Optimizing data-driven weights in multidimensional indexes

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. • The paper reviews all models that can potentially provide weights for multi-dimensional indexes. • It identifies six desirable conditions that models used for weighting in multi-dimensional indexes should meet. • It finds Bayesian Networks to be the only model that satisfies the full set of desirable conditions identified.