The results of the OECD PISA (Programme for International Student Assessment) international survey have highlighted, since its first edition in 2000, that boys outperform girls in mathematics but lag behind in reading, with some variability across countries (Di Castro, Ferri 2022). In particular, it is evident that Poland exhibits a near-zero gender gap in mathematics, whereas in Italy, the gap is very high.

In this contribution, we employ the OAXACA-BLINDER and OAXACA RIF methods to examine how scores differ across the distribution, identifying the variables most responsible for the gap. The international comparison is thus valuable in highlighting the reasons and potential policy solutions to address and reduce this gap in Italy.

Children of immigrants are becoming an increasingly relevant component of European societies, and their study is gaining momentum and broadening its range and scope (Heath et al., 2008). Anyway, their integration is still problematic along a number of different dimensions (Fajth and Lessard-Phillips, 2023).

Their socio-economic inclusion in the host countries is a marker of the ethnic-based mechanisms of social inequality transmission (Zhou and Bankston, 2016). Anyway, little is known about these mechanisms in countries that only recently arose as migrant destinations, like the Southern European ones (Gabrielli and Impicciatore, 2022).

This represents an important gap in the literature, since Southern European countries present peculiar characteristics in terms of patterns of socio-economic assimilation of first-generation migrants, and these peculiarities may also affect their children’s assimilation trajectories.

This work aims to fill this gap and provide, for the first time, a comprehensive overview of the migrants' educational and labour market achievement in 11 Western European countries, accounting for the different migrant generations.

To this purpose, multivariate statistical techniques are applied to the individual data of two Eurostat “ad hoc modules” of the 2008 and 2014 EU Labour Force Survey. These modules contain information on the country of birth of the respondent’s father and mother, and on the respondent’s time of arrival in the host country: hence, these dato allow to precisely define the migrant generations.

The sample consists of prime working-age men and women aged 15-29: such age selection is made to account for the fact that the careers of the children of immigrants are on average shorter than those of natives and first-generation migrants. Individuals in the sample are distinguished on the basis of their migration status, as follows: a) natives: those born in Italy with both Italian parents; b) first-generation migrants from high emigration countries (Hec): those born in a less socio-economically developed country and arrived in Italy when older than 12 years; c) generation 1.5: those born abroad but migrated in Italy when between 7 and 12 years of age; d) second-generation: those born in Italy with at least one foreign parent and those born abroad but migrated in Italy when younger than 7 years.

More in particular, starting from the theoretical model of the OED triangle (Blau and Duncan, 1967), it will be singled out, net of education and other socio-demographics, the direct effect of the workers’ migration generation on labour market attainment.

Preliminary results underline that the children of immigrants experience different patterns of socio-economic assimilation depending on the receiving context. These differences may be interpreted in terms of different migration histories, welfare systems and individual behavioral strategies.

Higher Education Institutions (HIE) can boost local entrepreneurship and development by increasing the supply of human capital and by improving regional innovation networks through their direct engagement in the territory (Benneworth and Fitjal, 2019). The increase in the supply of human capital caused by the reduction of the cost of tertiary education for low-income households is an essential channel through which universities can contribute to the reduction of regional inequalities (Evers, 2019). In this paper, we investigate the role of HIE in providing the human capital needed to foster the creation of innovative startups in Italian provinces. To this aim, we build a novel province-level (NUTS3) dataset by merging data from different sources. We use data from the Ministry of Made in Italy on newly established innovative firms and data on local graduates provided by the Ministry of University. We control for the local engagement of universities by using Ministry of Made in Italy data on incubators and spinoffs as well as data on technological transfer of HEIs provided by Netval (Network for Research Valorization). Furthermore, we control for several province characteristics (value added, population, unemployment, economy structure, R&D expenditure, and skill mismatch) by using regional data from Eurostat and the Italian Labour Force Survey. The final dataset is a panel of 107 provinces observed from 2014 to 2022.

From the methodological point of view, we use instrumental variables to control for the endogeneity of the supply of graduates and spatial econometric models to estimate local and global spillover effects across provinces.Preliminary results indicate that the supply of graduates from local universities increases the number of innovative startups per inhabitant. This result holds for startups engaged in R&D activities and those with a high share of graduates. No significant impact is found on startups holding a patent or a trademark. The effect on R&D-performing startups is concentrated in the center-south provinces, while the effect on startups employing graduates is concentrated in northern provinces. These results confirm the positive role of the supply of human capital in reducing inequalities. However, this effect is concentrated in large provinces, thus not including the country's least developed areas. As a main policy indication, place-based higher education and industrial policy measures should be designed to enhance the contribution of HEIs to local development.

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.

This study aims to examine in detail the Educational Centre Projects of a European (ECPs) metropolis, specifically Barcelona, in the framework of critical analyses investigating the presence and influence of neoliberal educational discourse. Through a methodology that combines quantitative text mining with qualitative discourse analysis, we aim to identify the traces of an ideology that permeates contemporary educational policies.

Our approach is informed by the reflections of scholars in the field of educational transformation (e.g., Dardot & Laval, 2019; Apple, 2005), who identify the market as a dominant paradigm shaping global educational policies. Other authors (Brödner, 2007; Bonilla-Molina, 2020; Gibbon & Henriksen, 2012; Ciccarelli, 2018; McGregor, 2009; Alves et al., 2021; Dahlstedt & Hertzberg, 2012) have focused on specific aspects of neoliberal educational policies, such as competence-based teaching, the extension of quality policies to schools, and the introduction of entrepreneurship education into school curricula.

Our research adopts a mixed-methods approach, combining quantitative text mining (Bolasco, 2021) with qualitative discourse analysis (Fitzgerald, 2022). We use the open-source platform Voyant (Sinclair & Rockwell, 2016) to perform text mining on the corpus consisting of ECPs of secondary education centres in Barcelona. Next, we perform discourse analysis using Voyant's Contexts tool. Before proceeding with the analysis, we preprocessed the data using a stopword list provided by Pompeu Fabra University and Ranks NL.

The analysed corpus comprises a total of 594,967 tokens and 22,150 unique word forms. Through text analysis, we identify several keywords relevant to neoliberal educational discourse, such as 'treball/laboral', 'competència', 'qualitat' and 'empresa'. The TF-IDF analysis reveals a homogeneous frequency distribution for the lemmas ‘treball’ and ‘competència’ within the corpus, while ‘qualitat’ and ‘empresa’ show higher frequencies in the documents of vocational training centres.

The discourse analysis shows a significant combination of terms evoking the language of private enterprise, such as ‘treball’, ‘qualitat’ and ‘empresa’, alongside words apparently belonging to the pedagogical discourse, such as ‘competència’. This use of entrepreneurial terminology in a formal educational context suggests an attempt to naturalise entrepreneurial language in educational practices. The implicit acceptance of the link between the world of production and educational institutions thus takes on, as Foucault (2014) suggests, a sense of truth.

In conclusion, our study provides a critical analysis of the Barcelona ECPs, highlighting the traces of neoliberal educational discourse within the school context. Such an investigation is useful to assess the depth of transformations in contemporary education. In this case, we examine documents produced by the teaching staff of individual educational centres, rather than national or European normative texts or documents of neoliberal institutions. Our work, therefore, can be useful in exploring how far and in what ways thr individual state schools are being transformed into agencies that help produce a new kind of subjectivity, the neoliberal individual who builds himself as human capital and/or entrepreneur of himself.

Over the last three decades, there has been a noticeable increase in youth not in education, employment, or training (NEET). The term "NEET" emerged in the late 1990s to specifically denote British individuals aged 16 to 18 who were not involved in education, employment, or training. Today, this classification is employed globally to characterize young people who are neither enrolled in education nor par. This category describes a heterogeneous social group, as the theoretical and statistical definitions vary depending on the context of the study participating in the workforce or training programs (Avis, 2014). For instance, the European Union began employing this category to analyse a phenomenon that affects the education and labour sectors, posing risks of social and economic exclusion for individuals aged 15 to 29 (Eurofound, 2012).
Scholars in the fields of education and labor studies have produced diverse analyses of the NEET phenomenon (Furlong, 2007; Maguire, 2015; Pacelli et al., 2023). Avis (2014) suggests that various themes with negative connotations can be identified through the study of NEET individuals, such as anomie (Merton, 1938), cultural deficit (Fryer, 1999), and marginalization manifested in various forms (Pearson, 1975). These dimensions portray the NEET phenomenon in a negative light. Moreover, the NEET phenomenon can be examined due to the transformations within capitalism and the precarization of the labor market (Fumagalli & Morini, 2013; Maguire, 2015).
The educational dimension of the NEET phenomenon is closely linked to the school choice process and the dropout phenomenon (Van Vugt et al., 2024). Additionally, we can analyze the NEET phenomenon as a consequence of educational poverty(Giancola & Salmieri, 2023).
In Italy, the NEET phenomenon gained significance since the late 1990s, with economic crises such as the 2008 financial downturn (Agnoli, 2014) as well as the COVID-19 pandemic, contributing to increased NEET rates in the country.
According to OECD data for Italy in 2021, there was a NEET rate of 26% among young people aged 15 to 29. In 2022, this rate decreased slightly to 23%, though the NEET issue persists as a pressing concern in our context. A recent study by Lazzarini and colleagues (Lazzarini et al., 2020) highlights the absence of effective educational and labour policies that support Italian youth, leaving them vulnerable to social exclusion and the risk of becoming NEET.
In our paper, we aim to depict the NEET condition in Italy following the emergence of the pandemic. For our analysis, we will utilize data from the 2018 and 2021 waves of the PLUS survey (Participation, Labour, Unemployment, Survey) conducted by the National Institute for Public Policies Analysis (INAPP). The PLUS survey includes various items related to educational background, motivations for university choice, inactive status, and other factors, allowing us to investigate how these variables influence the NEET category through a series of regression models. The primary objective of this paper is to provide a comprehensive description of this heterogeneous category in the Italian context.