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 severa...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.展开更多
文摘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.
