputs into outputs by a DMU.
Abstract: This chapter deals with the measurement of efficiency through the nonparametric, mathematical programming-based technique better known as data envelopment analysis (DEA). A producer is defined as an economic agent that takes a set of inputs and transforms them either in form or in location into a set of outputs. In DEA, the economic agent is referred to as a decision-making unit (DMU) to accord with the notion that we are assessing entities that have control over the processes they deploy to convert their inputs into outputs. The DEA can be used to address a large variety of questions about the transformation of inputs into outputs by a DMU. These include questions such as the relative efficiency of a DMU (e.g., how far short are its output levels from maximum levels attainable for its input levels?); identification of "suitable" efficient peers for an inefficient DMU to emulate; and estimates of input-output levels that would render a DMU efficient (i.e., targets for the DMU). The advantages and limitations of the DEA models are discussed.
TL;DR: The results show that the proposed approach allows decision makers to perform trade-off analysis among expected costs, quality acceptance levels, and on-time delivery distributions and provides alternative tools to evaluate and improve supplier selection decisions in an uncertain supply chain environment.
TL;DR: This paper provides the first taxonomy of hospital efficiency studies that uses data envelopment analysis (DEA) and related techniques and takes a longitudinal perspective that illustrates the life cycle of this research, as well as its diffusion across disciplines.
Abstract: This paper provides the first taxonomy of hospital efficiency studies that uses data envelopment analysis (DEA) and related techniques. We provide a systematic review of 79 such studies published from 1984–2004 that represent 12 countries. Only studies written in English are considered. A cross-national comparison reveals significant differences with respect to important study characteristics such as type of DEA model selected and choice of input and output categories. Compared with US studies, European efforts are more likely to measure allocative rather than technical efficiency, use longitudinal data, and use fewer observations. We take a longitudinal perspective that illustrates the life cycle of this research, as well as its diffusion across disciplines. Our taxonomy can be used by policy makers and researchers to review past, and assemble new, DEA models.
TL;DR: The original DEA cross-efficiency concept is generalized to game cross efficiency, where each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate.
Abstract: In this paper, we examine the cross-efficiency concept in data envelopment analysis (DEA). Cross efficiency links one decision-making unit's (DMU) performance with others and has the appeal that scores arise from peer evaluation. However, a number of the current cross-efficiency approaches are flawed because they use scores that are arbitrary in that they depend on a particular set of optimal DEA weights generated by the computer code in use at the time. One set of optimal DEA weights (possibly out of many alternate optima) may improve the cross efficiency of some DMUs, but at the expense of others. While models have been developed that incorporate secondary goals aimed at being more selective in the choice of optimal multipliers, the alternate optima issue remains. In cases where there is competition among DMUs, this situation may be seen as undesirable and unfair. To address this issue, this paper generalizes the original DEA cross-efficiency concept to game cross efficiency. Specifically, each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate. The average game cross-efficiency score is obtained when the DMU's own maximized efficiency scores are averaged. To implement the DEA game cross-efficiency model, an algorithm for deriving the best (game cross-efficiency) scores is presented. We show that the optimal game cross-efficiency scores constitute a Nash equilibrium point.
TL;DR: A multi-activity network data envelopment analysis model that represents both production and consumption technologies in a unified framework is applied to simultaneously estimate passenger and freight technical efficiency, service effectiveness, and technical effectiveness for 20 selected railways for the year 2002.
Abstract: This paper provides a multi-activity network data envelopment analysis model that represents both production and consumption technologies in a unified framework. The model is applied to simultaneously estimate passenger and freight technical efficiency, service effectiveness, and technical effectiveness for 20 selected railways for the year 2002. The results show that these measures differ significantly. Since the multi-activity network data envelopment analysis models the reality of railways’ operations, one can gain further insights from the estimated results and thus propose strategies for improving operational performance.
TL;DR: In this article, the authors derived the asymptotic distribution of DEA estimators under variable returns to scale and proved consistency of two different bootstrap procedures (one based on subsampling, the other based on smoothing).
Abstract: Nonparametric data envelopment analysis (DEA) estimators based on linear programming methods have been widely applied in analyses of productive efficiency. The distributions of these estimators remain unknown except in the simple case of one input and one output, and previous bootstrap methods proposed for inference have not been proved consistent, making inference doubtful. This paper derives the asymptotic distribution of DEA estimators under variable returns to scale. This result is used to prove consistency of two different bootstrap procedures (one based on subsampling, the other based on smoothing). The smooth bootstrap requires smoothing the irregularly bounded density of inputs and outputs and smoothing the DEA frontier estimate. Both bootstrap procedures allow for dependence of the inefficiency process on output levels and the mix of inputs in the case of input-oriented measures, or on input levels and the mix of outputs in the case of output-oriented measures.
TL;DR: This work presents and demonstrates a multi-criteria approach for evaluating R&D projects in different stages of their life cycle that integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model.
Abstract: We present and demonstrate a multi-criteria approach for evaluating R&D projects in different stages of their life cycle. Our approach integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model. The input and output measures for the integrated DEA–BSC model are grouped in “cards” which are associated with a “BSC for R&D projects”. The BSC is embedded in the DEA model through a hierarchical structure of constraints that reflect the BSC balance considerations. We illustrate the proposed approach with a case study involving an industrial research laboratory that selects and executes dozens of R&D projects every year.
TL;DR: In this paper, the impact of regulations and supervision approaches on banks' technical efficiency was investigated using Tobit regression and a two-stage data envelopment analysis (DEA) method.
Abstract: This study uses a sample of 715 banks from 95 countries and two-stage data envelopment analysis (DEA) to provide international evidence on the impact of regulations and supervision approaches on banks’ efficiency. We first use DEA to estimate technical and scale efficiency. We then use Tobit regression to investigate the impact of several regulations related to capital adequacy, private monitoring, banks’ activities, deposit insurance schemes, disciplinary power of the authorities, and entry into banking on banks’ technical efficiency. We estimate several specifications while controlling for bank-specific attributes and country-level characteristics accounting for macroeconomic conditions, financial development, market structure, overall institutional development, and access to banking services. In several cases, the results provide evidence in favour of all three pillars of Basel II that promote the adoption of strict capital adequacy standards, the development of powerful supervisory agencies, and the creation of market disciplining mechanisms. However, only the latter one is significant in all of our specifications. While the remaining regulations do not appear to have a robust impact on efficiency, several other country-specific characteristics are significantly related to efficiency.
TL;DR: This paper seeks to extend the model of Doyle and Green by introducing a number of different secondary objective functions, which provide a ranking among the decision-making units (DMUs) and eliminates unrealistic DEA weighting schemes without requiring a priori information on weight restrictions.
TL;DR: In this paper, the authors applied data envelopment analysis (DEA) to assess the relative efficiency of the academic departments at National Cheng Kung University in Taiwan, and four groups of departments of similar characteristics were categorized via an efficiency decomposition and cluster analysis.
Abstract: Universities play an important role in the development of a country in this age of the knowledge economy. As government subsidies to universities have been reducing in recent years, the more efficient use of resources becomes an important issue for university administrators. This paper applies data envelopment analysis (DEA) to assess the relative efficiency of the academic departments at National Cheng Kung University in Taiwan. The outputs considered are total credit-hours, publications, and external grants; and the inputs utilized by the departments are personnel, operating expenses, and floor space. An assurance region is constructed by the top administrators of the university to confine the flexibility in selecting the virtual multipliers in DEA. Four groups of departments of similar characteristics are categorized via an efficiency decomposition and cluster analysis. The aggregate efficiency indicates whether the resources have been utilized efficiently by a department and the efficiency decomposition helps identify the weak areas where more effort should be devoted so that the efficiency of the department can be improved.
TL;DR: A production-theoretical approach to decomposing the change of aggregate CO2 emissions over time using the Shephard input distance functions and the environmental data envelopment analysis (DEA) technology in production theory is presented.
TL;DR: In this article, the authors used data envelopment analysis (DEA) to examine the relative efficiency in the production of research of 109 Chinese regular universities in 2003 and 2004, and found that mean research efficiency is higher in comprehensive universities compared to specialist universities, and in universities located in the coastal region compared to those in the western region of China.
TL;DR: In this article, uncertainty and sensitivity analysis are used to assess the robustness of the final outcome and to analyse how much each source of uncertainty contributes to the output variance, using the Technology Achievement Index as an illustration.
Abstract: Composite indicators (CIs) are often used for benchmarking countries' performance, but they frequently stir controversies about the unavoidable subjectivity in their construction. Data Envelopment Analysis helps to overcome some key limitations, as it does not need any prior information on either the normalization of sub-indicators or on an agreed unique set of weights. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries' CI scores and rankings. Uncertainty and sensitivity analysis are therefore needed to assess the robustness of the final outcome and to analyse how much each source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.
TL;DR: This paper proposes that the zero sum gains DEA (ZSG-DEA) models look especially suitable for treating equilibrium models, where the sum of the quantities produced by all decision-making units can be set as the upper admissible bound.
Abstract: Data envelopment analysis (DEA) literature has proposed alternative models for performance assessment in the presence of undesirable outputs, such as pollutant emissions, where increased outputs imply reduced performance. However, the case where global equilibrium of outputs should be imposed has not yet been considered. We propose that the zero sum gains DEA (ZSG-DEA) models look especially suitable for treating equilibrium models, where the sum of the quantities produced by all decision-making units can be set as the upper admissible bound. This paper uses ZSG-DEA models to evaluate the carbon dioxide emission case study, which can be considered part of the Kyoto Protocol statement.
TL;DR: In this article, the authors examined the different options available in the literature for incorporating non-controllable inputs in a data envelopment analysis in order to determine the most appropriate model for evaluating schools.
Abstract: Measuring efficiency in the education sector is a highly complex task. One of the reasons is that the main resource of schools (the type of students they have) lie outside of their control, which means that it must be treated differently to other factors in analysis. This study examines the different options available in the literature for incorporating non-controllable inputs in a data envelopment analysis in order to determine the most appropriate model for evaluating schools. Our empirical study presents the results obtained using the model proposed by Fried et al . (1999), though we use bootstrap techniques to avoid problems of bias in the estimations.
Journal Article•
TL;DR: In this paper, the authors compared the cost, revenue and profit efficiency of 43 Islamic and 37 conventional banks over the period 1990-2005 in 21 countries using Data Envelopment Analysis and found that there are no significant differences between the overall efficiency results of conventional versus Islamic banks.
Abstract: This paper measures and compares the cost, revenue and profit efficiency of 43 Islamic and 37 conventional banks over the period 1990-2005 in 21 countries using Data Envelopment Analysis. It assesses the average and overtime efficiency of those banks based on their size, age, and region using static and dynamic panels. The findings suggest that there are no significant differences between the overall efficiency results of conventional versus Islamic banks. Overall, the results in this paper are favorable with the ‘new’ banking system.
TL;DR: In this article, the authors used data envelopment analysis (DEA) to investigate the efficiency of the Greek commercial banking industry over the period 2000-2004, and found that the inclusion of loan loss provisions as an input increases the efficiency scores, but off-balance sheet items do not have a significant impact.
TL;DR: In this paper, the authors proposed a unit-invariant leanness measure with a self-contained benchmark to quantify the leanness level of manufacturing systems, based on the concept of data envelopment analysis (DEA).
Abstract: Various lean tools and techniques have been developed for process improvement. In order to track the progress, lean metrics were developed correspondingly. However, an integrated and quantitative measure of overall leanness level has not been established. This paper proposes a unit-invariant leanness measure with a self-contained benchmark to quantify the leanness level of manufacturing systems. Evolved from the concept of data envelopment analysis (DEA), the leanness measure extracts the value-adding investments from a production process to determine the leanness frontier as a benchmark. A linear program based on slacks-based measure (SBM) derives the leanness score that indicates how lean the system is and how much waste exists. Using the score, impacts of various lean initiatives can be quantified as decision support information complementing the existing lean metrics.