Chiara Gigliarano

In un panorama economico sempre più frammentato e complesso, come si definisce e come si misura oggi il valore? Il volume raccoglie contributi scientifici e riflessioni maturate dal dialogo tra il mondo accademico, l'istat e le istituzioni, mettendo in luce la duplice dimensione della crescita: quella globale, legata alle grandi dinamiche dei mercati, e quella locale, profondamente radicata nei territori. Ne emerge una lettura integrata della ricchezza delle nazioni come fenomeno complesso e multidimensionale. La ricchezza non si esaurisce nei flussi economici correnti, ma prende forma nella capacità di governare risorse strategiche, costruire reti produttive resilienti, accumulare patrimonio nel tempo e adattarsi a un contesto globale in continuo mutamento. Il volume rafforza così la consapevolezza che ricchezza, competitività e resilienza delle economie dipendono da un intreccio sempre più stretto tra risorse materiali, strutture produttive, relazioni internazionali, capacità di accumulazione e qualità delle reti d'impresa.

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.

Come vivono oggi i giovani in Italia e in Europa? Quali paesi offrono loro le migliori opportunità; di lavoro, crescita e realizzazione personale? Il volume presenta i risultati di una ricerca condotta dall'Ufficio Studi LIUC insieme a FSI sul benessere socio-economico delle nuove generazioni, analizzando fattori come la qualità; dell'occupazione, l'adeguatezza delle retribuzioni rispetto al costo della vita e delle abitazioni, la possibilità; di fare impresa e la soddisfazione per il lavoro e per la vita in generale. Attraverso un insieme di indicatori originali, lo studio propone due strumenti innovativi di lettura della condizione giovanile: l'indice di maturità, che misura a quale età; si diventa di fatto adulti, e l'indice YES (Youth Enhancement Score), che confronta paesi europei e regioni italiane in termini di capacità; di attrarre e valorizzare i giovani. Un'analisi che offre dati, spunti e prospettive per comprendere meglio il rapporto tra le nuove generazioni e il loro futuro.

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 definition and construction of a composite indicator (CI) is a challenging operation. Here we propose a new technique to construct composite indicators that is based on the Box-Cox transformation. To illustrate our idea, a simulation study has been carried out. Results show that it is possible to find a parameter from the Box-Cox transformation that, in the least squares sense, ensures normality and, as a consequence, it opens new roads for researches in the field of the construction of CIs.

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.

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