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

After adjusting for all other variables, the picture of inequality of opportunity changes only slightly. The multivariable regression models in S4 Table show that the inequality of opportu- nity was widespread. SU6_2020 remained the only cluster that did not show significant differ- ences in odds of admission between applicants. The variables which most often influenced the odds of receiving an offer of placement, were migration background, sex, and age. However, the groups within these variables which had higher odds varied between clusters. For example, in seven out of 15 clusters, male applicants had higher odds, whereas in eight out of 15 clusters, women had higher odds. These mixed results were also visible for the urbanity degree of the postal code where applicants lived in 2015, as in some cases, those from (very) strongly urban areas had higher odds, whereas in other clusters, this was the case for applicants from weakly/ non urban areas. In clusters where year of birth mattered, younger applicants (born in 2000 or later) were usually at an advantage. Where groups are underrepresented based on sex, the inequality of opportunity in selection sometimes (slightly) compensates for their underrepresentation, whereas in others, it contrib- utes to it further. In some clusters, however, there is a loss of diversity after offers of admission have been made as well. For example, in SH5_2019, women make up 55% of the applicant pool, 62.4% of the applicants who have been offered admission, but only 50.3% of the enrolled

students. We do not have an explanation for this large difference. In almost all clusters where migration background was significant, applicants without a migration background had higher odds (in 18 clusters). However, in one cluster (SH3_2020), applicants with a Turkish, Moroccan, Surinamese, Dutch Caribbean, or Indonesian (TMSDI) migration background had higher odds, and in one cluster (SU5_2020), applicants with an Other migration background had higher odds than applicants without a migration back- ground. The multivariable models show that inequality based on migration background can- not be explained by the other nine variables included in the model: in the large majority of clusters, migration background remained significant after adjusting for those other back- ground characteristics of applicants. Analyses of the descriptive statistics shows that in 12 clusters where applicants without a migration background had higher odds, they were actually underrepresented in the applicant pool as compared to the higher general or pre-university population. Four of those clusters concerned health professions education programs. In some cases, the inequality of opportunity in admissions is the main reason for underrepresentation amongst students. For example, in SH1_2019 (Allied Medical Care, Nursing & Midwifery), 6% of applicants (117/1942) had a Turkish or Moroccan migration background, which is almost equal to their proportion in the age cohort (6.7%). However, these applicants only made up 1.3% of applicants offered admis-

sion (13/1028). Out of the 491 students who finally enrolled, less than 10 had a Turkish or Moroccan migration background. If enrolment rates had been equal to application rates, then there would have been 30 students in SH1_2019 with such a background. In clusters where parental assets category was significant, those from higher categories con- sistently had higher odds. This was also the case for having healthcare professional parents, and parents who worked in the education sector. Having parents with either a social welfare or other social services income was negatively associated with odds of admission twice. Intersectional acceptance rates To answer the third research question, we calculated the acceptance rates of applicants with either all, some or none of the background characteristics positively associated with odds of admission, based on the final regression models in S4 Table. Table 3 shows the results of this intersectional analysis for each study cluster. For example, the row of cluster SH1_2020 shows 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 12 / 24