Using data from the last European Survey on Income and Living Conditions (EU-SILC), this paper focuses on the measurement of well-being and on its association with education. EU-SILC survey gives information on several aspects of people’s daily life (i.e. housing, labour, health, education, finance, material deprivation and possession of durables) allowing a multi-dimensional approach to the study of well-being, poverty and social exclusion. For our aims we have considered only survey data collected in Italy. Due to the multidimensionality of well-being concept, we have selected some variables related principally to four main dimensions of well-being, which are financial endowment, housing conditions and goods possessions, health status, and environment. A first explanatory analysis via multivariate regression model has highlighted the effect of education on the factors considered. Finally, a latent class regression analysis has been used to cluster individuals into mutually exclusive latent classes which identify different intensities of well-being (the latent trait) taking into account the effect of education in the membership probability of each latent class.
KeywordsWell-BeingEducationLatent Class Regression AnalysisIRTMultivariate Regression
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