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Optimizing Data-driven Weights In Multidimensional Indexes

Lidia Ceriani, Chiara Gigliarano, Paolo Verme

arXiv 8 Apr 2025 · Econometrics · publishedEconomics Letters (2025)

arXiv:2504.06012 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

24
references
28
in-text mentions
24
distinct cited
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self-citations
3,601
main-text words

appendix boundary found by appendix_titled_section at “Appendix 1” · 81% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bossert, W., S. R. Chakravarty, and C. D'Ambrosio (2013) Multidimensional poverty and material deprivation with discrete data0.64422100%
2Decancq, K. and M. A. Lugo (2013) Weights in multidimensional indices of wellbeing: An overview0.64422100%
3Cugnata, F., R. S. Kenett, and S. Salini (2016) Bayesian networks in survey data: Robustness and sensitivity issues0.51121100%
4OECD (2015) How's Life? 2015: Measuring Well-being0.51121100%
5Aaberge, R. and A. Brandolini (2015) Multidimensional poverty and inequality0.40511100%
6Albrecht, D., A. Nicholson, and C. Whittle (2014) Structural sensitivity for the knowledge engineering of bayesian networks0.40511100%
7Alkire, S. and J. E. Foster (2011) Counting and multidimensional poverty measurement0.40511100%
8Belhadj, B (2012) New weighting scheme for the dimensions in multidimensional poverty indices0.40511100%
9CNEL and ISTAT (2015) Report on Equitable and Sustainable Wellbeing (BES 2014)0.40511100%
10Banerjee, A. K (2018) Multidimensional indices with data-driven dimensional weights: A multidimensional coefficient of variation0.40511100%

Showing the top 10 of 24 scored citations.