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The geography of social background inequalities in educational opportunities: a within-country study

Articolo
Data di Pubblicazione:
2025
Citazione:
The geography of social background inequalities in educational opportunities: a within-country study / M. Triventi, E. Fedeli. - In: EUROPEAN SOCIETIES. - ISSN 1461-6696. - (2025), pp. 1-32. [Epub ahead of print] [10.1162/euso.a.35]
Abstract:
We examine the main sources of social background inequalities in academic track enrolment whether their relative importance varies across 103 Italian provinces. We distinguish between three channels by which social inequalities in educational transitions are reproduced, the ‘primary’, ‘secondary’, and ‘tertiary’ effects (Boudon, 1974; Esser, 2016). They refer to the role of individual competencies, teachers’ assessments, and family decisions, respectively. We compiled a student population panel dataset from the INVALSI-SNV, following 1,344 million students from five cohorts (2013–2017) enrolled in the 8th grade of lower secondary school (untracked) to the 10th grade of upper secondary education (tracked). The results from the KHB method suggest that families’ choices are the prevalent source of reproduction of inequalities in academic track enrolment, followed by tertiary and then primary effects. Interestingly, we find more geographical heterogeneity in the channels by which educational inequalities are reproduced than in the total inequality by social background. Moreover, our findings align with the Incremental Reproduction regime found in cross-national studies, emphasising that an increase in secondary effects does not correspond to any notable variation in the magnitude of primary or tertiary effects.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Inequality of educational opportunity; social background; teachers’ marks; academic performance; family decisions; geographical inequality
Elenco autori:
M. Triventi, E. Fedeli
Autori di Ateneo:
FEDELI EMANUELE ( autore )
TRIVENTI MORIS ( autore )
Link alla scheda completa:
https://air.unimi.it/handle/2434/1184995
Link al Full Text:
https://air.unimi.it/retrieve/handle/2434/1184995/3146816/2025_EurSoc_geoINEOpp.pdf
Progetto:
A Novel Look at Socio-Economic Inequalities using Machine Learning Techniques and Integrated Data Sources (INEQUALITREES)
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Settori


Settore GSPS-05/A - Sociologia generale
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