Background characteristics and educational inequality

On the level of individual background characteristics, there are several general patterns of educational inequities. For example, students from socio-economically disadvantaged backgrounds are known to face numerous obstacles on the route to HE [15], including disadvantages in competitive selection procedures [3, 16, 17]. Applicants from a low SES background are often already underrepresented in the applicant pools of competitive HE programs [18]. There, they find themselves contending against applicants from high SES backgrounds, who had more resources to adequately prepare them [1]. Oftentimes low SES applicants lose this competition, not because they are less qualified, but because the selection procedures are (unintentionally) biased against them [19–21]. This is a major problem, as socio-economic conditions are likely the most important underlying factor influencing educational inequalities [1, 22].

Parental occupations may also influence odds of HE participation [1] or odds of admission in a selection process. For example, students with at least one parent in a Science, Technology, Engineering, and Mathematics (STEM) related job show higher levels of STEM interest, motivation and achievement, and choice to major in a STEM field [23]. Their higher achievement levels [23] are likely to result in higher odds of admission in selection procedures which give weight to previous scholastic achievement. Another example are the higher odds of admission to health professions education programs for applicants with at least one parent who is a registered healthcare professional [13]. Interviews with high school students who were interested in health professions education programs also showed how a social network connection in the medical field helped students to develop their interest for such programs, and why a network connection was the most important facilitator in preparing for the selection procedure [24].

Furthermore, having an underrepresented ethnic or migration background is associated with educational disadvantage and barriers to optimal achievement due to e.g. microaggressions and racism [25], lower expectations by the education system [26], the effects of testing language [27], and differential access to high-quality and highly-resourced schools [3, 26, 28]. As a consequence, in many countries, applicants with such backgrounds have lower odds of admission than their white peers without a migration background [13, 29–31].

Finally, rural applicants may be at a disadvantage in entering HE. For example, Canadian research has reported lower performance in a multiple mini interview of applicants graduating from a rural high school [32]. In China, educational stratification is mainly driven by a rural-urban divide in which rural schools are disadvantaged in numerous ways. For example, they receive lower levels of funding and have poorer teacher qualifications, negatively affecting school enrollment and educational attainment [33]. Elite universities have witnessed an increasing rural-urban enrollment disparity: In the 1970s, one in two first-year students were from a rural background; whereas in 2011, this was one in seven [34].

Altogether, the aforementioned examples of unequal access to HE represent a major problem for educational quality, as a diverse, representative student population has been shown to be associated with better educational experiences for students [3–5]. Furthermore, disadvantaging applicants who are already underrepresented in HE reproduces societal and educational inequities [14, 21]. Admissions committees should use valid selection methods [35], but research by the Dutch Inspectorate of Education suggests that this is not always the case [36]. However, it is unknown what the effects are of belonging to multiple (dis)advantaged groups at the same time. This study aims to address the gap in the literature of the intersectional (dis)advantages which applicants face in accessing selective programs in HE, based on nationwide data from The Netherlands.

The context: Higher education in The Netherlands

In The Netherlands, the typical route to HE consists of several steps. In the transition from elementary to high school, around ages 11–12, pupils are placed in either the pre-vocational (vmbo, 4 years), higher general (havo, 5 years), or pre-university (vwo, 6 years) track. Research has shown that there is inequality of opportunity in the placement into these tracks, and that the capabilities and potential of certain groups of children (e.g. lower SES, with certain migration backgrounds, from rural areas) are underestimated [37–39]. Although transitioning between tracks is possible, this is an exception [26], and the rates at which this happens differ greatly between provinces [40]. This means that for many children, their educational route is determined at an age which is lower than in many other countries [39]. This contributes to inequality of opportunity [39].

In the final year of high school, pupils can apply to post-secondary education programs. Most higher general pupils enter HBO, but they could also enter vocational education. Most pre-university pupils enter university, but they too have the option to enter HBO or vocational education. Of these post-secondary education programs, only higher education institutions (HEIs) use selection to admit students. These include higher professional education (in Dutch: HBO–often referred to as university of applied sciences), and university. For sake of readability, we have used the Dutch abbreviation HBO in this manuscript.

Within HEIs, most study programs are not selective, but programs may use specific eligibility criteria. Applicants with other (non-traditional) educational backgrounds than specified in the eligibility criteria must prove that they meet equivalent educational levels [41]. For example, for many HBO programs, it is possible to apply with either a higher general diploma or a vocational education diploma. For many university programs, it is possible to apply with either a pre-university diploma, or with a completion certificate of all first-year courses of a (related) HBO study program called propedeuse.

Some study programs have to use selection procedures, due to capacity limitations called numerus fixus (fixed number). When there are more eligible applicants than seats, institutions must use at least two types of qualitative selection criteria (e.g. intelligence, motivation, study skills) to determine who will be offered a place in the program [42]. In the selection procedure, diversity may be lost, which could result in underrepresentation of certain groups. However, apart from four university health professions programs for which inequalities based on sex, migration background, socio-economic status and having healthcare professionals as parents were found [13], it is unknown to what extent this is the case across the entire HE domain. Therefore, we wanted to investigate the possible inequality of opportunity in all selective HE programs. We hypothesized that there could be study programs with less or no inequality of opportunity, and programs in which underrepresented applicants have higher odds of admission. Programs with proven inequality of opportunity in the selection procedure, could then learn from the programs who offer equal or equitable opportunities to applicants. Additionally, based on the importance of a representative workforce in certain labor markets (e.g. healthcare), we were interested in whether there are study programs where potential best practices could be found in admitting underrepresented applicant groups. Thus, we aimed to answer the following research questions:

1. How representative are student populations of selective HE programs, compared to: a) the applicant pool; b) the higher general population and pre-university population; and c) their age cohort?
2. Which demographic background variables (e.g. sex, migration background, year of birth, socio-economic status, parental work sector) are associated with an applicant’s odds of admission into selective HE programs?
3. What are, from an intersectional perspective, the acceptance rates of applicants with all, some or none of the background characteristics positively associated with odds of admission?

Study design, setting and eligibility criteria, study size

We conducted a retrospective multi-cohort study using anonymized non-public microdata from Statistics Netherlands, focusing on applicants and students in academic years 2019–2020 and 2020–2021. These years were chosen, as they were the two most recent years for which essential variables were available in Statistics Netherlands microdata at the start of the research. Statistics Netherlands does not allow for analysis on single-institution data. Therefore, we created several clusters of studies which each consisted of at least three institutions, to make data non-traceable to any particular institution. In cases where different studies had to be combined to achieve this, clusters were made on the basis of similarity of programs (e.g., related to personal healthcare or not). Table 1 contains information on the study programs in all HBO clusters (starting with ‘SH’) and all university clusters (starting with ‘SU’).

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Table 1. Information on clusters of study programs.

https://doi.org/10.1371/journal.pone.0292805.t001

The six HBO clusters included 19 HBO study programs, the nine university clusters included 28 study programs. We only included study programs taught at HEIs which are publicly funded and which are members of either the Netherlands Association of Universities of Applied Sciences (Vereniging Hogescholen) or Universities of the Netherlands (Universiteiten van Nederland), as these are the organizations which granted us permission to perform data analysis on their anonymized applicant pool and student population. It is important to note that many selective programs (e.g. Nursing) are only selective at one or a few HEIs, but that students can enroll in these programs at other HEIs without having to go through a selection procedure. Therefore, S1 Table also contains information on the number of HEIs that did not use selection for the particular study programs. Since this research focuses on inequality of opportunity in selection, the student populations of non-selective programs are not included in our analyses. We only used the term ‘selective program’ for programs which have a capacity limit and use selection to decide which applicants to admit for the limited places available. We created the following groups:

Applicants for selective programs (N = 85,839).

This group consists of all domestic applicants who applied through Studielink (the national online application portal for post-secondary education in The Netherlands) for at least one selective program, and who received a ranking number. The group is divided into HBO applicants and university applicants. Applicants who never received a ranking number (e.g., because they decided to withdraw from the selection procedure, or were not eligible) were not included, since the selection committee was not able to offer them a place (see Fig 1).

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Fig 1. Study size and exclusion criteria.

https://doi.org/10.1371/journal.pone.0292805.g001

Data sources

Studielink provided all applicant data of all selective HE programs from 2019 and 2020 to Statistics Netherlands, which pseudonymized the Citizen Service Numbers (CSN) of domestic applicants. The researchers used pseudonymized CSN to merge other Statistics Netherlands microdata to the dataset. International applicants were excluded from this research.

Variables.

The study includes the following applicant characteristics: sex, year of birth, migration background, country of birth, and degree of urbanity of postal code of address in 2015. It also includes the following applicants’ parental data: Income percentile of parent with highest income; household assets percentile; number of parents who receive social welfare; number of parents who receive a social services income (excl. social welfare); number of parents who are registered healthcare professionals (HP) according to the official Dutch BIG-register; and the number of parents who work in the primary, secondary or vocational education sector. Details on these variables are described in Table 2. The Studielink dataset also contained information on whether an applicant had received an offer of admission, whether this offer was accepted, and whether an applicant had enrolled in the program by October 1^st in the year of application.

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Table 2. Demographic data recorded for each applicant and their parents.

https://doi.org/10.1371/journal.pone.0292805.t002

Representativeness

S3 Table summarizes the demographic data of all applicants who received a ranking number, all applicants who were offered a seat in the program, and all those who became registered students, for each cluster. With these results, we can answer research question 1, on how representative student populations of selective HE programs are, compared to: a) the applicant pool; b) the higher general population and pre-university population; and c) their age cohort. Due to the extensive amount of descriptive statistics, we focus here on the variables with the largest differences between categories.

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 background’ 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 background’, 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 regression 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 Artificial Intelligence programs; 2 Industrial Design programs), none of the individual variables were significantly associated with the odds of admission. All other study clusters showed significantly 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, secondary or vocational education sector (6 out of 30 clusters).

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 opportunity was widespread. SU6_2020 remained the only cluster that did not show significant differences 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 contributes 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 background. The multivariable models show that inequality based on migration background cannot 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 background 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 admission (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 consistently 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 that applicants who were female, were born in 2001 or earlier, had parents in the income category 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 indicates that applicants in group ALL were offered a place more than 5 times as often as applicants in group NONE.

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Table 3. Intersectional analysis of the inequality of opportunity in Dutch higher education selection procedures: Comparison between group ALL^* and group NONE^**.

https://doi.org/10.1371/journal.pone.0292805.t003

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 significant variables in the multivariable models are HBO clusters, resulting in the largest difference 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: migration 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 background 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 acceptance 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 (intersectional) groups, which diverge from the average acceptance rates as included in Table 1.

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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

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; Industrial 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).

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 primary to secondary school), resulting in non-representative higher general and pre-university student populations. Lottery-based admission will therefore still result in an overrepresentation 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 programs 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 underrepresented 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) representativeness 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 selection procedure.

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 applicants (namely in SU3, SU7, SU8 and SU9). It is possible that in these cases, applicants were e.g. 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 considered 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 similar 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 student 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 disadvantage 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.