Social Embeddedness in Social Networks and the Reproduction of Socioeconomic Inequality in Educational Attainments (SERIOUS)

• Applicant: Professor Dr. Marc Keuschnigg, since 10/2024
• Subject Area: Empirical Social Research
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 462521224

The proposed project aims at providing novel evidence on whether and how the social embeddedness of students exacerbates socioeconomic inequality in educational attainments. Many studies on educational inequality focus on social class differences in the endowment of resources that enhance scholastic performance. However, there is only very few direct evidence on how the frequency and intensity of social interaction, the attributes and behaviours of peers, and aspects of the social structure relate to educational inequality. In particular, research is mostly uninformative on the role of actual student interactions within schools. The project will close this gap by examining, on the one hand, relationships between the two macro-level phenomena of socioeconomic disparities in social capital access and socioeconomic disparities in educational outcomes. On the other hand, the project will investigate micro-level mechanisms underlying the two macro-level phenomena and their relationships. These mechanisms include, first, processes that drive the formation of social ties among students and shape differences in the access to social capital. Second, it will be investigated if socioeconomic differences in social capital access unfold through network effects. A third focus will be on contextual variance in these processes. The analytical focus will be on informal social networks that students build within schools based on friendships and help. Recent social network studies highlight that such networks must be considered as a key dimension of students’ social embeddedness. The empirical work will be based on large-scale longitudinal datasets from the Netherlands, Sweden, England, and the US as well as a unique, representative, and very powerful cross-sectional data set from Germany. The application of advanced and novel methods for the analysis of social network data will be combined with multilevel regression methods to analyse these data. On the contextual level, intra- and international comparisons will allow identifying differences between different school forms and schooling systems. The research will also consider that social origin intersects with both ethnicity and gender. A central hypothesis is that peer effects magnify initial social disparities in educational outcomes over time and lead to the cumulative advantage for children from upper-class families. This would happen if students from families with similar socioeconomic status and, thus, similar resources endowments (e.g., in terms of abilities and skills) cluster in social networks. Moreover, in comprehensive schooling systems, socioeconomically disadvantaged students might have more opportunities to interact with more privileged peers than in ability-tracked systems. Based on this assumption, the project will examine if possible effects of the students’ social embeddedness on socioeconomic educational inequality vary between comprehensive and ability-tracked schooling contexts.

• DFG Programme: Research Grants
• Ehemaliger Antragsteller: Professor Dr. Georg Lorenz, until 9/2024