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.

Evaluation of research in efficiency and productivity:a survey and analysis of the first 30 years of scholarly literature in DEA

In this paper, we study a supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the trade-off between the total cost and the environment influence. We conduct a comprehensive set of numerical experiments. The results show that our model can be applied as an effective tool in the strategic planning for green supply chain. Meanwhile, the sensitivity analysis provides some interesting managerial insights for firms.

A multi-objective optimization for green supply chain network design

Purpose – This paper aims to examine the determinants of performance of Greek banks during the period of EU financial integration (1990‐2002).Design/methodology/approach – The approach is to use an unbalanced pooled time series dataset of 23 banks.Findings – High return on average assets (ROAA) was found to be associated with well‐capitalized banks and lower cost to income ratios. Size was positive in all cases but statistically significant only when the macroeconomic and financial structure variables entered the models. Turning to macroeconomics and financial structure, the growth of gross domestic product (GDP) has a significant and positive impact on ROAA, while inflation has a significant negative impact.Originality/value – The paper's value lies in showing that money supply growth has no significant impact on profits, whereas the ratios banks' assets to GDP, stock market capitalization to banks assets and concentration are all statistical significant and negatively related to ROAA.

The determinants of banks' profits in Greece during the period of EU financial integration

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.

An Introduction to ‘Benefit of the Doubt’ Composite Indicators

In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances.

/pdf/on-the-methodological-framework-of-composite-indices-a-71skjifs0k.pdf

On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness

Data envelopment analysis with imprecise data

To consider different aspects of life when measuring human development, the United Nations Development Program introduced the Human Development Index (HDI). The HDI is a composite index of socioeconomic indicators that reflect three major dimensions of human development: longevity, knowledge and standard of living. In this paper, the assessment of the HDI is reconsidered in the light of data envelopment analysis (DEA). Instead of a simple rank of the countries, human development is benchmarked on the basis of empirical observations of best practice countries. First, on the same line as HDI, we develop a DEA-like model to assess the relative performance of the countries in human development. Then we extend our calculations with a post-DEA model to derive global estimates of a new development index by using common weights for the socioeconomic indicators. Finally, we introduce the transformation paradigm in the assessment of human development. We develop a DEA model to estimate the relative efficiency of the countries in converting income to knowledge and life opportunities.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2601927.pdf

A reassessment of the human development index via data envelopment analysis

The United Nations Development Program introduced in 1990 the Human Development Index (HDI), which ever since is published annually in the Human Development Report. In this paper, the assessment of the HDI is reconsidered in the light of data envelopment analysis (DEA). The new approach is applied to the countries of the regional aggregate of Asia and the Pacific. Human development is benchmarked on the basis of empirical observations of best practice countries. First, on the same line with HDI, we develop a DEA-like linear programming model to assess the relative performance of the countries in terms of human development. Then we extend our calculations with a post-DEA goal-programming model to derive estimates of a new development index by using optimal weights for the socioeconomic indicators. Finally, we estimate the relative efficiency of the countries in converting income to human development. We develop and solve for this purpose a pure DEA model with variable returns to scale.

Measuring human development via data envelopment analysis: the case of Asia and the Pacific

We introduce in this paper the global efficiency approach as a means to improve the discriminating power of Data Envelopment Analysis (DEA). To discriminate further among the DEA efficient units, we deal only with the units that can maintain their efficiency score under common weighting structures. Then we proceed further to ranking the whole set of DEA efficient units. We compare the global efficiency approach with the multi-criteria DEA and the cross-efficiency approaches on the basis of characteristic numerical examples drawn from the literature.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2601253.pdf

Improving the discriminating power of DEA: focus on globally efficient units

A two-stage production process assumes that the first stage transforms external inputs to a number of intermediate measures, which then are used as inputs to the second stage that produces the final outputs. The fundamental approaches to two-stage network data envelopment analysis are the multiplicative and the additive efficiency-decomposition approaches. Both they assume a series relationship between the two stages but they differ in the definition of the overall system efficiency as well as in the way they conceptualize the decomposition of the overall efficiency to the efficiencies of the individual stages. In this paper, we first show that the efficiency estimates obtained by the additive decomposition method are biased, by unduly favouring one stage against the other, while those obtained by the multiplicative method are not unique. Then, we present a novel approach to estimate unique and unbiased efficiency scores for the individual stages, which are then composed to obtain the efficiency of the overall system, by selecting the aggregation method a posteriori. Within the particularity of two-stage processes emerging from the conflicting role of the intermediate measures, we develop an envelopment model to locate the efficient frontier whose derivation from our primal multiplier efficiency assessment model is effectively justified. The results derived from our approach are compared with those obtained by the aforementioned basic methods on experimental data as well as on test data drawn from the literature. Similarities and dissimilarities in the results are rigorously justified.

Dimitris K. Despotis

Papers

Data envelopment analysis with imprecise data

A reassessment of the human development index via data envelopment analysis

Measuring human development via data envelopment analysis: the case of Asia and the Pacific

Improving the discriminating power of DEA: focus on globally efficient units

Composition versus decomposition in two-stage network DEA: a reverse approach