When students’ attainment is mismatched with their university course, life chances are affected
By Blog Editor, IOE Digital, on 9 December 2019
Higher education has long been thought of as a tool to equalise opportunities, with governments around the world spending billions per year on encouraging disadvantaged students into university through financial aid and other widening participation strategies.
Indeed, the Office for Students has recently set ambitious new targets to encourage universities to widen access. But is simply getting poor students into university enough? Our research, funded by the Nuffield Foundation, suggests that we need to pay much more attention to the types of universities and subjects that disadvantaged students enrol in, if we really want to improve their life chances.
Searching for the perfect match
We, along with our UCL IOE colleague Stuart Campbell, and Richard Murphy from the University of Texas at Austin, examine the quality match between students and the courses they attend, using data on a cohort of students who left school and enrolled in university in 2008.
We are interested in whether certain groups (for example, disadvantaged students) are more likely to “under-match”, by attending courses that are less selective than might be expected given their A level grades. We also examine whether certain types of students “over-match” – in other words, attend courses that are more selective than might be expected given their grades.
We examine this phenomenon of mismatch along two dimensions of course “quality”. First, we considered a student to be well-matched to their course if they have similar A level scores to others on the course (attainment match). For example, a high-attaining student would be well-matched if they attended a course with equally high attaining students.
They would be under-matched if they attended a course where their fellow students have lower grades than they do (suggesting they could have attended a more academically prestigious course), and over-matched if they attend a course where the other students on their course have higher grades than they do.
Second, we ranked courses based on the average earnings of their graduates five years later, and considered a student to be well-matched if that course had a similar ranking to their own individual ranking by attainment (earnings match). For example, a high-attaining student would be well-matched if they attended a course with high earnings potential, and would be under-matched if they were high attaining, but their course had low average earnings.
We found a significant amount of mismatch in the English system, with around 15-23 per cent of students under-matching and a similar proportion over-matching. Importantly, we find that students from low socio-economic status (SES) backgrounds are more likely to under-match than those from rich backgrounds. Comparing low and high SES students at every level of attainment, disadvantaged students attend less academically prestigious courses, and courses with lower earnings potential, than those from high SES backgrounds. So these students have the same A level attainment, but are attending lower “quality” courses. This has obvious implications for equity, and for equalising opportunities.
But economic disadvantage is not the only dimension of inequality we study. Examining mismatch by gender, we found that female students attend courses that are just as academically selective as male students (attainment match), but they attend courses which have lower future average earnings than men, comparing students with the same A level attainment. This has important implications for equity and for the gender pay gap.
What’s driving the mismatch?
So what should policy makers do? We examined three important factors which might drive this mismatch in an attempt to work out potential policy solutions. First, we considered the choice of subject studied at degree level, comparing students of similar academic attainment and studying the same degree subject, the gap between advantaged and disadvantaged students remains. This tells us that low SES students are studying at lower “quality” institutions relative to high SES students, rather than choosing lower “quality” subjects for their courses.
What about the role of geography? It is well known that low SES students are more likely to attend universities close to home, but does this drive them to choose a less selective institution? If we just consider the group of students living close to home, we still see differences in the institutions that disadvantaged students attend compared to more advantaged students.
High attaining low SES students tend to enrol in post 1992 institutions near home, whereas high attaining high SES students are more likely to attend a nearby Russell Group university. There may therefore be scope for some outreach work for high ranking universities to attract local disadvantaged students. Interestingly, those low SES students who move further away from home to attend university appear to be as well-matched as similar attaining high SES students.
Our third factor of interest is school attended, which we found accounts for the majority of mismatch among low SES students. The implication is that factors correlated with high school such as peers, school resources, information, advice and guidance at school, and sorting into different types of schools, play an important role in student match. Unpicking what is driving this important schools channel is an important step for future research.
Turning to our gender gap in earnings mismatch, we find no role for distance to university or schools attended. But we find a very important role for degree subject studied. The fact that women attend courses with lower future average earnings than men is largely driven by the subjects that women are studying, rather than the institutions they attend. For example a high attaining male student might choose a subject such as engineering, which is typically high returns, whereas a high attaining female student might choose a subject such as English or History, commanding a lower average salary.
So what can we do?
The evidence suggests that an intervention that may help to reduce SES and gender gaps in match would be to improve the level and quality of information available to under-matched students, for example on the attainment profile of students on each course, and labour market returns.
Some recent studies have investigated the importance of providing information to low SES students specifically to improve match (Dynarski et al, 2018, Sanders et al., 2018). Our results highlight that it may also be beneficial to target women in a similar way, providing information on potential earnings associated with both institution and field of study.
However, as with most studies of mismatch, we have no information on the preferences of students. Women may be well-informed on the earnings potential of subjects, but simply prefer not to study them. Similarly, it may be the case that low SES students prefer to attend less academically challenging institutions even when their attainment levels suggest they are academically prepared. This could be down to perceptions about institutions not being a good fit for them. Our finding on geography suggests that university widening participation units could do some important outreach work in these cases to challenge perceptions.
This blog is re-posted from WonkHE.