Inequality of opportunity in selection procedures limits diversity in higher education: An intersectional study of Dutch selective higher education programs

Socio-economic status indicators. When it comes to parental assets category, we see that compared to the age cohort, applicants and students from percentile category 1–40 (the 40% of households with the lowest assets levels) are underrepresented in every cluster except SH2. This is also the case when compared to the eligible pool (higher general for HBO programs, pre-university for university programs), except in SH2, SH6 and SU2. The same can be observed for parental income category: compared to the age cohort, per- centile category 1–70 is underrepresented in every cluster except SH2 and SU2. Compared to the eligible pool, students from this income category are underrepresented in every cluster except SH2, SH3_2019, SH6, SU1, and SU2. This means that in the majority of clusters, appli- cants and students who have parents belonging to the top-30% of the income distribution are disproportionately present. This is especially the case for cluster SU4 (Biomedical Sciences, Biomedical Engineering & Clinical Technology), where more than 85% of students have parents in the top-30% income category, and cluster SU1 (Medicine), where this is the case for at least 78% of students. Compared to the age cohort, students with parents who receive social welfare are underrep- resented in all clusters except SH2 and SU2. Relative to the eligible pool, they are underrepre- sented in all clusters except SH2, SH3, SH6, SU1, SU2, and SU3_2020. Migration background. Compared to the age cohort, students without a migration back-

ground are overrepresented in SH1, SH4, SU4, and SU5. Students with certain migration back- grounds, such as Turkish or Moroccan, Surinamese, Dutch Caribbean or Indonesian, are underrepresented in the majority of clusters. This pattern is less evident when compared to the eligible pools. Students with an EU/EEA/Swiss migration background are overrepresented in most university clusters, both compared to the age cohort and the pre-university population. Finally, students with an Other migration background are sometimes (slightly) underrepre- sented, but more often overrepresented compared to the age cohort and eligible pools. Variables associated with odds of admission Research question 2 aimed to find out which demographic background variables are associated with an applicant’s odds of admission into selective HE programs. We excluded the variable ‘born in The Netherlands or abroad’ from our logistic analyses, as the variable ‘migration back- ground’ is also based on (parental) country of birth. We used ‘migration background’ as it is able to give more detailed information on someone’s background. However, the variable ‘born in The Netherlands or abroad’ remained in the descriptive statistics (S3 Table), to provide a better insight into those applicants who, according to the Statistics Netherlands definition, have a ‘migration background’ despite being born in The Netherlands. Their ‘migration back- ground’, as defined by Statistics Netherlands, is then based on the country of birth of at least

one of their parents. No evidence was found for multicollinearity in the remaining variables (the highest Pearson correlation was 0.389). S2 Table contains the univariable logistic regres- sion results for each cluster, and S4 Table gives the multivariable regression models. The univariable logistic regression results show that only in study cluster SU6_2020 (4 Arti- ficial Intelligence programs; 2 Industrial Design programs), none of the individual variables were significantly associated with the odds of admission. All other study clusters showed sig- nificantly different odds based on at least one variable. The variables most often associated with the odds of admission, were migration background (23 out of 30 clusters), assets category (20 out of 30 clusters), and income category, sex and urbanity degree (all 17 out of 30 clusters). The variable associated the least often, was having 1 or 2 parents working in the primary, sec- ondary or vocational education sector (6 out of 30 clusters). PLOS ONE Inequality of opportunity in Dutch selection procedures limits diversity in higher education PLOS ONE | https://doi.org/10.1371/journal.pone.0292805 October 13, 2023 11 / 24