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Optimizing Data-driven Weights In Multidimensional Indexes

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Listed:
  • 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

    as
    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. 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.
    3. 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.
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    5. 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.
    6. 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.
    7. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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