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

that applicants who were female, were born in 2001 or earlier, had parents in the income cate- gory 71–90, had 1 or 2 healthcare professional parents, had no migration background, and who lived in a postal code area which was averagely to weakly/not urban, had significantly higher odds of admission. Cluster SH1_2020 had 25 applicants in total who met all six criteria, and 20 of them received an offer of admission. This means group ALL had an acceptance rate of 80%. On the other hand, group NONE had an acceptance rate of 15.4%. As the number of persons accepted in group NONE was smaller than 10, Statistics Netherlands regulations do not allow the publication of the precise frequencies. However, the ratio of 80/15.4 = 5.19 indi- cates that applicants in group ALL were offered a place more than 5 times as often as applicants in group NONE. The ratios in Table 3 show that depending on the cluster, group ALL was admitted between 1.06 and 9.01 times as often as group NONE. The study clusters with the highest number of sig- nificant variables in the multivariable models are HBO clusters, resulting in the largest differ- ence in acceptance rates between group ALL and group NONE (see Ratio column in Table 3). To advance our intersectional understanding of (dis)advantage, Fig 2 gives the example of cluster SU4_2020. Two variables were significantly associated with odds of admission: migra- tion background and parental assets category. Acceptance rates differed vastly between groups: 50.1% for applicants without a migration background, 36.4% for TMSDI applicants, and

43.4% for applicants with an other migration background. When we divide these three groups based on parental assets category, we see additional discrepancies. Acceptance rates differed the most between applicants who had a TMSDI migration background and whose parents belonged to the lower assets category (28.8%) versus applicants who had no migration back- ground and whose parents belonged to the higher assets category (50.9%). Those with a TMSDI migration background and parents in the higher assets category, had a lower accep- tance rate (37.3%) than the applicants without a migration background or with a different migration background from the lower assets category (38.7% and 39.8%). In S1 File, similar figures can be found for all clusters. These illustrate how the variables which were significant in the multivariable model, result in (vastly) different acceptance rates for different (intersec- tional) groups, which diverge from the average acceptance rates as included in Table 1. The intersectional acceptance rates in S1 File add value to our understanding of inequality of opportunity in the selection procedures for higher education. They show that different groups of applicants (based on a combination of e.g. sex, migration background, and other variables) have very different chances of success in different programs. For example, in cluster SH5_2020 (Creative Media and Game Technologies; Fashion & Textile Technologies; Indus- trial Design Engineering; and Art and Economics), female applicants from (very) strongly

urban areas without a migration background had an acceptance rate of 80.8% (198/245). In comparison, the same cluster only accepted 56.0% of female applicants from (very) strongly urban areas with a TMSDI migration background (28/50). In cluster SU1_2019 (Medicine), female applicants without a migration background had an acceptance rate of 48.4% (1504/ 3107), while this was only 32.0% for male applicants with a TMSDI migration background (81/253). The latter percentage is significantly lower than the average acceptance rate of the cluster: 45.4%. Combined with the fact that male TMSDI applicants comprised the smallest group of the applicant pool (with only 253 applicants out of 6571 applicants - 3.9%), their low odds of admission contribute to their underrepresentation in the student population. This is part of a pattern found in many clusters. The result is that the student population of many selective programs does not reflect the diversity of the age cohort, the higher general or the pre-university population (see S3 Table). 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 15 / 24

Discussion This study aimed to investigate 1) the representativeness of student populations of selective HE programs, compared to the applicant pool and their age cohort; 2) the demographic back- ground variables which are associated with an applicant’s odds of admission into selective HE programs; and 3) the acceptance rates of applicants with either all, some or none of the back- ground characteristics positively associated with odds of admission. The results show that student representativeness in selective HE programs is limited. Part of the reason for this, is self-selection. Applicants are often not representative of their respec- tive eligible pools or the age cohort, especially in the case of sex–as men are underrepresented in the applicant pools of almost every cluster. However, this is not the only reason for the underrepresentation of different groups in selective HE programs. The widespread inequality of opportunity in the selection procedures, both at HBO and university level, contributes to the pattern that student populations do not represent their age cohort, the higher general or Fig 2. Intersectional acceptance rates in the cluster SU4_2020 (Biomedical sciences and clinical technology in 2020). Note: numbers do not always add up due to missing data on parental assets category. https://doi.org/10.1371/journal.pone.0292805.g002 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 16 / 24

the pre-university population. Only one cluster (Artificial Intelligence and Industrial Design in 2020) did not show significantly different odds of admission based on any of the ten back- ground characteristics of applicants. The intersectional analyses show that there are vast differences in acceptance rates between group ALL and group NONE (Table 3). Additionally, Fig 2 and the online figures in S1 File show the acceptance rates for groups which possessed some, but not all, of the background characteristics positively associated with odds of admission. Table 1 and the figures prove that the discussion on inequality in selective admissions cannot be based on single background characteristics only: we need an intersectional understanding of inequality. However, the char- acteristics which resulted in higher odds were not always what one would expect, based on intersectionality theory. For example, men did not always have higher odds of admission, nor did applicants from the highest SES categories or without a migration background. Addition- ally, whereas international evidence suggests predominantly lower odds for rural applicants [32, 33], this was not always the case in our study: in several study clusters, applicants from weakly to non-urban postal codes had higher odds of admission, whereas in others, their odds were significantly lower. This shows that (dis)advantage is context-dependent. In this section, we discuss the significance and implications of our findings. The variable which most often related to inequality of opportunity, was migration back-

ground. In almost all these cases, applicants with a migration background had significantly lower odds of admission (both at HBO and university programs) than applicants without a migration background. This is problematic, as Dutch and international research shows that children with certain migration backgrounds face numerous obstacles in the educational sphere that limit their potential, starting from a very young age [3, 25, 26, 28]. The inequality based on migration background in admission to HE could not be explained by socio-economic characteristics, as there are several SES indicators included in the multivariable model, which already results in adjusted ORs for migration background. This finding is in line with interna- tional research on inequalities based on ethnic background in admission to HE [19, 30, 31]. In clusters where socio-economic indicators influenced the odds of acceptance, applicants from lower and average SES backgrounds were usually (but not always) at a disadvantage. This is disconcerting as well, since these applicants have previously faced several obstacles in and beyond the educational system [55]. As discussed in detail in the results, applicants who do not belong to the highest SES groups are already (strongly) underrepresented in the applicant pools of the majority of clusters. For the period of 2016–2018, Mulder et al. (2022) found that having healthcare professional parents resulted in significantly higher odds of admission in Medicine, Clinical Technology,

Dentistry and Pharmacy. The data presented in this study show that this was no longer the case in 2020 for these programs. However, in other healthcare related study programs, appli- cants with healthcare professional parents did have significantly higher odds: For HBO pro- grams, these are Allied Medical Care, Nursing, Midwifery, Dental Hygiene, Denturism, Optometry, Physiotherapy, Psychomotoric Therapy/Psychomotricity, and Sport Studies. For university programs, these are Psychobiology and Psychology. The previously detected inequality based on migration background and sex (Mulder et al., 2022) are no longer found in the 2020 cluster of Medicine (SU1_2020) and Dentistry and Pharmacy (SU2). We speculate that the broad attention towards potential inequality of oppor- tunity in their selection procedures in recent years has led to effective changes to selection pro- cedures. However, it remains important to note that our analyses concern the nation-wide applicant pools for these programs. A study by Fikrat-Wevers et al. [56] showed that within individual Medicine, Pharmacy and Clinical Technology faculties, inequality of opportunity in selection still existed. Their multi-site study indicated that men performed significantly poorer 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 17 / 24

on CVs, but had higher biomedical knowledge test scores than women. On curriculum-sam- pling tests, applicants with a non-Western migration background had lower scores. CV-scores were lower for first-generation Western immigrants, and significantly lower GPAs were found for first-generation university applicants. Since each program decides the content of their selection procedure, it is important to consider the unique selection context of each HEI. The results of Fikrat-Wevers et al. and this study show that inequality of opportunity in the selec- tion is context-dependent and may change over the years. Therefore, we suggest it is important to replicate our research in future years. Rejecting low SES applicants and applicants with a migration background results in a loss of potential. Individuals who managed to overcome multiple barriers on the way to reaching the eligibility requirements of HE [26] are definitely talented. Moreover, in some contexts, stu- dents from lower SES backgrounds perform better than students who came from higher SES backgrounds and were admitted with the same or a similar GPA [28, 57, 58]. Thus, by rejecting low SES applicants, HEIs lose out on these applicants’ contributions to educational quality. Furthermore, society foregoes the opportunity of providing upward socio-economic mobility [59] to members of families who may have had low wealth for generations, which constitutes an economic loss. Society also misses out on the contributions which these applicants could

bring to future policymaking and leadership, which are grounded in experiences, knowledge and skills that current leadership from high wealth backgrounds lacks [60]. In our study, the majority of applicants with a migration background are born in The Neth- erlands (and one or both parents were born abroad). Based on this, these applicants are likely to speak other languages in addition to Dutch and English, which can be useful in the labor market after graduation. For example, most selective programs are related to healthcare (see Table 1). Multilingualism is highly valuable in providing excellent patient care in a diverse society [61]. When a fair share of applicants with a migration background would be admitted, then it is more likely that future healthcare providers are able to speak the languages of the patients they serve. They may also bring with them socio-cultural knowledge that applicants without a migration background may lack. These skills and knowledge bases would be imme- diately beneficial to culturally sensitive healthcare provision in a multicultural society, and in improving health equity [61, 62]. For example, the study cluster with the highest level of inequality partly consists of Midwifery, for which there are no non-selective alternatives in The Netherlands. This is a healthcare field in which globally, societal inequities and systemic injustice play a large role in unequal outcomes in sexual, reproductive, maternal and newborn health [63]. It is therefore of utmost importance that student populations of these programs

become representative of their patient population, in order to contribute to equitable health- care provision in these areas. Recommendations In ‘The Tyranny of Merit’ [14], Michael Sandel discusses why rejecting low SES applicants in favor of applicants from elite backgrounds is an affront to the idea of meritocracy. He therefore proposes the use of lottery-based admission. However, our research shows that compared to the age cohort, applicant pools of many selective programs predominantly consist of students from higher SES backgrounds without an underrepresented migration background (such as Turkish, Moroccan, Surinamese, Dutch Caribbean or Indonesian). This is especially the case for university programs. The lack of a representative applicant pool, especially in the area of migration background and SES, is mainly due to educational inequality on the route to the gates of HE (e.g. lower expectations by the education system [26] in the transition from pri- mary to secondary school), resulting in non-representative higher general and pre-university 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 18 / 24

student populations. Lottery-based admission will therefore still result in an overrepresenta- tion of high SES students without a migration background, and will not ensure the entry of low SES applicants with an underrepresented migration background [17]. Furthermore, an interview study with Dutch applicants to undergraduate health professions education pro- grams shows that there is no (sub)group of applicants which favors lottery, regardless of whether this is a weighted or a random lottery. They prefer selection instruments on which they can feel more ‘in control’ [64]. The data in this study suggests that in the Dutch context, equitable admissions procedures (which give more opportunities to applicants from underrep- resented groups which face significant barriers on the road to HE), have a better chance of increasing the (intersectional) diversity of student populations in selective HE programs than a lottery can achieve. These findings can be relevant in other higher education contexts where applicant pools are also not representative of the general society. We recommend improved national monitoring in the future of the (potential) inequality of opportunity in HE selection procedures, and its effects on the (intersectional) representative- ness of the student population. Furthermore, as Steenman argues, “using selection is generally seen as limiting the standard idea of accessibility and should therefore be used sparingly” [65]. HEIs could consider whether, in the absence of government-mandated capacity limitations, it

is justified to use a selection procedure as long as there is no guarantee that this does not harm equality of opportunity and student diversity. Furthermore, we recommend a detailed study of the selection procedures in the only cluster which did not show any inequality of opportunity, as other selective programs may be able to learn from the Artificial Intelligence and Industrial Design selection procedures in the year 2020. In the same way, study programs in which certain groups are underrepresented (e.g. on the basis of sex or migration background) and/or had lower odds of admission, may be able to learn from other clusters where these underrepresented groups had an advantage in the selec- tion procedure. On methodology. Our findings show that it is important to study inequality of opportu- nity through an intersectional lens [8]. The acceptance rates of groups ALL versus NONE indi- cate the vast differences of odds of admission between applicants who either have all the beneficial ‘check marks’ [60] in a selection procedure, and those who have none. We therefore recommend future quantitative (international) research to build on our methodology, when- ever suitable, to create intersectional analyses based on the multiplicity of identity layers that people have. We believe our method to be easier to interpret than traditional regression mod- els with interaction terms. This is especially relevant when the research focuses on inequalities between groups. In order to address and resolve inequities, such as in the educational sphere,

it is important that these inequities are clearly demonstrated in a way that all audiences can understand. This is what Table 3 in this article has intended to do. Limitations A limitation is that we could not use parental education levels, as these data are largely missing for parents who were educated outside of the Netherlands. For parents who were educated in The Netherlands, educational achievement is not sufficiently standardized. We circumvented this limitation by including other SES variables that served as proxies. In the original data, assets percentiles are registered at the household level, on the person who is the main breadwinner. When parents are divorced and live in separate households with partners who each are the main breadwinners, this results in missing data on parental assets. For a few clusters, parental income and assets data was missing for more than 20% of appli- cants (namely in SU3, SU7, SU8 and SU9). It is possible that in these cases, applicants were e.g. 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 19 / 24

internationals who were already living in The Netherlands (giving them a Citizen Service Number), but for whom parental data are missing as parents live abroad. Six out of eight of the multivariable models of these clusters showed no significant association between income/ assets category and the odds of admission. Due to the high amount of missing data, these results must be interpreted with caution. In this research, we assumed that data was missing completely at random. However, there are methods for handling missing data, e.g. multiple data imputation [66]. In future work, we could investigate the usefulness of multiple data imputation. Our focus in this paper was to create association models for the two academic years under study in the Dutch HE context. These models are not prediction models, which could be con- sidered a limitation. Furthermore, the models are not validated in other contexts or other years. To generalize our results to other contexts, we would need to create prediction models which are validated based on data from an external context and from different time periods, which we do not have. Because of the explorative nature of our study, in which our goal was to find out which variables are more or less related to gaining admission to selective programs, we did not adjust the p-values for multiple testing. Finally, in Table 3, Group ALL and NONE consist in some study clusters of small numbers of applicants and/or admitted students. We addressed this limitation by creating figures simi- lar to Fig 2 for all clusters (in S1 File). Future research

The diversity of the student populations as shown in S3 Table only applies to the students at selective programs. As shown in S1 Table, there are many HEIs that do not use selection for the same study programs. Therefore, S3 Table lacks the composition of the student populations of comparable non-selective programs (e.g. in Nursing, where only one out of 17 HBOs used selection). It is possible that self-selection has an effect both on whether and where students apply to such programs, and that certain groups would rather avoid a selection procedure compared to other groups. Thus, further research could investigate potential differences in stu- dent diversity between selective and non-selective programs. This could also aid in mapping the diversity and representativeness of the future workforce in these fields. Although our quantitative analyses found widespread inequality in selection to Dutch HE, our data cannot explain what causes this inequality (e.g. do certain selection instruments dis- advantage certain groups? Are certain instruments used more often in some clusters than in others?) and why there are such large differences between clusters. Future work could answer these questions with qualitative or mixed-methods research. Lastly, as we were not able to acquire data on parental jobs for this study other than jobs in the healthcare professions or in education, future research could investigate if other types of parental jobs also have an influence on the odds of admission. Supporting information S1 Table. Clusters of study programs. (DOCX)

S2 Table. Results from the univariable logistic regression. *p < .05; **p<0.01; ***p<0.001. Ref. = reference category; PSVA = Primary, Secondary or Vocational or Agrarian; TMSDI: Turkish, Moroccan, Surinamese, Dutch Caribbean, or Indonesian; Urbanity Degree of postal code of residential address in 2015. Results in bold are statistically significant (p < .05). (DOCX) 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 20 / 24

S3 Table. Demographic data of all applicants who received a ranking number; all appli- cants who were offered a seat in the program; and all those who became registered stu- dents, for each cluster. (XLSX) S4 Table. Results of multivariable logistic regression for the final model for each group, performed on applicants eligible for placement. *p < .05; **p<0.01; ***p<0.001. Ref. = ref- erence category; PSVA = Primary, Secondary or Vocational or Agrarian; TMSDI: Turkish, Moroccan, Surinamese, Dutch Caribbean, or Indonesian; Urbanity Degree of postal code of residential address in 2015. Results in bold are statistically significant (p < .05). (DOCX) S1 File. Intersectional acceptance rates based on background characteristics. (DOCX) S2 File. Research protocol, including all procedures, sources of variables, and software syn- tax for statistical analysis. (XLSX) Acknowledgments The authors wish to thank all involved people working at Statistics Netherlands, Studielink, the Universities of The Netherlands (UNL) and Vereniging Hogescholen for their cooperation in making this research project possible, and for permitting access to the required datasets. The authors also wish to thank their colleagues at Amsterdam UMC, location VUmc for their valuable feedback on our manuscript. We also thank Ine Vos for her valuable insights on selec- tion procedures and eligibility requirements. Author Contributions Conceptualization: Lianne Mulder. Data curation: Lianne Mulder. Formal analysis: Lianne Mulder. Funding acquisition: Lianne Mulder, Gerda Croiset, Rashmi A. Kusurkar, Anouk Wouters.

Investigation: Lianne Mulder. Methodology: Lianne Mulder, Eddymurphy U. Akwiwu, Jos W. R. Twisk, Andries S. Koster, Anouk Wouters. Project administration: Lianne Mulder, Anouk Wouters. Supervision: Eddymurphy U. Akwiwu, Jos W. R. Twisk, Andries S. Koster, Jan Hindrik Ravesloot, Gerda Croiset, Rashmi A. Kusurkar, Anouk Wouters. Visualization: Lianne Mulder, Anouk Wouters. Writing – original draft: Lianne Mulder. Writing – review & editing: Lianne Mulder, Eddymurphy U. Akwiwu, Jos W. R. Twisk, Andries S. Koster, Jan Hindrik Ravesloot, Gerda Croiset, Rashmi A. Kusurkar, Anouk Wouters. 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 21 / 24

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