Chiara Gigliarano

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.

This paper aims to examine the impact of the Covid-19 pandemic on multidimensional poverty in Italy and its provinces by comparing household poverty levels before and after the outbreak. To capture the multidimensionality of poverty, we analyze various dimensions, including economic well-being, health status, education, neighborhood quality, and subjective well-being. The empirical analysis relies on micro-data from Istat’s aspects of daily life (AVQ) survey, covering the years 2018–2021. As the survey’s direct estimates are reliable only at the regional level (NUTS 2), we apply small area estimation techniques to produce accurate estimates of provincial (NUTS 3) deprivation incidences. Subsequently, we aggregate the deprivation headcounts across the elementary indicators using penalized power mean composite indicators. The empirical findings indicate that overall multidimensional poverty worsened in most of the Italian provinces, particularly during the second year of the pandemic, with higher levels persisting in southern areas. The various dimensions of poverty exhibited different trends, with education, subjective well-being, and health emerging as the most negatively affected in numerous provinces.

An implementation of Gini-based weighting approaches in constructing composite indicators, providing functionalities for normalization, aggregation, and ranking comparison.

The aim of this paper is to measure to which extent income distribution is polarized across European countries by means of polarization measures based on the Bonferroni and De Vergottini indices of inequality. Different from traditional measures of polarization, the indices proposed in this paper are sensitive to progressive transfers, attaching more importance to some part of the income distribution. These indices enriches the analysis and contribute to disentangle the different faces of income polarization. In the empirical application we compare European countries over the period 2010–2019 using EU-SILC data. Results reveal significant changes in polarization over the last decades for most countries.

Composite indicators are widely recognized as effective tools for representing complex assessments in the form of a one-dimensional measure. The proliferation of related theoretical frameworks and methodologies has been accompanied by a growing debate around the determination of optimal weights in developing composite indicators. This paper introduces two weighting procedures aimed at assisting developers in attaining the most plausible solution, which closes the disparity between the importance of input features and their corresponding weights. The first technique involves utilizing variance-based sensitivity analysis and calibrating the weights in accordance with the contribution of each input to the output uncertainty. Alternatively, the second approach employs a combination of cluster analysis and predictive modeling to evaluate the relative capability of individual features in differentiating observations within the multidimensional context, thereby informing a proper weight assignment. To demonstrate the practical application of these weighting procedures, a composite indicator has been developed to assess the level of well-being in large European regions during the ten-year period from 2010 to 2019. Despite differences in the weighting schemes used to calculate the final index values, the empirical results indicate a general consensus regarding the allocation of welfare across the territories.