Slack-adjusted DEA for time series analysis: Performance measurement of Japanese electric power generation industry in 1984–1993

Using a new slack-adjusted data envelopment analysis (SA-DEA) model which explicitly incorporates an influence of slacks into its efficiency measurement, this study discusses a use of various efficiencies and index measures for DEA dynamic analysis. An analytical formulation to determine the type of return to scale (RTS) is proposed for the new DEA model. This paper mathematically discusses when multiple solutions occur on RTS and how to deal with such a difficulty. As an important case study, this paper applies the proposed DEA approach to examine the performance of Japanese electric power generation companies from 1984 to 1993. Two policy implications are suggested for guiding the Japanese electric power industry.     

Pricing public health care services using DEA: Methodology versus politics

The New Zealand public health sector has used DEA since 1997 to identify efficient expenditure levels to set prices for hospital services at the DRG level. Given the size of the expenditure (NZ$ 2.6 billion), considerable robustness was required for the results and sophistication of the models/process. While the model development and application appeared to be successful, politics overturned the results in the short run. In the longer term, the results have been shown to be reasonably robust and have become a base-line reference for future developments. As such, this paper reports a relatively successful transfer of theory into practice.     

DEA-discriminant analysis in the view of goal programming

This article identifies differences and similarities between DEA (Data Envelopment Analysis) and DA (Discriminant Analysis) in the view of GP (Goal Programming). Based upon such characterization, this article proposes a new type of DA technique, referred to as “DEA-Discriminant Analysis (DEA-DA)”, that incorporates a methodological strength of DEA into the DA formulation. This research applies the proposed DEA-DA method to both an illustrative data set and a real case study related to Japanese banks. The importance of DEA-DA is confirmed by comparing it with other DA methods.     

A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)

This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.     

Handling estimated proportions in public sector data envelopment analysis

Periodically data envelopment analysis (DEA) is conducted on values that include estimated proportions, such as defect, satisfaction, mortality, or adverse event rates computed from samples. This occurs frequently in healthcare and public sector analysis where proportions frequently are estimated from partial samples. These estimates can produce statistically biased and variable estimates of DEA results, even as sample sizes become fairly large. This paper discusses several approaches to these problems, including Monte Carlo (MC), bootstrapping, chance constrained, and optimistic/pessimistic DEA methods. The performance of each method was compared using previously published data for fourteen Florida juvenile delinquency programs whose two of three inputs and one output were proportions. The impact of sample size and number of estimated rates also were investigated. In most cases, no statistically significant differences were found between the true DEA scores and the midpoints of optimistic/pessimistic, MC, and bootstrap intervals, the latter two after bias correction. True DEA results are strongly correlated with those produced by the MC (r=0.9865, p<0.001), chance constraint (r=0.9536,p<0.001), bootstrapping (r=0.9368,p<0.001), and optimistic/pessimistic (r=0.6799,p<0.001) approaches. While all methods perform fairly well, the MC approach tends to produce slightly better results and be fairly easy to implement.     

Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment

This study compares DEA (data envelopment analysis) with DEA–DA (discriminant analysis) in terms of bankruptcy assessment. Recently, many DEA researchers propose a use of DEA as a quick-and-easy tool to assess corporate bankruptcy. Meanwhile, other DEA researchers discuss a use of DEA–DA for bankruptcy-based financial analysis. The two groups are very different from the conventional use of DEA because we have long applied DEA to the measurement of operational performance, or productivity analysis. The two research groups open up a new application area (bankruptcy-based financial assessment) for DEA. This study discusses methodological strengths and weaknesses of DEA and DEA–DA from the perspective of corporate failure. The proposed comparative analysis has the three main criteria: (a) how to handle negative data in financial variables, (b) how to handle data imbalance between default and non-default firms, and (c) how to identify a failure process over time. This study finds that DEA is a managerial tool for the initial assessment of corporate failure and DEA is useful for busy corporate leaders and financial managers. In contrast, DEA–DA is useful for researchers and individuals who are interested in the detailed assessment of bankruptcy and its failure process in a time horizon.     

The measurement of returns to scale under a simultaneous occurrence of multiple solutions in a reference set and a supporting hyperplane

This research theoretically explores the measurement of RTS (Returns to Scale) under a possible occurrence of multiple solutions in DEA (Data Envelopment Analysis). In this study, the occurrence of multiple solutions is classified into Type I and Type II. Type I is an occurrence of multiple solutions in a reference set. Type II is an occurrence of multiple solutions on a supporting hyperplane passing on the reference set. Both Types I and II are very well known among DEA researchers, but previous research has not sufficiently explored a simultaneous occurrence of Type I and Type II in the RTS measurement. The two types of multiple solutions influence a degree of RTS in the DEA measurement. Such a quantitative issue on RTS is examined from the perspective of the Type I and Type II problems. To deal with such difficulties, a new linear programming approach is proposed to identify all efficient DMUs (Decision Making Units) that consist of a reference set, even if multiple solutions occur on the reference set. Based upon the research result, we can identify when multiple solutions of Type I and/or Type II occur on the RTS measurement and how to deal with such difficulties. Our research result makes it possible to measure a degree of scale economies (RTS) under the simultaneous occurrence of Type I and Type II.     

Exploring the use of DEA for formative evaluation in primary diabetes care: An application to compare English practices

Primary care is currently at the heart of the UK National Health Service policy. Primary care trusts have the crucial role of improving the efficiency and equity in primary care delivery. However, few studies have focused on performance assessment in primary care provision. In this paper, we examine the role of data envelopment analysis (DEA) in helping decision makers to understand and improve the performance of primary care practices. We discuss the results from a study of 14 practices in England in terms of their delivery of diabetes services. In order to take into account the multiple values that underlie public services provision, we have considered several different perspectives for evaluation. These were: technical, allocative and cost efficiency, clinical and patient-focused effectiveness, and equity. The approach adopted involved a deep engagement with the practices. The purpose was to understand the structures and processes supporting effective practice using DEA in a formative role rather than the more usual summative role with a large data set. This approach is in the tradition of the developmental engagement approach of operational research.     

The law of one price in data envelopment analysis: Restricting weight flexibility across firms

The Law of One Price (LoOP) states that all firms face the same prices for their inputs and outputs under market equilibrium. Taken here as a normative condition for ‘efficiency prices’, this law has powerful implications for productive efficiency analysis, which have remained unexploited thus far. This paper shows how LoOP-based weight restrictions can be incorporated in Data Envelopment Analysis (DEA). Utilizing the relation between industry-level and firm-level cost efficiency measures, we propose to apply a set of input prices that is common for all firms and that maximizes the cost efficiency of the industry. Our framework allows for firm-specific output weights and for variable returns-to-scale, and preserves the linear programming structure of the standard DEA. We apply the proposed methodology to the evaluation of the research efficiency of economics departments of Dutch Universities. This application shows that the methodology is computationally tractable for practical efficiency analysis, and that it helps in deepening the DEA analysis.     

DEA congestion and returns to scale under an occurrence of multiple optimal projections

Economic implications of congestion have been recently discussed in many DEA (data envelopment analysis) studies. In addition, several previous research efforts have explored a theoretical linkage between returns to scale (RTS) and the concept of congestion, because the two economic concepts are closely connected to each other. Tone and Sahoo [Tone, K., Sahoo, B.K., 2004. Degree of scale economies and congestion: A unified DEA approach. European Journal of Operational Research 158, 755–772] have published the theoretical linkage in this journal. All of the previous studies, including their research (2004), assume a unique optimal solution in the investigation on DEA-based congestion. When multiple solutions occur in DEA-based congestion measurement, the economic implications of congestion obtained from the previous research are all problematic from both theoretical and practical perspectives. To deal with the issue, this study explores how to deal with the occurrence of multiple solutions in the DEA-based congestion measurement. This study proposes a new approach for the congestion measurement and theoretically compares the proposed approach with Tone and Sahoo (2004).     

Are economically advanced countries more efficient in basic and applied research?

Research and development (R&D) of countries play a major role in a long-term development of the economy. We measure the R&D efficiency of all 28 member countries of the European Union in the years 2008–2014. Super-efficient data envelopment analysis (DEA) based on robustness of classification into efficient and inefficient units is adopted. We use the number of citations as output of basic research, the number of patents as output of applied research and R&D expenditures with manpower as inputs. To meet DEA assumptions and to capture R&D characteristics, we analyze a homogeneous sample of countries, adjust prices using purchasing power parity and consider time lag between inputs and outputs. We find that the efficiency of general R&D is higher for countries with higher GDP per capita. This relation also holds for specialized efficiencies of basic and applied research. However, it is much stronger for applied research suggesting its outputs are more easily distinguished and captured. Our findings are important in the evaluation of research and policy making.     

Using DEA to assess NATO burden-sharing

As the member making many of the most visible contributions to NATO, the USA has often claimed that they shoulder the heaviest ‘burden’ in maintaining the objectives of the alliance. This claim has been backed by research which has concentrated on contributions such as defence expenditure and benefits such as protection from external threats. However, modern alliances entail the exchange of multiple forms of alliance benefits and liabilities and therefore the study of ‘burden-sharing’ in these alliances is a more complicated accounting problem than has been acknowledged by previous research. In this paper, burden-sharing is studied using Data Envelopment Analysis (DEA). This is a novel application of DEA. Instead of calculating relative ‘efficiencies’, the analysis produces a ‘net-burden index’ for each member nation of NATO. The results of the analysis indicate that whilst the USA shoulders a heavy burden, some other member nations, including Canada and Spain, may in fact shoulder a heavier burden.     

The impact of information technology on organizational efficiency in public services: a DEA-based DT approach

This study attempts to examine the impact of information technology (IT) on organizational efficiency in public services. We propose a new approach to providing the directions of IT investments to improve organizational efficiency. The proposed approach is based on the integrated form of data envelopment analysis (DEA) and decision tree (DT), and composed of two steps. First, DEA is conducted to measure organizational efficiency with the selected inputs and outputs. Second, DT is built based on efficiency scores obtained from DEA and IT factors, as a target variable and predictor variables, respectively. In doing so, we can identify the relative impact of IT investments on organizational efficiency and set priorities to the IT investments to improve efficiency from a view point of each organization. A case study on the Korean local governments is presented to illustrate the usefulness of the proposed approach.     

Measurement of Returns to Scale and Damages to Scale for DEA-based operational and environmental assessment: How to manage desirable (good) and undesirable (bad) outputs?

Environmental assessment is increasingly important in preventing various types of pollutions. Data Envelopment Analysis (DEA) has been long used as an operational performance measure, but we have insufficiently explored the use of DEA for environmental assessment. This study explores a new use of DEA for the environmental assessment in which outputs are classified into desirable (good) and undesirable (bad) outputs. Such an output separation is important in the DEA-based environmental assessment. This study extends the use of DEA to the measurement of both Returns to Scale (RTS) for desirable outputs and Damages to Scale (DTS) for undesirable outputs. A Range-Adjusted Measure (RAM) is used as a DEA model for this study because the non-radial model can easily combine the two types of outputs in a unified treatment. All the mathematical features regarding the RAM-based RTS/DTS measurement are first discussed from the operational and environmental performance in a separate treatment. Then, this study combines the two performance measures as a unified measure. The RAM-based RTS/DTS is mathematically explored from the unified measure for operational and environmental performance.     

Measurement of returns to scale using a non-radial DEA model: A range-adjusted measure approach

This research theoretically explores the measurement of returns to scale (RTS), using a non-radial DEA (data envelopment analysis) model. A range-adjusted measure (RAM) is used as a representative of such non-radial models. Historically, a type of RTS has been discussed within an analytical framework of radial models. The radial-based RTS measurement is replaced by the non-radial RAM/RTS measurement in this study. When discussing the non-radial RAM/RTS measurement, this study finds a problem of multiple projections that cannot be found in the radial measurement. In this research, a new linear programming approach is proposed to identify all efficient DMUs (decision making units) on a reference set. The important feature of the proposed approach is that it can deal with a simultaneous occurrence of (a) multiple reference sets, (b) multiple supporting hyperplanes and (c) multiple projections. All of the three difficulties are handled by the proposed RAM/RTS measurement. In particular, we discuss both when the three different types of multiple solutions occur on the RAM/RTS measurement and how to deal with such difficulties. Our research results make it possible to measure not only the type of RTS but also the magnitude of RTS in the RAM measurement.     

Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments

This paper enhances cost efficiency measurement methods to account for different scenarios relating to input price information. These consist of situations where prices are known exactly at each decision making unit (DMU) and situations with incomplete price information. The main contribution of this paper consists of the development of a method for the estimation of upper and lower bounds for the cost efficiency (CE) measure in situations of price uncertainty, where only the maximal and minimal bounds of input prices can be estimated for each DMU. The bounds of the CE measure are obtained from assessments in the light of the most favourable price scenario (optimistic perspective) and the least favourable price scenario (pessimistic perspective). The assessments under price uncertainty are based on extensions to the Data Envelopment Analysis (DEA) model that incorporate weight restrictions of the form of input cone assurance regions. The applicability of the models developed is illustrated in the context of the analysis of bank branch performance. The results obtained in the case study showed that the DEA models can provide robust estimates of cost efficiency even in situations of price uncertainty.     

Within-group common weights in DEA: An analysis of power plant efficiency

In many real world applications where DEA is applied, DMUs can often be put into groups, such as those which may be under a single management team. This often means that the multipliers used within a group should be common across that group’s members. The case example examined in this regard is one involving a set of power plants, with each containing a set of power units under a common plant management. We develop a goal-programming model for this setting that seeks to derive such a common-multiplier set. The important feature of this multiplier set is that it minimizes the maximum discrepancy among the within-group scores from their ideal levels.     

Measurement of a linkage among environmental,operational, and financial performance in Japanese manufacturing firms: A use of Data Envelopment Analysis with strong complementary slackness condition

This study investigates a linkage among environmental, operational and financial performance in Japanese manufacturing industry. All manufacturing firms examined in this study are listed in Tokyo stock exchange market. We use DEA (Data Envelopment Analysis) as an evaluation methodology. This study finds that large firms have managerial capabilities to improve their operational and environmental performance. The improvement leads to the enhancement of their financial performance. However, we cannot find such a business linkage in small and medium-sized firms. They improve their operational performance and then direct themselves toward the improvement of their environmental performance. Their environmental performance is, not the first priority, the second priority for the small and medium-sized firms even though Japanese government is currently making a policy pressure on all manufacturing firms to pay attention to various environmental issues related to the global warming and climate change. The environmental protection policy is effective on only large Japanese manufacturing firms that have technological and financial capabilities for environmental protection.     

Alternative statistical regression studies of the effects of Joint and Service Specific advertising on military recruitment

Three different regression approaches use a large database developed by the Wharton Center for Applied Research (WCAR) to study the effects of Joint versus Service Specific advertising on military recruitment. (Here ‘Joint’ refers to advertising designed to serve recruitment for all four services simultaneously. Service Specific refers to advertising administered separately by each of the four services.) These regression approaches and the data and models are examined with special reference to US Army recruitment. The WCAR study led to a recommendation to replace Service Specific with Joint advertising. This recommendation was called into question by the RAND Corporation in its study that used a different regression approach. A third study that combines regressions with data envelopment analysis (DEA) is presented in this paper. This study utilizes recently developed methods based on DEA which, when incorporated in the regression, make it possible to distinguish between efficient and inefficient performances. The resulting regression yields results that show Joint advertising to be not only less efficient but also to attract potential recruits from the Army to other services. Implications for further research are set forth, which can also cast light on commercial practice by regarding Joint as a type of ‘category advertising’ and Service Specific as a type of ‘brand advertising’.