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Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model

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  • Yongjun Shen
  • Elke Hermans
  • Tom Brijs
  • Geert Wets

Abstract

The development of a composite indicator (CI) over a set of individual indicators is worthwhile in case the methodological aggregation process is sound and the results are clear. It can then be used as a powerful tool for performance evaluation, benchmarking, and decision making. In this respect, data envelopment analysis (DEA), as a self appraisal technique, has recently received considerable attention in the construction of CIs for policy analysis and public communication. However, due to the ever increasing complexity of numerous performance evaluation problems, more and more potential indicators might be developed to represent an evaluation activity in a more comprehensive way. These indicators might also belong to different categories and further be linked to one another constituting a multilayer hierarchical structure. Simply treating all the indicators to be in the same layer as is the case in the basic DEA model thereby ignores the information on their hierarchical structure, and further leads up to weak discriminating power and unrealistic weight allocations. To overcome this limitation, a multiple layer DEA-based CI model is developed in this study to embody a hierarchical structure of indicators in the DEA framework, and both its primal and dual form are realized. The proposed model is illustrated by constructing a composite road safety performance index for a set of European countries. Copyright Springer Science+Business Media Dordrecht 2013

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  • Yongjun Shen & Elke Hermans & Tom Brijs & Geert Wets, 2013. "Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 739-756, November.
    • Handle: RePEc:spr:soinre:v:114:y:2013:i:2:p:739-756
    DOI: 10.1007/s11205-012-0171-0
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    17. Vidoli, F. & Fusco, E. & Pignataro, G. & Guccio, C., 2024. "Multi-directional Robust Benefit of the Doubt model: An application to the measurement of the quality of acute care services in OECD countries," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    18. Oliveira, Renata & Zanella, Andreia & Camanho, Ana S., 2019. "The assessment of corporate social responsibility: The construction of an industry ranking and identification of potential for improvement," European Journal of Operational Research, Elsevier, vol. 278(2), pages 498-513.
    19. Genave, Anna & Blancard, Stéphane & Garabedian, Sabine, 2020. "An assessment of energy vulnerability in Small Island Developing States," Ecological Economics, Elsevier, vol. 171(C).
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    21. Yang Ding & Yelin Fu & Kin Keung Lai & W. K. John Leung, 2018. "Using Ranked Weights and Acceptability Analysis to Construct Composite Indicators: A Case Study of Regional Sustainable Society Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(3), pages 871-885, October.
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    24. Omrani, Hashem & Fahimi, Pegah & Mahmoodi, Abdollah, 2020. "A data envelopment analysis game theory approach for constructing composite indicator: An application to find out development degree of cities in West Azarbaijan province of Iran," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).

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