Transformation of wealth produced into quality of life: analysis of the social efficiency of nation-states with the DEA's triple index approach

Although an essential condition for the occurrence of human development, economic growth is not always efficiently converted into quality of life by nation-states. Accordingly, the objective of this study is to measure the social efficiency-the ability of a nation-state to convert its produced wealth into quality of life-of a set of 101 countries. To achieve this goal, the Data Envelopment Analysis method was used in its standard, cross-multiplicative and inverted form, by means of a new approach called 'triple index'. The main results indicated that the former Soviet republics and Eastern European countries stood out in terms of social efficiency. The developed countries, notwithstanding their high social indicators, did not excel in efficiency; however, the countries of south of Africa, despite having the worst social conditions, were also the most inefficient.

Metodologia DEA e optimização multiobjectivo

Mestrado em Contabilidade e Gestão das Instituições Financeiras

A New Approach to Reducing Search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Eficiência bancária no Brasil: uma análise envoltória de dados (DEA)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Technical Efficiency in the Indian Textiles Industry: A Nonparametric Analysis of Firm-Level Data

The Indian textiles industry is now at the crossroads with the phasing out of quota regime that prevailed under the Multi-Fiber Agreement (MFA) until the end of 2004. In the face of a full integration of the textiles sector in the WTO, maintaining and enhancing productive efficiency is a precondition for competitiveness of the Indian firms in the new liberalized world market. In this paper we use data obtained from the Annual Survey of Industries for a number of years to measure the levels of technical efficiency in the Indian textiles industry at the firm level. We use both a grand frontier applicable to all firms and a group frontier specific to firms from any individual state, ownership, or organization type in order to evaluate their efficiencies. This permits us to separately identify how locational, proprietary, and organizational characteristics of a firm affect its performance.

Analysis of the Technical Efficiency of Urban Bus Services in Spain Based on SBM Models

During the first decade of the new millennium, fueled by the economic development in Spain, urban bus services were extended. Since the years 2008 and 2009, the root of the economic crisis, the improvement of these services is at risk due to economic problems. In this paper, the technical efficiency of the main urban bus companies in Spain during the 2004–2009 period are studied using SBM (slack-based measures) models and by establishing the slacks in the services' production inputs. The influence of a series of exogenous variables on the operation of the different services is also analyzed. It is concluded that only the 24% of the case studies are efficient, and some urban form variables can explain part of the inefficiency. The methodology used allows studying the inefficiency in a disaggregated way that other DEA (data envelopment analysis) models do not.

Selecting the most preferable alternatives in a group decision making problem using DEA

Group decision making is the study of identifying and selecting alternatives based on the values and preferences of the decision maker. Making a decision implies that there are several alternative choices to be considered. This paper uses the concept of Data Envelopment Analysis to introduce a new mathematical method for selecting the best alternative in a group decision making environment. The introduced model is a multi-objective function which is converted into a multi-objective linear programming model from which the optimal solution is obtained. A numerical example shows how the new model can be applied to rank the alternatives or to choose a subset of the most promising alternatives.

An Approach to Variable Aggregation in Efficiency Analysis

In the nonparametric framework of Data Envelopment Analysis the statistical properties of its estimators have been investigated and only asymptotic results are available. For DEA estimators results of practical use have been proved only for the case of one input and one output. However, in the real world problems the production process is usually well described by many variables. In this paper a machine learning approach to variable aggregation based on Canonical Correlation Analysis is presented. This approach is applied for efficiency estimation of all the farms in Terceira Island of the Azorean archipelago.

A new inverse DEA method for merging banks

This study suggests a novel application of Inverse Data Envelopment Analysis (InvDEA) in strategic decision making about mergers and acquisitions in banking. The conventional DEA assesses the efficiency of banks based on the information gathered about the quantities of inputs used to realize the observed level of outputs produced. The decision maker of a banking unit willing to merge/acquire another banking unit needs to decide about the inputs and/or outputs level if an efficiency target for the new banking unit is set. In this paper, a new InvDEA-based approach is developed to suggest the required level of the inputs and outputs for the merged bank to reach a predetermined efficiency target. This study illustrates the novelty of the proposed approach through the case of a bank considering merging with or acquiring one of its competitors to synergize and realize higher level of efficiency. A real data set of 42 banking units in Gulf Corporation Council countries is used to show the practicality of the proposed approach.

Fenntarthatósági szempontok beépítése a beszállító értékelésébe a DEA/CI összetett indikátorok módszere alkalmazásával (Integrating sustainability criteria in supplier evaluation with application of the method of of DEA/CI complex indicators)

A környezeti hatások rendszerint túlmutatnak egy vállalat határain, éppen ezért az ellátási lánc kontextusban a környezeti szempontok érvényesítése során fontos szerep jut a beszerzési döntéseknek is. Számos olyan példát lehetne említeni, amikor egy adott szempont szerint egy alternatíva környezetileg előnyös, de az ellátási lánc egészét nézve már környezetterhelő. A környezeti hatások ellátási lánc szinten való mérése azonban komoly kihívásokat jelent. Ezzel jelentős kutatásokat és fejlesztéseket inspirált a téma. Az egyik olyan terület, amelyben komoly kutatási eredmények születtek, az a környezeti szempontok beszállítói értékelésbe való beépítése. A kutatások ezen irányához csatlakozva a szerzők tanulmányunkban azt keresik, hogyan lehet meghatározni az egyik legáltalánosabban használt szállítóértékelési módszerben, a súlyozott pontrendszerben egy adott szemponthoz azt a súlyt, amely mellett az adott szempont már döntésbefolyásoló tényezővé válik. Ehhez a DEA (Data Envelopment Analysis) összetett indikátorok (Composite Indicators, CI) módszerét alkalmazzák. A szempontok közös súlyának fontossága megállapításához a lineáris programozás elméletét használják. _____ Management decisions often have an environmental effect not just within the company, but outside as well, this is why supply chain context is highlighted in literature. Measuring environmental issues of supply decisions raise a lot of problems from methodological and practical point of view. This inspires a rapidly growing literature as a lot of studies were published focusing on how to incorporate environmental issues into supplier evaluation. This paper contributes to this stream of research as it develops a method to help weight selection. In the authors’ paper the method of Data Envelope Analysis (DEA) is used to study the extension of traditional supplier selection methods with environmental factors. The selection of the weight system can control the result of the selection process.