High School Choices by Immigrant Students in Italy: Evidence from Administrative Data

Koray Aktas, Gianluca Argentin, Gian Paolo Barbetta, Gianna Barbieri and Luca Colombo

No def108, DISCE - Working Papers del Dipartimento di Economia e Finanza from Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE)

Abstract: We investigate the educational choices of first- and second-generation immigrant students at the transition between lower-secondary school and high school by exploiting a large longitudinal dataset of about 50,000 students in Italy. We find that immigrant students are less likely to choose challenging academic track high schools compared with their Italian counterparts, after controlling for household characteristics, school fixed e ects, and students' performance. We show that systematic di erences in teachers' evaluations received by the two groups of students are an important driver of the observed di erences in educational choices by immigrant and native students. In particular, after controlling for observable characteristics, we find that immigrant students are more likely to be formally advised by their teachers to choose vocational or technical high schools rather than academic tracks, re ecting a discrimination bias that has not previously been emphasized in the literature. This suggests the role of a new dimension of policy intervention aimed at reducing the possibility of teachers' induced discrimination based on implicit stereotypes.

Keywords: immigrant students; high school choice; academic track; discrimination biases; implicit stereotypes. (search for similar items in EconPapers)
JEL-codes: I21 I24 I26 I28 (search for similar items in EconPapers)
Pages: 43
Date: 2021-07
New Economics Papers: this item is included in nep-edu, nep-mig and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Journal Article: High School Choices by Immigrant Students in Italy: Evidence from Administrative Data (2022)
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