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A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion

Author

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  • Matheus Pereira Libório

    (Graduate Program in Computer Science, Pontifical Catholic University of Minas Gerais, Belo Horizonte 30535-901, Brazil)

  • Alexandre Magno Alves Diniz

    (Graduate Program in Geography, Pontifical Catholic University of Minas Gerais, Belo Horizonte 30535-901, Brazil)

  • Hamidreza Rabiei-Dastjerd

    (School of Architecture, Planning, and Environmental Policy & CeADAR, University College Dublin (UCD), D04 V1W8 Dublin, Ireland
    Social Determinants of Health Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran)

  • Oseias da Silva Martinuci

    (Department of Geography, Maringá State University, Maringá 87020-900, Brazil)

  • Carlos Augusto Paiva da Silva Martins

    (Graduate Program in Computer Science, Pontifical Catholic University of Minas Gerais, Belo Horizonte 30535-901, Brazil)

  • Petr Iakovlevitch Ekel

    (Graduate Program in Computer Science, Pontifical Catholic University of Minas Gerais, Belo Horizonte 30535-901, Brazil)

Abstract

This research proposes a decision framework that allows for the identification of the most suitable methods to construct stable composite indicators that capture the concept of multidimensional social phenomena. This decision framework is applied to discover which method among six best represents the social exclusion of eight medium-sized Brazilian cities. The results indicate that space is important in the definition and performance of the method, and ease methods to apply present the best performance. However, one of them fails to capture the concept of the multidimensional phenomenon in two cities. The research makes six important contributions to the literature. First, it offers a decision framework for choosing the best-fit method to construct a composite social indicator. Second, it shows to what extent geographic space matters in defining the best-fit method. Third, it identifies the best-fit method regarding stability and linkage with the conceptually most significant indicator of social exclusion. Fourth, it reveals the methods to be avoided, given their poor performance. Fifth, it indicates the mathematical properties that best represent composite social phenomena. Sixth, it illuminates the debate on social exclusion from a geographical and public policy perspective.

Suggested Citation

  • Matheus Pereira Libório & Alexandre Magno Alves Diniz & Hamidreza Rabiei-Dastjerd & Oseias da Silva Martinuci & Carlos Augusto Paiva da Silva Martins & Petr Iakovlevitch Ekel, 2023. "A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6171-:d:1115187
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    References listed on IDEAS

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    1. Charles J. Kowalski, 1972. "On the Effects of Non‐Normality on the Distribution of the Sample Product‐Moment Correlation Coefficient," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(1), pages 1-12, March.
    2. Itismita Mohanty & Martin Edvardsson & Annie Abello & Deanna Eldridge, 2016. "Child Social Exclusion Risk and Child Health Outcomes in Australia," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-16, May.
    3. Marta Kuc-Czarnecka & Samuele Lo Piano & Andrea Saltelli, 2020. "Quantitative Storytelling in the Making of a Composite Indicator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 775-802, June.
    4. Carlo Drago, 2021. "The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator," Working Papers 2021.21, Fondazione Eni Enrico Mattei.
    5. Ajit Bhalla & Frédéric Lapeyre, 1997. "Social Exclusion: Towards an Analytical and Operational Framework," Development and Change, International Institute of Social Studies, vol. 28(3), pages 413-433, July.
    6. Carlo Drago, 2021. "The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator Approach," Economies, MDPI, vol. 9(4), pages 1-17, October.
    7. Jesús Peiró-Palomino & Andrés J. Picazo-Tadeo, 2018. "OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(3), pages 847-869, October.
    8. Alfredo Cartone & Domenica Panzera, 2021. "Deprivation at local level: Practical problems and policy implications for the province of Milan," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 43-61, February.
    9. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.
    10. Fusco, Elisa & Vidoli, Francesco & Sahoo, Biresh K., 2018. "Spatial heterogeneity in composite indicator: A methodological proposal," Omega, Elsevier, vol. 77(C), pages 1-14.
    11. Matteo Mazziotta & Adriano Pareto, 2019. "Use and Misuse of PCA for Measuring Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 451-476, April.
    12. Jens S. Dangschat, 2009. "Space Matters — Marginalization and Its Places," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 33(3), pages 835-840, September.
    13. Hamidreza Rabiei‐Dastjerdi & Stephen A. Matthews, 2021. "Who gets what, where, and how much? Composite index of spatial inequality for small areas in Tehran," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 191-205, February.
    14. Tim Schwanen & Donggen Wang, 2014. "Well-Being, Context, and Everyday Activities in Space and Time," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(4), pages 833-851, July.
    15. Sinéad Keogh & Stephen O’Neill & Kieran Walsh, 2021. "Composite Measures for Assessing Multidimensional Social Exclusion in Later Life: Conceptual and Methodological Challenges," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 389-410, June.
    16. Salvatore Greco & Alessio Ishizaka & Benedetto Matarazzo & Gianpiero Torrisi, 2018. "Stochastic multi-attribute acceptability analysis (SMAA): an application to the ranking of Italian regions," Regional Studies, Taylor & Francis Journals, vol. 52(4), pages 585-600, April.
    17. Matheus Pereira Libório & Lívia Maria Leite Silva & Petr Iakovlevitch Ekel & Letícia Ribeiro Figueiredo & Patrícia Bernardes, 2022. "Consensus-Based Sub-Indicator Weighting Approach: Constructing Composite Indicators Compatible with Expert Opinion," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1073-1099, December.
    18. Rui Xiao & Guofeng Wang & Meng Wang, 2018. "Transportation Disadvantage and Neighborhood Sociodemographics: A Composite Indicator Approach to Examining Social Inequalities," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 29-43, May.
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