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Composite Indicators Construction by Data Envelopment Analysis: Methodological Background

Composite Indicators Construction by Data Envelopment Analysis: Methodological Background

Gordana Savić, Milan Martić
Copyright: © 2017 |Pages: 29
ISBN13: 9781522507147|ISBN10: 1522507140|EISBN13: 9781522507154
DOI: 10.4018/978-1-5225-0714-7.ch005
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MLA

Savić, Gordana, and Milan Martić. "Composite Indicators Construction by Data Envelopment Analysis: Methodological Background." Emerging Trends in the Development and Application of Composite Indicators, edited by Veljko Jeremic, et al., IGI Global, 2017, pp. 98-126. https://doi.org/10.4018/978-1-5225-0714-7.ch005

APA

Savić, G. & Martić, M. (2017). Composite Indicators Construction by Data Envelopment Analysis: Methodological Background. In V. Jeremic, Z. Radojicic, & M. Dobrota (Eds.), Emerging Trends in the Development and Application of Composite Indicators (pp. 98-126). IGI Global. https://doi.org/10.4018/978-1-5225-0714-7.ch005

Chicago

Savić, Gordana, and Milan Martić. "Composite Indicators Construction by Data Envelopment Analysis: Methodological Background." In Emerging Trends in the Development and Application of Composite Indicators, edited by Veljko Jeremic, Zoran Radojicic, and Marina Dobrota, 98-126. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0714-7.ch005

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Abstract

Composite indicators (CIs) are seen as an aggregation of a set of sub-indicators for measuring multi-dimensional concepts that cannot be captured by a single indicator (OECD, 2008). The indicators of development in different areas are also constructed by aggregating several sub-indicators. Consequently, the construction of CIs includes weighting and aggregation of individual performance indicators. These steps in CI construction are challenging issues as the final results are significantly affected by the method used in aggregation. The main question is whether and how to weigh individual performance indicators. Verifiable information regarding the true weights is typically unavailable. In practice, subjective expert opinions are usually used to derive weights, which can lead to disagreements (Hatefi & Torabi, 2010). The disagreement can appear when the experts from different areas are included in a poll since they can value criteria differently in accordance with their expertise. Therefore, a proper methodology of the derivation of weights and construction of composite indicators should be employed. From the operations research standpoint, the data envelopment analysis (DEA) and the multiple criteria decision analysis (MCDA) are proper methods for the construction of composite indicators (Zhou & Ang, 2009; Zhou, Ang, & Zhou, 2010). All methods combine the sub-indicators according to their weights, except that the MCDA methods usually require a priori determination of weights, while the DEA determines the weights a posteriori, as a result of model solving. This chapter addresses the DEA as a non-parametric technique, introduced by Charnes, Cooper, and Rhodes (1978), for efficiency measurement of different non-profitable and profitable units. It is lately adopted as an appropriate method for the CI construction due to its several features (Shen, Ruan, Hermans, Brijs, Wets, & Vanhoof, 2011). Firstly, individual performance indicators are combined without a priori determination of weights, and secondly, each unit under observation is assessed taking into consideration the performance of all other units, which is known as the ‘benefit of the doubt' (BOD) approach (Cherchye, Moesen, Rogge, & van Puyenbroeck, 2007). The methodological and theoretical aspects and the flaws of the DEA application for the construction of CIs will be discussed in this chapter, starting with the issues related to the application procedure, followed by the issues of real data availability, introducing value judgments, qualitative data, and non-desirable performance indicators. The procedure of a DEA-based CI construction will be illustrated by the case of ranking of different regions of Serbia based on their socio-economic development.

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