DEA and the discriminant analysis of ratios for ranking units

Sinuany‐Stern, Zilla; Friedman, Lea

doi:10.1016/s0377-2217(97)00313-5

Cited by 137 publications

(42 citation statements)

References 26 publications

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“…A promising approach to alleviate the weak discrimination capability of the basic DEA models is based on the calculation of the DMU cross-efficiency score [86]. While in the traditional DEA models the measurement of a DMU efficiency is based only on self evaluation, by assigning the most favorable set of weights for outputs and inputs to maximize its efficiency (that is to say, from an optimistic perspective), in the DEA cross-efficiency approach a peer evaluation together with a pure self evaluation of DMUs are performed [87]. Ranking procedures based on the calculation of cross-efficiency scores have a number of advantages [88,89].…”

Section: Step 2: the Calculation Of The Cross-efficiency Scoresmentioning

confidence: 99%

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

Storto

2016

Sustainability

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Abstract:In this paper, a method is proposed to calculate a comprehensive index that calculates the ecological efficiency of a city by combining together the measurements provided by some Data Envelopment Analysis (DEA) cross-efficiency models using the Shannon's entropy index. The DEA models include non-discretionary uncontrollable inputs, desirable and undesirable outputs. The method is implemented to compute the ecological efficiency of a sample of 116 Italian provincial capital cities in 2011 as a case study. Results emerging from the case study show that the proposed index has a good discrimination power and performs better than the ranking provided by the Sole24Ore, which is generally used in Italy to conduct benchmarking studies. While the sustainability index proposed by the Sole24Ore utilizes a set of subjective weights to aggregate individual indicators, the adoption of the DEA based method limits the subjectivity to the selection of the models. The ecological efficiency measurements generated by the implementation of the method for the Italian cities indicate that they perform very differently, and generally largest cities in terms of population size achieve a higher efficiency score.Keywords: cross-efficiency; data envelopment analysis; Shannon's entropy; ecological efficiency; cities; ranking; Italy BackgroundIn the last decade, cities have gained a greater centrality in the economic and social growth of nations. The recent report delivered by the Brookings Institution indicates that in 2014 the economies of the 300 largest metropolitan areas accounted for 47% of global gross domestic product (GDP) and 38% of GDP growth [1]. According to recent estimates provided by Seto et al.[2], more than 80% of the national gross domestic product (GDP) is generated in urban areas. A recent worldwide research conducted by the McKinsey Global Institute on a sample of 600 cities estimates that between 2010 and 2050, the GDP of these cities is expected to double, while 23 megacities-cities having more than 10 million inhabitants-in 2007 generated 14% of global GDP [3].People move to and live in cities to have access to better jobs, education, health care, goods and services. More than half of the human population over the world is living in cities and towns, and, in the next decades, the number of people expected to live in cities will grow to 75%, while population growth over the next 25 years will be concentrated in cities and towns [4,5]. The most urbanized areas are located in the American and Europe continents, respectively having about 80% and 70% of all inhabitants residing in cities and towns. However, while cities are the primary source of economic development and social prosperity, and house more than half of the world population, they are large users of resources, responsible for about 2/3 of energy demand and greenhouse gas emissions [5]. The recent rapid and intense urbanization has often resulted in an over consumption of water, energy, raw

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Section: Step 2: the Calculation Of The Cross-efficiency Scoresmentioning

confidence: 99%

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

Storto

2016

Sustainability

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“…The idea of common weights in "DEA" was first introduced for considering DEA for measuring maintenance units of highways and it was presented as mental preferences in DEA framework (Sinuany-Stern & Friedman, 1998).…”

Section: Common Weights and Deamentioning

confidence: 99%

“…They performed canonic correlation analysis for receiving a weight vector, for all inputs and outputs. On the other hand, they introduced non-linear discriminate analysis, measuring common weights for all related units (Sinuany-Stern & Friedman, 1998). In addition, some other people used common weights for measuring the exact efficiency of ranked units based on DEA (Wang et al, 2006).…”

Section: Common Weights and Deamentioning

confidence: 99%

Using CSW weight’s in UTASTAR method

Makui

Momeni²

2012

10.5267/j.dsl

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Several researchers have considered similarities between Multi-Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA), as tools for solving decision making problems. As the preferences of decision-maker (DM) on alternatives are not considered in classical DEA, some researchers have tried to consider it in DEA. The UTA-STAR method is one of the techniques widely used in Multi Criteria Decision Analysis. In this technique, the preferences of decision maker on alternatives are considered and UTA-STAR tries to compute the most suitable weights for criteria and alternatives to obtain a utility function having a minimum deviation from the preferences. The goal of this paper is interpreting decision maker's preferences in UTA-STAR method, in a new manner, using the common set of weights (CSW) in DEA.

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“…Andersen and Petersen [5] modify the CCR model to allow for a ranking of the efficient units. A procedure using DEA to rank DMUs of interests is proposed by combining multivariate statistic approaches such as discriminant analysis of ratios [6] and canonical correlation analysis [7]. Wang and Luo [8] proposed a ranking approach by incorporating DEA into the technique for order preference by similarity to ideal solution (TOPSIS, 1981).…”

Section: Introductionmentioning

confidence: 99%

A note on ranking efficient DMUs based on a single virtual inefficient DMU in DEA

Balf¹

2017

Data Envelopment Analysis and Decision Science

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In this paper we give a comment on paper by Shetty and Pakkala [U. Shetty, T.P.M. Pakkala, (2010), Ranking efficient DMUs based on single virtual inefficient DMU in DEA. OPSEARCH, 47 (1):50-72]. They proposed an approach to rank the efficient decision making units based on a virtual DMU in the constant return to scale DEA model. The input and output levels of this virtual DMU are the average of input and outputs of all DMUs. We obtain another method for select virtual DMU as negative ideal DMU. Our approach doesn't need to existence at least an inefficient DMU in the set DMUs. In addition, it can be used in constant and variable return to scale DEA models. This brief comment provides an alternative approach for their work.

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DEA and the discriminant analysis of ratios for ranking units

Cited by 137 publications

References 26 publications

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

Using CSW weight’s in UTASTAR method

A note on ranking efficient DMUs based on a single virtual inefficient DMU in DEA

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