Toshiyuki Sueyoshi

Evaluating Water Supply Services in Japan with RAM: a Range-adjusted Measure of Inefficiency

Abstract A range-adjusted measure (RAM) of efficiency, as recently developed in data envelopment analysis (DEA), is used to evaluate the performance of entities that supply water services in Japan. Its robustness properties are tested and pointed up for uses in improved accountability, and are further pointed up in terms of the potential for help in conducting performance and efficiency audits. The results from DEA are also joined with the Mann–Whitney rank order statistic to show how the two techniques may be jointly used in addressing issues of general policy. The Kanto Region and Kanagawa Prefecture in Japan are used for an illustration.

Data envelopment analysis for environmental assessment: Comparison between public and private ownership in petroleum industry

Environmental assessment recently becomes a major policy issue in the world. This study discusses how to apply Data Envelopment Analysis (DEA) for environmental assessment. An important feature of the DEA environmental assessment is that it needs to classify outputs into desirable (good) and undesirable (bad) outputs because private and public entities often produce not only desirable outputs but also undesirable outputs as a result of their production activities. This study proposes the three types of unification for DEA environmental assessment by using non-radial DEA models. The first unification considers both an increase and a decrease in the input vector along with a decrease in the direction vector of undesirable outputs. This type of unification measures “unified efficiency”. The second unification considers a decrease in an input vector along with a decrease in the vector of undesirable outputs. This type of unification is referred to as “natural disposability” and measures “unified efficiency under natural disposability”. The third unification considers an increase in an input vector but a decrease in the vector of undesirable outputs. This type of unification is referred to as “managerial disposability” and measures “unified efficiency under managerial disposability”. All the unifications increase the vector of desirable outputs. To document their practical implications, this study has applied the proposed approach to compare the performance of national oil firms with that of international oil firms. This study identifies two important findings on the petroleum industry. One of the two findings is that national oil companies under public ownership outperform international oil companies under private ownership in terms of unified (operational and environmental) efficiency and unified efficiency under natural disposability. However, the performance of international oil companies exhibits an increasing trend in unified efficiency. The other finding is that national oil companies need to satisfy the environmental standard of its own country while international oil companies need to satisfy the international standard that is more restricted than the national standards. As a consequence, international oil companies outperform national oil companies in terms of unified efficiency under managerial disposability.

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

This paper proposes data envelopment analysis (DEA) as a quick-and-easy tool for assessing corporate bankruptcy. DEA is a non-parametric method that measures weight estimates (not parameter estimates) of a classification function for separating default and non-default firms. Using a recent sample of large corporate failures in the United States, we examine the capability of DEA in assessing corporate bankruptcy by comparing it with logistic regression (LR). We find that DEA outperforms LR in evaluating bankruptcy out-of-sample. This feature of DEA is appealing and has practical relevance for investors. Another advantage of DEA over LR is that it does not have assumptions associated with statistical and econometric methods. Furthermore, DEA does not need a large sample size for bankruptcy evaluation, usually required by such statistical and econometric approaches. The need for such a large sample size is a significant disadvantage to practitioners when investment decisions are made using small samples. DEA can bypass such a difficulty related to a sample size. Thus, DEA is a practically appealing method for bankruptcy assessment.

Returns to scale in dynamic DEA

Two different types of inputs (variable inputs and quasi-fixed inputs) are incorporated into an analytical framework of dynamic data envelopment analysis (DEA). A unique feature of the quasi-inputs is that those are considered as outputs at the current period, while being treated as inputs at the next period. The dynamic DEA can measure interdependency among consecutive periods. This study incorporates the concept of returns to scale into the dynamic DEA.

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.

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

Abstract 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.

An occurrence of multiple projections in DEA-based measurement of technical efficiency: Theoretical comparison among DEA models from desirable properties

This study discusses nine desirable properties that a measure of technical efficiency (TE) needs to satisfy from the perspective of production economics and optimization. Seven data envelopment analysis (DEA) models are theoretically compared from a viewpoint of nine TE criteria. All the seven DEA models suffer from a problem of multiple projections even though a unique projection for efficiency comparison is one of the nine desirable properties. Furthermore, all the DEA models violate the property on aggregation of inputs and outputs. Thus, the seven DEA models do not satisfy all desirable TE properties. In addition, the comparison provides us with the following guidelines: (a) The additive model violates all desirable TE properties. (b) Russell measure and SBM (=ERGM) perform as well as RAM as a non-radial measure. If we are interested in strict monotonicity, the two models outperform the other DEA models including RAM. In contrast, if we are interested in translation invariance, RAM is better than Russell measure and SBM (=ERGM). (c) The radial measures (CCR and BCC) have the property of linear homogeneity. (d) The CCR model is useful for measuring a frontier shift among different periods. (e) If a data set contains a negative value, RAM becomes a DEA model to handle the negative value because it has the property of translation invariance. After examining the desirable TE properties, this study proposes a new approach to deal with an occurrence of multiple projections. The proposed approach includes a test to examine an occurrence of multiple projections, a mathematical expression of a projection set, and a selection process of a unique reference set as the largest one covering all the possible reference sets.

DEA-discriminant analysis in the view of goal programming

Abstract 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.

Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment

Environmental assessment recently becomes a very large-scale policy issue among corporate leaders, environmental researchers and individuals who are interested in environmental protection in the world. This study discusses how to apply Data Environment Analysis (DEA) for environmental assessment. DEA has been long utilized to measure operational performance in private and public sectors. However, previous DEA research has documented a limited use of DEA on environmental assessment. A unique feature of DEA-based environmental assessment is that it needs to classify outputs into desirable (good) and undesirable (bad) outputs because private and public entities often produce not only desirable outputs but also undesirable outputs as a result of their production activities. A methodological difficulty associated with the previous DEA-based environmental assessment is how to combine operational performance on desirable outputs and environmental performance on undesirable outputs in a unified treatment. This study proposes two types of unification for DEA-based environmental assessment within a non-radial DEA framework. Then, this study compares the two types of unification from economic and mathematical perspectives of environmental assessment.

DEA non-parametric ranking test and index measurement: slack-adjusted DEA and an application to Japanese agriculture cooperatives

This article describes a new DEA (data envelopment analysis) ranking approach that combines efficiency analysis (by DEA) with index measurement (by DEA sensitivity analysis). The sensitivity analysis, incorporated into the index measurement, omits an efficient DMU (decision making unit) to obtain the index numbers of all the DMUs. The proposed DEA ranking approach is theoretically linked to a non-parametric rank sum test. Using the rank sum test, this article statistically examines whether two groups of DMUs have different distribution functions of efficiency. As an illustrative case study, this article applies the proposed approach to Japanese Agriculture Cooperatives.