Educational and economic status differentials: a composition approach to social origin dimensions

The hypothesis that education leads to benefits such as enhancement of job opportunities, higher incomes, improved quality of life, and poverty reduction is widely recognised in the literature (Tilak, 2002; Solga, 2014; Bernardi & Ballarino, 2016 Giancola & Salmieri, 2023). Despite the democratisation of education systems as an effect of the reforms in Italy and the associated improvement in education levels, the impact of ascriptive factors and social origin still affects individual pathways and choices, as well as academic performance (Ballarino & Schadee, 2006; Ciarini & Giancola, 2016; Schizzerotto et al., 2018). This directly impacts the probability of educational success, which in turn affects the labour market outcomes (in terms of access and earning). Given the importance of education as a driver of personal and economic development, this paper tries to analyse the impact of social origin (in terms of socio-economic and cultural background) on educational attainment and to examine their effect on occupational outcomes. The focus is on the impact of social origin first on educational attainment and then on labour market access and income of Italians aged 25-68. These relations are explored using three waves (2016, 2018, 2020) of Italian data from the European Social Survey (ESS). The first goal is to observe the configuration in terms of the social field (in a Bourdiesian sense, Bourdieu 2018) of the elements that, combined, define social background. We propose a composition approach through Multiple Correspondence Analysis (MCA) aimed at defining the inherited social space. In the second step, we analyze the disposition in the inherited social space of the respondents' educational attainment (as projections of these into the factors extracted in the previous step). This will show the level of association between achieved education and background dimensions. A further goal is to analyse whether and to what extent the results of the different waves aggregated in the derived database change or overlap. Following this step, we move on to the analysis of the impact of social origin and educational qualifications on occupational outputs. The theoretical basis in this phase recalls the well-known "OED" model (Origin, Education, Destination) proposed by Blau & Duncan (1967) and tested in several studies (Bukodi & Goldhtorpe, 2015; Bernardi & Ballarino, 2016; Hällsten & Yaish, 2022). The difference in our approach lies because, rather than starting from pre-coded class diagrams (EGP type, Erikson et al. 1979), we use synthesised clusters as independent variables from the MCA. In this sense, we include a clustering approach that aims to create a synthetic measure of social origin and estimate the direct and indirect effects of social origins on occupational outcomes. The aim is twofold: on the one hand, we propose an analysis that, while following the tradition of inequality analyses, attempts a configurational approach to synthesise the ascriptive variables (education and occupations of the respondents' parents). On the other, we examine the reproduction mechanisms of intergenerational inequalities.