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Multi-level DEA for the construction of multi-dimensional indices

Data Envelopment Analysis (DEA) is a non-parametric, mathematical programming method that is used to evaluate the performance of Decision Making Units. One variation of the method is focused on expanding the number of stages: inputs are transformed into intermediate measures and in turn those are tr...

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Autores principales: Tsaples, Georgios, Papathanasiou, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726711/
https://www.ncbi.nlm.nih.gov/pubmed/33318961
http://dx.doi.org/10.1016/j.mex.2020.101169
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author Tsaples, Georgios
Papathanasiou, Jason
author_facet Tsaples, Georgios
Papathanasiou, Jason
author_sort Tsaples, Georgios
collection PubMed
description Data Envelopment Analysis (DEA) is a non-parametric, mathematical programming method that is used to evaluate the performance of Decision Making Units. One variation of the method is focused on expanding the number of stages: inputs are transformed into intermediate measures and in turn those are transformed into outputs. DEA and its variations have been used to construct composite indicators. The purpose of the current paper is to propose a new variation of DEA that relies on a two-stage model for the construction of multi-dimensional indices. The proposed variation: • Uses a two-stage DEA model for the calculation of each sub-indicator that will be integrated into the final index; • All the sub-indicators are integrated into the final index with the use of a Benefit-of-the-Doubt mathematical programming model. As it was mentioned, the proposed method can be used for the construction of multi-dimensional indicators and in the current paper is used to calculate the sustainability of the EU-28 countries.
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spelling pubmed-77267112020-12-13 Multi-level DEA for the construction of multi-dimensional indices Tsaples, Georgios Papathanasiou, Jason MethodsX Method Article Data Envelopment Analysis (DEA) is a non-parametric, mathematical programming method that is used to evaluate the performance of Decision Making Units. One variation of the method is focused on expanding the number of stages: inputs are transformed into intermediate measures and in turn those are transformed into outputs. DEA and its variations have been used to construct composite indicators. The purpose of the current paper is to propose a new variation of DEA that relies on a two-stage model for the construction of multi-dimensional indices. The proposed variation: • Uses a two-stage DEA model for the calculation of each sub-indicator that will be integrated into the final index; • All the sub-indicators are integrated into the final index with the use of a Benefit-of-the-Doubt mathematical programming model. As it was mentioned, the proposed method can be used for the construction of multi-dimensional indicators and in the current paper is used to calculate the sustainability of the EU-28 countries. Elsevier 2020-12-03 /pmc/articles/PMC7726711/ /pubmed/33318961 http://dx.doi.org/10.1016/j.mex.2020.101169 Text en © 2020 The Author(s). Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Method Article
Tsaples, Georgios
Papathanasiou, Jason
Multi-level DEA for the construction of multi-dimensional indices
title Multi-level DEA for the construction of multi-dimensional indices
title_full Multi-level DEA for the construction of multi-dimensional indices
title_fullStr Multi-level DEA for the construction of multi-dimensional indices
title_full_unstemmed Multi-level DEA for the construction of multi-dimensional indices
title_short Multi-level DEA for the construction of multi-dimensional indices
title_sort multi-level dea for the construction of multi-dimensional indices
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726711/
https://www.ncbi.nlm.nih.gov/pubmed/33318961
http://dx.doi.org/10.1016/j.mex.2020.101169
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