Conference Agenda

Session
OS-172: The role of networks in education and labor markets 2
Time:
Thursday, 26/June/2025:
3:40pm - 5:20pm

Session Chair: Annatina Aerne
Session Chair: Mattia Vacchiano
Session Chair: Maria Prosperina Vitale
Location: Room 202

Session Topics:
The role of networks in education and labor markets

Presentations

Navigating structural constraints: Educators’ personal networks and the dynamics of professional knowledge work

Liam Bekirsky, Bernie Hogan

University of Oxford, United Kingdom

Educators operate within layered structural constraints—bureaucratic, technological, and professional—that shape how they access, share, and create resources. We investigate these constraints by drawing on a qualitative mixed-methods study of UK teachers’ personal networks. We focus on how teachers adapt to these constraints through formal and informal means relying on both school-based and external social contacts.

Findings suggest that educators adopt different strategies in selecting collaborators and sustaining professional relationships based on structural constraints. For example, institutional policies and hierarchies often limit direct collaboration, creating negative space where informal networks emerge as counterstructures. Some teachers prioritise closure, seeking shared norms, community building, and institutional alignment, while others take on brokerage roles, reaching beyond their immediate circles to integrate external resources. However, these logics are not mutually exclusive - educators shift between them in response to technological and institutional barriers, such as policies restricting resource sharing via platforms like OneDrive.

Educators with greater institutional support are more likely to act as brokers, while those in precarious roles (early-career teachers) tend to be limited by their perceived network isolation. This study highlights the use of personal networks in understanding how educators navigate structural affordances and pressures to bridge structural divides.

As AI resource creation tools are increasingly integrated into teachers’ professional work, the presentation will also consider how network structures might shape their adoption and dissemination across professional communities. The findings provide insights into how institutions can better support collaborative resource creation as digital and AI-driven tools reshape professional knowledge work.



Neighborhood peer effects in school choice

Quentin Ramond

Universidad Mayor, Chile

This article examines the extent to which neighborhood peers influence families’ school choice, and whether this effect varies according to socioeconomic background. It uses geocoded administrative data from Chile, where families have to rank and apply to schools through a centralized admission system. I build a unique longitudinal dataset linking four applicant cohorts to their nearest neighbors who applied to the same grade the years before, which allows to account for several endogeneity issues when identifying peer effects. I define peer group with egocentric neighborhoods, a series of local environments surrounding each student based on geographic distance and population density that approximate meaningful exposure and social interactions. I estimate a series of logistic regression models that analyze similarity in applications to specific schools as well as similarity in the ranking of these applications. Then, I use an instrumental variable approach to assess whether choosing the same school as neighbors leads to applying or not to schools with different student body composition, test scores, and instructional resources.

The results indicate that low-SES students are most likely to conform to their neighbors' choice, especially when the latter are from slightly higher-SES background. This pattern suggests that low-SES families face greater informational frictions in the school market or may face higher social costs of deviating from neighborhood peer norms, such that the information acquired through neighborhood interactions becomes more consequential for choosing a school among these groups. Then, the study shows that choosing the same school as more (dis)advantaged neighbors is (causally) associated with application to different schools, especially regarding the student body socioeconomic composition.

I conclude that geographically embedded social interactions influence the process of school choice and thereby may contribute to sustaining school segregation, with potential far-reaching consequences for the persistence of social inequality. The results also highlight the need for public policy to consider neighborhood social interactions to mitigate spatial and social disparities in educational opportunities.