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Optimizing data-driven weights in multidimensional indexes

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  • Ceriani, Lidia
  • Gigliarano, Chiara
  • Verme, Paolo

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

  • Ceriani, Lidia & Gigliarano, Chiara & Verme, Paolo, 2025. "Optimizing data-driven weights in multidimensional indexes," Economics Letters, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:ecolet:v:255:y:2025:i:c:s0165176525003362
    DOI: 10.1016/j.econlet.2025.112499
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    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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