Chiara Gigliarano

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 study investigates the determinants of water conservation behaviour through a comprehensive empirical analysis and explores spatial heterogeneity in their coefficients at a regional unit scale, with a specific focus on Italy. The results of the ordinal logistic regression indicate that various socio-demographic, infrastructural, behavioural, and social factors, including trust in public institutions, trust in people, and charitable donations, have a significant impact on water conservation. Furthermore, the study reveals statistically significant spatial variations in the relationship between water conservation and its five factors, including education, energy saving, and trust in public institutions, using geographically weighted regression.

We analyse the effect of COVID-19 on multidimensional poverty in three Italian Regions, namely Trentino Alto Adige, Marche and Sicily by measuring changes in households poverty in 2019 and 2020. Multidimensional poverty is de- fined in terms of deprivation in five dimensions: economic, health, education, neigh- bourhood quality, subjective well-being. The index at regional level is computed ac- cording to the Alkire and Foster methodology. The analysis is based on data from the ‘Aspects of daily life’ survey provided by Istat.

The Index of Sustainable Economic Welfare (ISEW) proposed by Daly and Cobb in 1989 provides a more down-to-earth representation of the well-being of a society than GDP does, since its definition involves also variables that are not included in the conventional national accounts (such as social and environmental issues). Recently there has been an increasing interest in calculating this index also at local levels. Following this strand of literature, aim of this paper is to provide a comparison between ISEW and GDP trends for two central Italian regions, Tuscany and Marche, over the years 1999-2009. In particular, we try to explain how differences in the level of GDP are reflected in differences in ISEW levels between these regions. Finally, we discuss and propose further adjustments in calculation of the ISEW.