Abstract
In this paper, we consider whether certain countries are particularly adept (or particularly poor) at getting children from disadvantaged homes to study for a bachelor’s degree. A series of university access models are estimated for four English-speaking countries (England, Canada, Australia and the USA), which include controls for comparable measures of academic achievement at age 15. Our results suggest that socioeconomic differences in university access are more pronounced in England and Canada than Australia and the USA and that cross-national variation in the socioeconomic gap remains even once we take account of differences in academic achievement. We discuss the implications of our findings for the creation of more socially mobile societies.
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Notes
Note that figures for England refer to the period before the 2012 reforms—with tuition fees now substantially higher.
Huber-White adjustments or school fixed effects are used to account for clustering.
Of course one might argue that students will apply effort differentially even before age 16 depending on whether they intend to go to university. We cannot overcome this problem.
Of course, PISA scores also have limitations, including less than perfect reliability, as discussed by Jerrim (2013).
Substantive findings remain intact if (observed) Key Stage 3 scores are used in place of (estimated) PISA scores. As PISA scores for England are estimated, we have investigated the sensitivity of the estimated standard errors using (1) analytic methods; (2) bootstrapping (3) observed key stage 3 test scores in place of the PISA estimates, and find little change to our results.
Canada, Australia and the USA include state and private school pupils.
Specifically, we estimate test scores for private school children via imputation. The high SES parameter estimates increase by approximately 0.10 standard deviations.
Some two-year college students may complete a 4-year degree, though upgrade rates remain low (Long and Kurlaender 2009). Exclusion of these students means we may be slightly understating low SES HE participation rates in the USA (as this group is the most likely to enrol in an associate degree).
We have experimented with models including controls for respondents’ month and year of birth and found very little change to the results presented (and substantive conclusions drawn). Similarly, we have also re-estimated models including controls for family structure and number of siblings. In the baseline (unconditional) estimates, this reduces the SES gap in university access by about 10 percent in England and the USA, with little change in Australia (data not available for Canada).
PISA scores at age 15 are included, but school fixed effects are removed.
Chowdry et al. use an income-based measure that is an amalgam of pupil’s eligibility for Free School Meals (which in turn is linked to their family being in receipt of different types of welfare) and the affluence of the neighbourhood in which the child lives.
This is under the assumption that parental education is a better measure of family background than those used in the study by Chowdry et al.
The low–middle parental education gap for the USA just reaches significance at the 10 percent threshold in Panel B.
These estimates are still conditional upon university participation.
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Acknowledgments
Jerrim has been partly funded by the British Academy and the ESRC. He also wishes to acknowledge the invaluable support of the PATHWAYS postdoctoral programme. Vignoles time has been partly funded by the Nuffield Foundation. Helpful comments have been received from participants at the Sutton Trust social mobility summit, the 2012 IWAAE conference, 2012 LLAKES conference and seminars at the Institute of Education and Michigan State University. Particular thanks go to Stephen Childs for research assistance with the YITS data and to Barbara Schneider for facilitating Jerrim’s visit to MSU.
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Appendices
Appendix 1
See Tables 4.
Appendix 2
See Tables 5
Appendix 3: Socioeconomic differences in access to ‘selective’ universities
Selective institutions are a route to prestigious occupations and high earnings, but defining this group is not a trivial task. We take a pragmatic approach and use the following pre-defined categories:
England = ‘Russell Group’ (www.russellgroup.ac.uk/our-universities.aspx).
Australia = ‘Group of Eight’ (www.go8.edu.au/go8-members/go8-member-profiles).
Canada = ‘U15’ (://rd-review.ca/eic/site/033.nsf/vwapj/sub198.pdf/$file/sub198.pdf).
USA = ‘Highly selective’ (Carnegie classification).
In England, Australia and Canada, these are self-selected alliances of research intensive institutions, whilst the USA categorisation is based upon SAT scores of entrants. Around one in eight young people attend a selective university using these definitions (16 % Canada, 13 % USA, 12 % Australia and 10 % England) with substantive conclusions largely unchanged when we have experimented with alternatives (e.g. those based upon entrants test scores). We note that there is some debate as to whether simple categorisations such as the Russell Group are indeed a valid and reliable indicator of university ‘quality’. Nevertheless, we proceed using such groupings in our analysis for consistency and comparability with the existing literature (e.g. Chowdry et al. 2013; Anders 2012; Boliver 2013).
Append Fig. 3 Panel A presents estimates where all young people are included in the sample, and gender and immigrant status are the only controls. We find a strong association between parental education and access to selective institutions in all four countries, with a particularly big difference between the middle and high parental education groups. As qualifications from selective universities are thought to offer higher economic rewards than a ‘typical’ bachelor’s degree (Chevalier and Conlon Chevalier and Conlon 2003, Hoekstra Hoekstra 2009), it is a concern that young people with poorly educated parents are under-represented in such institutions.
The socio-economic gap in entry to a selective higher education institution. Notes figures for England refer to state school pupils only. The light grey segment of the bars illustrates the difference between ISCED 0–2 and ISCED 3–5B groups. Dark grey segments refer to the difference between ISCED 3–5B and ISCED 5A/6 groups. Thin black lines running through the centre are the estimated 90 % confidence intervals. Estimates in Panel A are based upon the full sample and include only basic controls (gender and language spoken at home). In panel B, the data sets have been restricted to university graduates only. PISA test scores are then controlled for in panel C, with achievement scores at age 18 also included in panel D. a Raw socioeconomic gap, b conditional upon university entry (basic controls), c PISA test scores, d school grades
Appendix Fig. 3 panels B–D consider the parental education– selective university gap amongst young people who enrol in higher education (i.e. estimates are conditional upon university attendance). The difference between the low and middle parental education groups is now small and statistically insignificant in England, Canada and Australia; conditional upon going to university, children from low-educated households are just as likely to enter a selective institution as a young person from an average background. This gap is larger, and statistically different from 0 at the 5 % level, in the USA (0.56 log-odds or 7 % points). Yet confidence intervals are wide, partly due to relatively few children from low parental education backgrounds remaining in the sample now it includes university attendees only. This suggests that the major issue facing the low parental education group is access to university in general and not specifically about admission to selective institutions. Indeed, the low–middle parental education gap does not reach statistical significance in almost all remaining model specifications for any country (panels B to D).Footnote 13
The middle–high parental education gap in Appendix Fig. 3 Panel B is significantly larger in England (1.07 log-odds) and the USA (1.0 log-odd) than in Australia (0.74 log-odds) and Canada (0.62 log-odds) at the 10 % threshold. Nevertheless, in all four countries, the middle–high parental education gap is substantial (15 % points in Australia and Canada and 20 % points in England and the USA). Hence, not only are children with highly educated parents more likely to go to university, but they are also more likely enter a selective institution conditional upon their higher rate of attendance.
Is this parental education gap in selective university access simply a reflection of differences in cognitive ability and school grades? Appendix Fig. 3 Panel C (PISA controls) and Panel D (PISA controls plus age 18 school grades) suggest this is only part of the explanation.Footnote 14 For instance, in the USA, estimates decline from 1.0 (panel B) to 0.74 in Panel C (PISA test scores controlled) and to 0.61 in Panel D (age 18 grades controlled). A similar pattern occurs in England and Australia. But a non-trivial difference between young people from high parental education backgrounds and the other two groups remains. In the previous section, we demonstrated how children from high parental education backgrounds in England are 6 % points more likely to enter university than their peers from ‘average’ parental education backgrounds, even once school achievement measures were controlled. Appendix Fig. 3 Panel D illustrates that, conditional upon this already greater likelihood of going to university, children from high parental education backgrounds are a further 8 % points more likely to attend a selective institution (having controlled for academic achievement). Moreover, in additional analysis available upon request, we continue to find the link between parental education and selective university entry remains, even after conditioning upon family income, high school graduation, school grades and multiple cognitive test scores.
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Jerrim, J., Vignoles, A. University access for disadvantaged children: a comparison across countries. High Educ 70, 903–921 (2015). https://doi.org/10.1007/s10734-015-9878-6
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DOI: https://doi.org/10.1007/s10734-015-9878-6