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Weights and Importance in Composite Indicators: Mind the Gap

In: Handbook of Uncertainty Quantification

Author

Listed:
  • William Becker

    (European Commission Joint Research Centre)

  • Paolo Paruolo

    (European Commission Joint Research Centre)

  • Michaela Saisana

    (European Commission Joint Research Centre)

  • Andrea Saltelli

    (University of Bergen (UIB), Centre for the Study of the Sciences and the Humanities (SVT)
    Universitat Autonoma de Barcelona (UAB), Institut de Ciencia i Tecnologia Ambientals (ICTA))

Abstract

Multidimensional measures (often termed composite indicators) are popular tools in the public discourse for assessing the performance of countries on human development, perceived corruption, innovation, competitiveness, or other complex phenomena. These measures combine a set of variables using an aggregation formula, which is often a weighted arithmetic average. The values of the weights are usually meant to reflect the variables importance in the index. This paper uses measures drawn from global sensitivity analysis, specifically the Pearson correlation ratio, to discuss to what extent the importance of each variable coincides with the intentions of the developers. Two nonparametric regression approaches are used to provide alternative estimates of the correlation ratios, which are compared with linear measures. The relative advantages of different estimation procedures are discussed. Three case studies are investigated: the Resource Governance Index, the Good Country Index, and the Financial Secrecy Index.

Suggested Citation

  • William Becker & Paolo Paruolo & Michaela Saisana & Andrea Saltelli, 2017. "Weights and Importance in Composite Indicators: Mind the Gap," Springer Books, in: Roger Ghanem & David Higdon & Houman Owhadi (ed.), Handbook of Uncertainty Quantification, chapter 34, pages 1187-1216, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-12385-1_40
    DOI: 10.1007/978-3-319-12385-1_40
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    Citations

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    Cited by:

    1. Muhammed Benli, 2025. "Global dimensions of human freedom: a two-decade analysis using fuzzy set theory and TOPSIS analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(6), pages 5681-5707, December.
    2. Denise Chavez & Katherine Nelson & Sam Zipper, 2026. "Variability of an adaptive capacity index to construction method and socio-economic context," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(2), pages 1-26, January.
    3. Ariane Carla Barbosa da Silva & Diogo Rodrigo dos Reis & Matheus Pereira Libório & Hasheem Mannan & Cristiane Neri Nobre, 2026. "A Composite Indicator of Anxiety and Depression Signs in Children and Adolescents of Low- and Middle-Income Countries: a Subjective-Objective Multidimensional Approach," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 19(2), pages 917-956, April.
    4. Cantone, Giulio Giacomo & Tomaselli, Venera, 2025. "On the coherence of composite indexes of well-being: Multiverse analysis for formative models of measurement," Socio-Economic Planning Sciences, Elsevier, vol. 102(C).
    5. Mahdieh Khezri Nezhad Gharaei & Bouali Guesmi & Jose Maria Gil Roig, 2025. "From metrics to insights: Evaluating cereal farming sustainability in Catalonia using composite index approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 71(11), pages 592-603.
    6. Daniel Feldmeyer & David C. Folch & Eric Tate, 2026. "Quantifying the Impact of Sampling Error in Composite Index Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 182(1), pages 1-20, March.

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