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Optimizing Data-driven Weights In Multidimensional Indexes

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  • Lidia Ceriani
  • Chiara Gigliarano
  • Paolo Verme

Abstract

Multidimensional indexes are ubiquitous, and popular, but present non-negligible normative choices when it comes to attributing weights to their dimensions. This paper provides a more rigorous approach to the choice of weights by defining a set of desirable properties that weighting models should meet. It shows that Bayesian Networks is the only model across statistical, econometric, and machine learning computational models that meets these properties. An example with EU-SILC data illustrates this new approach highlighting its potential for policies.

Suggested Citation

  • Lidia Ceriani & Chiara Gigliarano & Paolo Verme, 2025. "Optimizing Data-driven Weights In Multidimensional Indexes," Papers 2504.06012, arXiv.org.
    • Handle: RePEc:arx:papers:2504.06012
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    References listed on IDEAS

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    1. Asis Kumar Banerjee, 2018. "Multidimensional Indices with Data-driven Dimensional Weights: A Multidimensional Coefficient of Variation," Arthaniti: Journal of Economic Theory and Practice, , vol. 17(2), pages 140-156, December.
    2. Alkire, Sabina & Foster, James, 2011. "Counting and multidimensional poverty measurement," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 476-487, August.
    3. Walter Bossert & Satya R. Chakravarty & Conchita D’Ambrosio, 2019. "Multidimensional Poverty and Material Deprivation with Discrete Data," Themes in Economics, in: Satya R. Chakravarty (ed.), Poverty, Social Exclusion and Stochastic Dominance, pages 191-209, Springer.
    4. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    5. GUIO Anne-Catherine & FUSCO Alessio & MARLIER Eric, 2009. "A European Union Approach to Material Deprivation using EU-SILC and Eurobarometer data," IRISS Working Paper Series 2009-19, IRISS at CEPS/INSTEAD.
    6. Koen Decancq & María Ana Lugo, 2013. "Weights in Multidimensional Indices of Wellbeing: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 7-34, January.
    7. Belhadj, Besma, 2012. "New weighting scheme for the dimensions in multidimensional poverty indices," Economics Letters, Elsevier, vol. 116(3), pages 304-307.
    8. Bosmans, Kristof & Decancq, Koen & Ooghe, Erwin, 2015. "What do normative indices of multidimensional inequality really measure?," Journal of Public Economics, Elsevier, vol. 130(C), pages 94-104.
    9. Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
    10. Paolo Liberati & Giuliano Resce & Francesca Tosi, 2023. "The probability of multidimensional poverty: A new approach and an empirical application to EU‐SILC data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(3), pages 668-700, September.
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