Three essays on well-being and inequality

The work is structured in three independent chapters with own structure and corresponding to three essays. First chapter focus on the relationship between income inequality and social networking. Second chapter focus on the effects of social mobility on subjective well-being and political opinions. Last chapter analyzes the determinants of happiness and job satisfaction by focusing on the role of relative income. First chapter analyzes the effect of income inequality on social networking in Fourteen European countries by using the European Community Household Panel (ECHP) data from 1994 to 2001. The chapter is introduced by recalling the reference literature on the relationship between social capital and income inequality. Some relevant contributions come from Putnam (1993), Alesina and La Ferrara (2000), Gustavsson e Jordahl (2006). Income inequality is measured by three indexes directly derived from the micro-data of ECHP: the Gini index, three percentile ratios and the general entropy indexes. Social networks are classified in three categories corresponding to three levels of social relationships. A first level is represented by the narrow definition of social network measured by the interpersonal closed relationships among neighbours; a second level is the wide definition that refers to social contacts among friends and finally the anonymous social network given by formal relationships among members of some associations regardless the typology. The analysis follows two main ways. First, I investigate the relationship between total income inequality and the three levels of social network. Second I introduce the concept of clustered networks based on the decomposition of the general entropy index according to the education level. The idea of clustered network is based on the hypothesis that changes in inequality among people with different level of education (between component) makes individuals more prone to social networking while changes in inequality among people with the same education level (within component) halt social networking. All the estimations are based on the pooled ordered probit method, since the dependent variables are categorical multinomial. The analysis underlines heterogenous results, according to the social network level and the inequality index used. Increasing total income inequality