Dimensions of well-being and their statistical measurements

Nowadays, a relevant challenge regards the assessment of a global measure of well-being by using composite indicators of different features such as level of wealth, comfort, material goods, quality and availability of education, living standard, etc. The employ of a unique measure, as a consensus of several indicators, in general allows to better understand and synthesize the underlying processes with a more accurate picture of the social progress and it is useful for giving a better information to citizens and policy makers. In this paper, we focus on statistical methodologies designed to build composite indicators of well-being by detecting latent components and assessing the statistical relationships among indicators. We will consider Principal Component Analysis (PCA) and a constrained PCA version, which allows to specify disjoint classes of variables with an associated component of maximal variance. Furthermore, we will take into account the Structural Equation Model (SEM)