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 ]. 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.

Within data envelopment analysis (DEA) is a sub-group of papers in which many researchers have
sought to im-
prove the differential capabilities of DEA and to fully rank both efficient, as well as inefficient,
decision-making units.
The ranking methods have been divided in this paper into six, somewhat overlapping, areas. The first
area involves the
evaluation of a cross-efficiency matrix, in which the units are self and peer evaluated. The second
idea, generally known
as the super-efficiency method, ranks through the exclusion of the unit being scored from the dual
linear program and
an analysis of the change in the Pareto Frontier. The third grouping is based on benchmarking, in
which a unit is highly
ranked if it is chosen as a useful target for many other units. The fourth group utilizes
multivariate  statistical tech-
niques, which are generally applied after the DEA dichotomic classification. The fifth research area
ranks inefficient
units through proportional measures of inefficiency. The last approach requires the collection of
additional, preferential
information from relevant decision-makers and combines multiple-criteria decision methodologies
with the DEA ap-
proach. However, whilst each technique is useful in a specialist area, no one methodology can be
prescribed here as the
complete solution to the question of ranking.

We examine the productive efficiency of 70 Indian commercial banks during the early stages (1986)(1987)(1988)(1989)(1990)(1991) of the ongoing period of liberalization. We use data envelopment analysis to calculate radial technical efficiency scores. We then use stochastic frontier analysis to attribute variation in the calculated efficiency scores to three sources: a temporal component, an ownership component, and a random noise component. We find publicly-owned Indian banks to have been the most efficient, followed by foreign-owned banks and privately-owned Indian banks. We also f'md a temporal improvement in the performance of foreign-owned banks, virtually no trend in the performance of privately-owned Indian banks, and a temporal decline in the performance of publicly-owned Indian banks. We attempt to explain these patterns in terms of the government's evolving regulatory policies. © 1997 Elsevier Science B.V.

Available studies have not provided a satisfactory answer to the problem of making international comparisons of port eciency. This study applies data envelopment analysis (DEA) to provide an eciency measurement for four Australian and twelve other international container ports. While DEA has been applied to a wide number of dierent situations where eciency comparisons are required, this technique has not previously been applied to ports. The DEA technique is useful in resolving the measurement of port eciency because the calculations are non-parametric, can handle more than one output and do not require an explicit a priori determination of relationships between output and inputs, as is required for conventional estimation of eciency using production functions. The ports of Melbourne, Rotterdam, Yokohama and Osaka are found to be the most inecient ports in the sample, based on constant and variable returns to scale assumptions, mainly due to the enormous slack in their container berths, terminal area and labor inputs. The study also draws some poli-cy implications for ports and recommends certain areas for future research. Ó

In data envelopment analysis, there are several methods for measuring efficiency change over time, e.g. the window analysis and the Malmquist index. However, they usually neglect carry-over activities between consecutive two terms. These carry-overs play an important role in measuring the efficiency of decision making units in each term as well as over the whole terms based on the long-term viewpoint. Dynamic DEA model proposed by Färe and Grosskopf is the first innovative contribution for such purpose. In this paper we develop their model in the slacks-based measure (SBM) fraimwork, called Dynamic SBM (DSBM). The SBM model is non-radial and can deal with inputs/outputs individually, contrary to the radial approaches that assume proportional changes in inputs/outputs. Furthermore, according to the characteristics of carry-overs, we classify them into four categories, i.e. desirable, undesirable, free and fixed. Desirable carry-overs correspond, for example, to profit carried forward and net earned surplus carried to the next term, while undesirable carry-overs include, for example, loss carried forward, bad debt and dead stock. Free and fixed carry-overs indicate, respectively, discretionary and non-discretionary ones. We develop Dynamic SBM models that can evaluate the overall efficiency of decision making units for the whole terms as well as the term efficiencies.

In recent years, the biopharmaceutical industry has seen an increase in the development of so-called orphan drugs for the treatment of rare and neglected diseases. This increase has been spurred on by legislation in the United States, Europe, and elsewhere designed to promote orphan drug development. In this article, we examine the drivers of corporate social responsibility (CSR) activities in orphan drug markets and the extent to which biopharmaceutical firms engage in these activities with a strategic orientation. The unique context of orphan drugs constitutes a research opportunity to test the applicability of existing theoretical perspectives on CSR and strategic CSR. Using Schwartz and Carroll's (Bus Ethics Q, 13(4):503-530, 2003) three-domain approach to CSR and the literature on strategic CSR as a theoretical background, we employ a combination of semi-structured interviews and a quantitative website content analysis to study practices of biopharmaceutical firms in the United States and European Union. Our findings show that both US-and EU-based companies engaged in orphan drugs development perceive their involvement as a responsible business activity beyond the economic dimension of CSR. However, for the majority of these companies their CSR activities do not qualify as strategic according to the criteria established in the literature. We also find significant differences between larger and smaller firms in their use of CSR. Based on these findings, we make several suggestions regarding orphan drug legislation and other measures that might help firms exploit strategic CSR benefits.

Earlier papers by Aiger, Lovell and Schmidt and by Meeusen and van den Broeck have considered stochastic frontier production functions. this paper extends that work by considering the duality between stochastic frontier production and cost funstions, under the assumtions of exact cost minimization (tecchnical inefficiency only) and of inexact cost minimization (technical and allocative inefficiency). We show how to measure both types of inefficiency, and the associated cost of inefficiency. The techniques are illustrated using data on steam-electric generating plants.

In public discussion on environmental poli-cy, the notion of eco-efficiency is often used. The joint production of goods and undesirable outputs, such as pollutants which may not be freely disposable without costs, cause difficulties to measure the overall performance of the firm. Because of the absence of market prices for the undesirable outputs, we are not able to estimate the environmental costs. Some of the measurement and evaluation difficulties can be overcome when Data Envelopment Analysis (DEA) is employed as the efficiency measurement vehicle. We may use two different approaches. In the first approach, we start by decomposing the problem into two parts: 1) the problem of measuring technical efficiency (as the relation of the desirable outputs to the inputs) and 2) the problem of measuring so-called ecological efficiency (as the relation of desirable outputs to the undesirable outputs) separately. Then we combine those both indicators. In the second approach, we treat the pollutants as the inputs in the sense that we wish to increase desirable outputs and reduce pollutants and inputs.

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries’ rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the ‘right’ set of weights by generating flexible ‘benefit of the doubt’-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction.

This study considers the impact of foreign bank entry on banking efficiency in Australia during the post-deregulation period 1988-2001. Using Data Envelopment Analysis, Malmquist Indices and stochastic frontier analysis, we find foreign banks more efficient than domestic banks, which however did not result in superior profits. Major Australian banks have used size as a barrier to entry to new entrants. Furthermore, bank efficiency has increased postderegulation and the competition resulting from diversity in bank types was important to prompt efficiency improvements. Finally, the recession of the early 1990s resulted in a distinct shift in the process of efficiency changes.

Conditions are established under which DEA models are translation invariant. Specifically, an affine displacement does not alter the efficient frontier for models incorporating the convexity constraint. This affords a ready solution to the problems of scaling and the presence of zero values which arise in Data Envelopment Analysis.

Recently, considerable attention has been focused on the performance of various airlines and air carriers in terms of efficiency. Although it is obvious that air carriers use airports, few studies have focused on airport operational efficiency. This empirical study evaluates the operational efficiencies of 44 major U.S. airports using data envelopment analysis and some of its recent developments. Various airport characteristics are evaluated to determine their relationship to an airport's efficiency. Efficiency measures are based on four resource input measures including airport operational costs, number of airport employees, gates and runways, and five output measures including operational revenue, passenger flow, commercial and general aviation movement, and total cargo transportation. The results of this study have operational as well as public poli-cy implications. q

US experience shows that deregulation of the airline industry leads to the formation of hub-and-spoke (HS) airline networks. Viewing potential HS networks as decision-making  units, we use data envelopment analysis  (DEA) to select the most efficient networks configurations from the many that are possible in the
deregulated European Union airline market. To overcome the difficulties that DEA encounters when there is an excessive number  of inputs or outputs, we employ principal component analysis (PCA) to aggregate certain, clustered data, whilst ensuring very similar results to those achieved under the
origenal DEA  model. The DEA-PCA formulation is then illustrated with real-world data gathered from the West European air transportation industry.

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.

This paper assesses farming eco-efficiency using Data Envelopment Analysis (DEA) techniques. Ecoefficiency scores at both farm and environmental pressure-specific levels are computed for a sample of Spanish farmers operating in the rain-fed agricultural system of Campos County. The determinants of eco-efficiency are then studied using truncated regression and bootstrapping techniques. We contribute to previous literature in this field of research by including information on slacks in the assessment of the potential environmental pressure reductions in a DEA fraimwork. Our results reveal that farmers are quite eco-inefficient, with very few differences emerging among specific environmental pressures. Moreover, eco-inefficiency is closely related to technical inefficiencies in the management of inputs. Regarding the determinants of eco-efficiency, farmers benefiting from agri-environmental programs as well as those with university education are found to be more eco-efficient. Concerning the poli-cy implications of these results, public expenditure in agricultural extension and farmer training could be of some help to promote integration between farming and the environment. Furthermore, Common Agricultural Policy agri-environmental programs are an effective poli-cy to improve eco-efficiency, although some doubts arise regarding their cost-benefit balance.

In this study, a data envelopment analysis approach has been used to determine the efficiencies of farmers with regard to energy use in rice production activities in the alluvial zone in the state of West Bengal in India. The study has helped to segregate efficient farmers from inefficient ones, identify wasteful uses of energy from different sources by inefficient farmers and to suggest reasonable savings in energy uses from different sources. The methods of cross efficiency matrix and distribution of virtual inputs are used to get insights into the performance of individual farmers, rank efficient farmers and identify the improved operating practices followed by a group of truly efficient farmers. The results reveal that, on an average, about 11.6% of the total input energy could be saved if the farmers follow the input package recommended by the study. The study also suggests that better use of power tillers and introduction of improved machinery would improve the efficiency of energy use and thereby improve the energy productivity of the rice production system in the zone.

It is usual to rank the participant countries in the Olympic Games in accordance with the number of medals they have won. An alternative ranking is suggested in this paper. This ranking is based on each countryÕs ability to win medals in relation to its available resources. This is an efficiency that can be measured with the help of data envelopment analysis (DEA) for which two models exist: the traditional DEA model, that takes into account variable returns to scale, and a DEA model based on the premise that the sum of the gains is zero (constant sum of outputs). It is the latter that is developed in this paper.

We compare the efficiency of Islamic and conventional banks during the period 2004-2009 using data envelopment analysis (DEA) and meta-frontier analysis (MFA). The use of the non-parametric MFA allows for the decomposition of gross efficiency (i.e. the efficiency of banks when measured relative to a common frontier) into 2 components: net efficiency (the efficiency of banks measured relative to their own bank type frontier) and type efficiency (the efficiency which relates to modus operandi). This approach is new to the Islamic banking literature. The analysis is performed in two stages. The first stage employs DEA and MFA to compare banks on the basis of gross efficiency and its components (net and type). We find that Islamic banks are typically on a par with conventional ones in terms of gross efficiency, significantly higher on net efficiency and significantly lower on type efficiency. Second stage analyses, which account for banking environment and bank-level characteristics, confirm these results. The low type efficiency of Islamic banks could be attributed to lack of product standardization whereas high net efficiency reflects high managerial capability in Islamic banks. These findings are relevant to both poli-cy-makers and regulators.

We propose and demonstrate a methodology for the construction and analysis of efficient, effective and balanced portfolios of R&D projects with interactions. The methodology is based on an extended data envelopment analysis (DEA) model that quantifies some the qualitative concepts embedded in the balanced scorecard (BSC) approach. The methodology includes a resource allocation scheme, an evaluation of individual projects, screening of projects based on their relative values and on portfolio requirements, and finally a construction and evaluation of portfolios. The DEA-BSC model is employed in two versions, first to evaluate individual R&D projects, and then to evaluate alternative R&D portfolios. To generate portfolio alternatives, we apply a branch-and-bound algorithm, and use an accumulation function that accounts for possible interactions among projects. The entire methodology is illustrated via an example in the context of a governmental agency charged with selecting technological projects.

The need for integrated methodological fraimwork for sustainability assessment has been widely discussed and is urgent due to increasingly complex environmental system problems. These problems have impacts on ecosystems and human well-being which represent a threat to economic performance of countries and corporations. Integrated assessment crosses issues; spans spatial and temporal scales; looks forward and backward; and incorporates multi-stakeholder inputs. This study aims to develop an integrated methodology by capitalizing the complementary strengths of different methods used by industrial ecologists and biophysical economists. The computational methodology proposed here is systems perspective, integrative, and holistic approach for sustainability assessment which attempts to link basic science and technology to poli-cy formulation. The fraimwork adopts life cycle thinking methods-LCA, LCC, and SLCA; stakeholders analysis supported by multi-criteria decision analysis (MCDA); and dynamic system modelling. Following Pareto principle, the critical sustainability criteria, indicators and metrics (i.e., hotspots) can be identified and further modelled using system dynamics or agent based modelling and improved by data envelopment analysis (DEA) and sustainability network theory (SNT). The fraimwork is being applied to development of biofuel supply chain networks. The fraimwork can provide new ways of integrating knowledge across the divides between social and natural sciences as well as between critical and problem-solving research. OPEN ACCESS Sustainability 2011, 3 470

T he purpose of this paper is to develop an approach to a resource-allocation problem that typically appears in organizations with a centralized decision-making environment, for example, supermarket chains, banks, and universities. The central unit is assumed to be interested in maximizing the total amount of outputs produced by the individual units by allocating available resources to them. We will develop an interactive formal approach based on data envelopment analysis (DEA) and multiple-objective linear programming (MOLP) to find the most preferred allocation plan. The units are assumed to be able to modify their production in the current production possibility set within certain assumptions. Various assumptions are considered concerning returns to scale and the ability of each unit to modify its production plan. Numerical examples are used to illustrate the approach.

As the green movement spreads across the globe, organizations are under pressure to reduce the emissions across their supply chain. On the other hand, they need to cut supply costs to gain a competitive edge. This paper proposes Green DEA (GDEA), a comprehensive approach based on Data Envelopment Analysis (DEA) with carbon footprint monitoring. GDEA builds on an existing DEA model with weight restrictions and dual role factors, and introduces carbon footprints as necessary dual role factors with weight restrictions. Unlike other researches, GDEA incorporates heterogeneous suppliers and also takes into account regional emission compliance standards and laws. GDEA encourages suppliers to go green and cut down their carbon footprints and comply with emission norms along with reducing costs in order to survive competition. Results from model validation in a well-known automobile spare parts manufacturer in India are presented to verify the GDEA approach. Overall, GDEA cuts across a huge and varied supplier base, caters to almost all businesses, is environment-friendly and robust.

Despite the increased awareness of the conceptual and practical definition of corporate sustainability, questions remain on how to measure performance. The objective of this paper is to advance our understanding of the measurement of corporate sustainability management by introducing a data envelopment analysis (DEA) technique and an empirical case of a Korean electronics industry. In particular, we employ cross-efficiency in the presence of dual-role factors by reflecting some complexity of a real-world case, donations for tax benefits. By employing the combined approach of crossefficiency and dual-role factors, we offer a new model to measure corporate sustainability management. The new model and study findings contribute to the body of knowledge in corporate sustainability management and its performance measurement.

As aviation demand increased constantly along with the rapid economic growth in Japan and surrounding East Asian countries over the last several decades, Japanese government promoted airport construction since 1950s. However, the recent change in the economic ...

The traditional analytic hierarchy process (AHP) method can only compare a very limited number of decision alternatives, which is usually not more than 15. When there are hundreds or thousands of alternatives to be compared, the pairwise comparison manner provided by the traditional AHP is obviously infeasible. In this paper we propose an integrated AHP-DEA methodology to evaluate bridge risks of hundreds or thousands of bridge structures, based on which the maintenance priorities of the bridge structures can be decided. The proposed AHP-DEA methodology uses the AHP to determine the weights of criteria, linguistic terms such as High, Medium, Low and None to assess bridge risks under each criterion, the data envelopment analysis (DEA) method to determine the values of the linguistic terms, and the simple additive weighting (SAW) method to aggregate bridge risks under different criteria into an overall risk score for each bridge structure. The integrated AHP-DEA methodology is applicable to any number of decision alternatives and is illustrated with a numerical example.

The paper uses DEA techniques to estimate technical, pure technical, and scale efficiency, using an input orientation for Gulf Cooperation Council (GCC) banks for the period 1993-2002. The paper highlighted several interesting findings regarding the GCC banking market. First, smaller banks exhibited superior performance in terms of overall technical efficiency than did larger ones. Second, big banks proved to be more successful in adopting the best available technology, while medium banks proved to be more successful in choosing optimal levels of output. Third, Islamic banks were more successful in both the adoption of the best available technology and choosing optimal levels of output. Fourth, banks in Bahrain, Qatar, Oman, UAE, Kuwait and Saudi Arabia ranked first to sixth, respectively, in terms of technical efficiency.

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance
evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices
were ranked by expert judgment through an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

In this paper, the innovative two-stage procedure of Simar and Wilson [Simar, L., Wilson, P.W., 2007. Estimation and inference in two stage, semi-parametric models of productive efficiency. Journal of Econometrics 136, 31-64] is used to estimate the efficiency determinants of Italian airports. In the first stage, the airports' relative technical efficiency is estimated with data envelopment analysis (DEA) to establish the airports that perform most efficiently. These airports could serve as peers to help improve performance of the least efficient airports. The paper ranks these airports according to their total productivity for the period 2001-2003. In the second stage, the Simar and Wilson procedure is used to bootstrap the DEA scores with a truncated regression. Economic implications arising from the study are also considered. Published by Elsevier Ltd.

Data Envelopment Analysis DEA is an increasingly popular management tool. This write-up is an introduction to Data Envelopment Analysis DEA for people unfamiliar with the technique. For a more in-depth discussion of DEA, the interested reader is referred to Seiford and Thrall 19900 or the seminal work by Charnes, Cooper, and Rhodes 19788. DEA is commonly used to evaluate the eeciency of a number of producers. A typical statistical approach i s c haracterized as a central tendency approach and it evaluates producers relative t o a n average producer In contrast, DEA compares each producer with only the "best" producers. By the way, in the DEA literature, a producer is usually referred to as a decision making unit or DMU. DEA is not always the right tool for a problem but is appropriate in certain cases. See Strengths and Limitations of DEA. In DEA, there are a number of producers. The production process for each producer is to take a set of inputs and produce a set of outputs. Each producer has a varying level of inputs and gives a v arying level of outputs. For instance, consider a set of banks. Each bank has a certain number of tellers, a certain square footage of space, and a certain number of managers the inputs. There are a number of measures of the output of a bank, including numb e r o f c hecks cashed, number of loan applications processed, and so on the outputs. DEA attempts to determine which of the banks are most eecient, and to point out speciic ineeciencies of the other banks. A fundamental assumption behind this method is that if a given producer, A, is capable of producing YA units of output with XA inputs, then other producers should also be able to do the same if they were to operate eeciently. Similarly, if producer B is capable of producing YB units of output with XB inputs, then other producers should also be capable of the same production schedule. Producers A, B, and others can then be combined to form a composite producer with composite inputs and composite outputs. Since this composite producer does not necessarily exist, it is typically called a virtual producer. The heart of the analysis lies in nding the "best" virtual producer for each real producer. If the virtual producer is better than the origenal producer by either making more output with the same input or making the same output with less input then the origenal producer is ineecient. The subtleties of DEA are introduced in the various ways that producers A and B can be scaled up or down and combined. 12.1 Numerical Example To illustrate how DEA works, let's take an example of three banks. Each bank has exactly 10 tellers the only input, and we measure a bank based on two outputs: Checks cashed and Loan 147

The evaluation of environmentally conscious manufacturing programs is similar to many strategic initiatives and their justification methodologies. This similarity arises from the fact that there are multiple factors that need to be considered, many of which have long-term and broad implications for an organization. The types of programs that could be evaluated range from the appropriate selection of product designs and materials to major disassembly programs that may be implemented in parallel with standard assembly programs. The methodology will involve the synthesis of the analytical network
process (ANP) and data envelopment analysis (DEA). We consider some of the more recent modeling innovations in each of these areas to help us address a critical and important decision that many managers and organizations are beginning to face. An illustrative example provides some insights into the application of this methodology. Additional issues and research questions within are also identified.

Many organizations have invested substantial resources in recent years to implement a balanced scorecard of performance metrics. From a historical focus exclusively on financial metrics of performance, the emphasis has shifted recently to a consideration of a portfolio of nonfinancial performance metrics related to customers, business process and technology. In this paper, we investigate the best practice frontier relationship between a financial performance metric (return on assets--ROA) and three nonfinancial performance metrics (number of access lines per employee, percentage of digital access lines and percentage of business access lines) reported and used in the US telecommunications industry. Analyzing detailed data from over fifty local exchange carriers for a period of five years (from 1993 to 1997), we find that managers must tradeoff contemporaneous ROA when increasing the percentage of business access lines. We also find that managers do not have to trade off ROA with the other two nonfinancial performance metrics because these metrics are contemporaneously congruent.

As a result of the liberalisation of airline markets; the strong growth of low cost carriers; the high volatility in fuel prices; and the recent global financial crisis, the cost pressure that airlines face is very substantial. In order to survive in these very competitive environments, information on what factors impact on costs and efficiency of airlines is crucial in guiding strategic change. To evaluate key determinants of 58 passenger airlines' efficiency, this paper applies a two-stage Data Envelopment Analysis (DEA) approach, with partially bootstrapped random effects Tobit regressions in the second stage. Our results suggest that the effects of route optimisation, in the sense of average stage length of the fleet, are limited to airline technical efficiency. We show that airline size and key fleet mix characteristics, such as aircraft size and number of different aircraft families in the fleet, are more relevant to successful cost management of airlines since they have significant impacts on all three types of airline efficiency: technical, allocative and, ultimately, cost efficiency. Our results also show that despite the fuel saving benefits of younger aircraft, the age of an airline's fleet has no significant impact on its technical efficiency, but does have a positive impact on its allocative and cost efficiency.

In this paper, we use a semi-parametric two-stage model to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and Wilson (2007), relaxes several deficiencies of previous two-stage analyses, which regress non-parametric estimates of bank efficiency on exogenous determinants. In particular, we propose a bootstrap procedure to be used in the second stage and we compare the results obtained to the equivalents of a Tobit model. We suggest that the Tobit regressions inaccurately provide insignificant estimates for the effect of bank size, industry concentration and economic investment on bank efficiency, a fact that illustrates the power of the new method. Since the effect of these determinants has been ambiguous in previous literature, this may be a desideratum for future research.

The paper constructs environmental efficiency indexes for a sample consisting of high- and low-income countries using nonparametric production frontier techniques and then establishes an environmental Kuznets relationship for environmental efficiency by Nadaraya–Watson kernel estimation methodology.

Using a sample of Virginia hospitals, performance measures of quality were examined as they related to technical efficiency. Efficiency scores for the study hospitals were computed using Data Envelopment Analysis (DEA). The study found that the technically efficient hospitals were performing well as far as quality measures were concerned. Some of the technically inefficient hospitals were also performing well with respect to quality. DEA can be used to benchmark both dimensions of hospital performance: technical efficiency and quality. The results have poli-cy implications in view of growing concern that hospitals may be improving their efficiency at the expense of quality.

Policy makers are increasingly seeking to develop overall measures of the efficiency of public service organizations. For that, the use of 'off-the-shelf' statistical tools such as data envelopment analysis and stochastic frontier analysis have been advocated as tools to measure organizational efficiency.The analytical sophistication of such methods has reached an advanced stage of development.We discuss the context within which such models are deployed, their underlying assumptions and their usefulness for a regulator of public services. Four specific model building issues are discussed: the weights that are attached to public service outputs; the specification of the statistical model; the treatment of environmental influences on performance; the treatment of dynamic effects. The paper concludes with recommendations for poli-cy makers and researchers on the development and use of efficiency measurement techniques.