Chiara Gigliarano

Composite indicators are reliable tools that have recently gained popularity because of their effectiveness in solving problems of multidimensional measurement. Along with the notable increase in the number of applications, finding optimal weights for input features during aggregation is a topic that creates many controversies but very few radical solutions. This paper presents a novel statistical method designed to assist developers in attaining a plausible weighting scheme for composite indices. The solution obtained is referred to as sensitivity-based weights, where the magnitude of each weight aligns with the proportion of output variance contributed by the corresponding input. Within the context of our theoretical framework, these weights can be identified based on the multivariate distribution of input features. In case the population distribution is unknown, we introduce an optimization procedure for estimating sensitivity-based weights from a finite sample of inputs. Two supporting algorithms are proposed to facilitate the weighting process, including regression-based estimation and coarse estimation. These algorithms are tested in two numerical simulation cases, where the results show that the paramount factor affecting the accuracy and robustness of estimates lies in the sample size, and the coarsening technique exhibits superiority in performance when dealing with inputs from various distributions. Finally, a composite index for identifying fragile municipalities in Italy has been examined and reconstructed to demonstrate the method’s applicability in practice.

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 study investigates the relationship between quality of government and environmental wellbeing in European regions at the NUTS-2 level. First, we find that subnational environmental data are spatially interdependent. Then, we construct a set of composite indicators of environmental wellbeing via Bayesian spatial factor analysis. Finally, by using these composite indicators in spatial regression analysis, we show that institutional quality is a key determinant of environmental wellbeing. We also find that the institutions-environment nexus varies across dimensions of environmental wellbeing – institutions matter especially for the quality of air and soil. Policymakers should be aware that environmental degradation can be tackled by building more effective and well-functioning regional public institutions

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.

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.

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.

This paper examines the association between pro-environmental consumption and subjective well-being and tests whether this type of decision could be explained as a utility-maximizing choice under welfare economics, or it is subject to systematic deviations from rational choice. The literature provides evidence for positive and significant effects of pro-environmental consumption on subjective well-being; however, studies describing this relationship and drawing comparisons based on specific dimensions are limited. Here we try to fill the gap by proposing two composite indicators, representing preferences for pro-environmental behaviour in different dimensions. The indicator "proactive behaviour" encompasses the consumption of products with better environmental efficiency, while the indicator "avoidance behaviour" pertains to sustainable choices that involve avoiding – or less frequently engaging in – consumption decisions with negative ecological externalities. The findings, based on data from the Aspects of Daily Life survey conducted in Italy by ISTAT, provide that the former has a stronger effect on subjective well-being compared to the latter. Furthermore, the analysis reveals that environmental satisfaction negatively moderates the relationship between sustainable consumption and life satisfaction.

This paper proposes spatial comprehensive composite indicators to evaluate the well-being levels and ranking of Italian provinces with data from the Equitable and Sustainable Well-Being dashboard. We use a method based on Bayesian latent factor models, which allow us to include spatial dependence across Italian provinces, quantify uncertainty in the resulting estimates, and estimate data-driven weights for elementary indicators. The results reveal that our data-driven approach changes the resulting composite indicator rankings compared to those produced by traditional composite indicators' approaches. Estimated social and economic well-being is unequally distributed among southern and northern Italian provinces. In contrast, the environmental dimension appears less spatially clustered, and its composite indicators also reach above-average levels in the southern provinces. The time series of well-being composite indicators of Italian macro-areas shows clustering and macro-areas discrimination on larger territorial units.

One prominent change induced by the COVID-19 pandemic concerns the worldwide use of public transportation for commuting purposes. This study focused on university commuting in Italy by examining the propensity to change transport modes under different infection risk scenarios. Data were collected in 2020 through an online survey of college mobility conducted by the Italian University Network for Sustainable Development. Asking the respondents to consider both a pessimistic and an optimistic scenario, with respect to the risk odds of being infected, we followed a two-step approach to study the prospective travel habits of college users. First, we tested a logit model to estimate the propensity to abandon one's pre-COVID-19 commuting mode. Then, we investigated the factors influencing the choice of switching from public transportation to either cars or active modes by estimating a multinomial logit model. By exploiting the novelty of considering two risk scenarios, this study highlighted that, especially in the pessimistic case, the change to active modes was constrained by spatial aspects in favour of motorized vehicles. From a policy perspective, this COVID-19-based natural experiment advocates transportation authorities taking effective actions to ensure that, in case of emergencies, a modal shift would not benefit more-polluting transport means.