Gatekeeping at work: a multi-dimensional analysis of student, institutional, and employer characteristics associated with unpaid internships

Internships are recognized globally as a high-impact practice that substantially enhances students’ future prospects. However, concerns persist about their legality and potentially exclusionary nature. While prior research indicates participation varies based on key variables, such as gender and maj...

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Bibliographic Details
Published in:Higher education Vol. 89; no. 4; pp. 907 - 936
Main Authors: Jang-Tucci, Kyoungjin, Hora, Matthew T., Zhang, Jiahong
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.04.2025
Springer Nature B.V
Subjects:
College students
Compensation
Data
Demography
Dimensional analysis
Education
Employers
Gatekeeping
Gender
Gender differences
Higher Education
Interaction analysis
Interaction Process Analysis
Internship programs
Internships
Legality
Majors (Students)
Minority Serving Institutions
Participation
Race
Social justice
Student Participation
Students
Unpaid
Work
Student success
Linear probability model
Access
Unpaid internships
Work-based learning
Multi-actor frameworks
Minority-serving institutions
Inequality
ISSN:0018-1560, 1573-174X
Online Access:Get full text
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Summary:Internships are recognized globally as a high-impact practice that substantially enhances students’ future prospects. However, concerns persist about their legality and potentially exclusionary nature. While prior research indicates participation varies based on key variables, such as gender and major, empirical work remains limited and tends to focus on univariate or single-actor explanations. We employ multi-actor models from management studies to analyze survey data ( n  = 1153) from 13 U.S. institutions, nine of which are minority-serving institutions (MSI). The data reveal that only 30.3% of the students participated in internships, of which 43.4% were unpaid. Linear probability analysis results indicate that contrary to expectations, individual demographic characteristics, such as gender, were not significant predictors of internship compensation on their own. Instead, academic, institutional, and employer characteristics emerged as significant predictors. Interaction analysis results highlight the combined effects of race, gender, major, MSI status, and employer characteristics in predicting participation in unpaid internships. Further, the data suggest that gender effects are largely influenced by academic major affiliation, emphasizing that unpaid internships do not uniformly affect all students but are predominant in specific sub-groups. We conclude by proposing a strategy to eliminate unpaid internships in the interest of transformative social justice work.
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ISSN:0018-1560
1573-174X
DOI:10.1007/s10734-024-01254-6