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Students mismatching with courses affects their future life chances

New research shows the scale of academic "mismatch" between students and their courses - and how mismatching could be holding disadvantaged students back. Gill Wyness and Lindsey Macmillan explain
This article is more than 4 years old

Gill Wyness is an Associate Professor of Economics, and Deputy Director of the Centre for Education Policy and Equalising Opportunities at the UCL Institute of Education.

Lindsey Macmillan is Professor of Economics and Director of the Centre for Education Policy and Equalising Opportunities at UCL Institute of Education.

Higher education has long been thought of as a tool to equalise opportunities, with governments around the world spend 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 and 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 it is that 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 (Sanders et al., 2018).

4 responses to “Students mismatching with courses affects their future life chances

  1. On the gender gap for courses thing, is the causation that way round?

    You seem to be suggesting that History is a less well-earning field than Engineering, so more women should be encouraged into Engineering so that they earn more (and presumably men into History so that they earn less but keep the overall number of History graduates at a suitable level)

    Alternatively it could be because in almost all fields women are paid less than men, History is a less well-earning field than Engineering precisely because more women study it, and if they were studied equally they would be paid more equally. In this scenario if more women studied Engineering and more men studied History, then the result over time would be that Engineering graduates were paid less and History graduates were paid more. (A scenario seen in practice over the last 50 years with computing as a profession, for example)

    The test would perhaps be to compare Engineering and History cohorts at different institutions and see if the institutions with narrower gender gaps in the student body also show narrower pay gaps later on relative to the subject average.

  2. 100% agree with cim. There are some pretty significant assumptions baked into the conclusions which may reflect the views of the researchers… one of which being that women “should” be encouraged to take subjects with higher earnings potential. So are we now accepting the toxic viewpoint that value in higher education is derived purely from income? How very depressing (and, surely, wrong)

  3. Aside from all the assumptions about ‘quality’ and selectivity in here that I don’t much like…

    I’m interested in what more the authors have to say on this point “There may therefore be scope for some outreach work for high ranking universities to attract local disadvantaged students.” Presumably they are aware that ‘high ranking universities’ already carry out extensive outreach work with local disadvantaged students, and have done so for many years. Are there any lessons for why this does not seem to have been effective in sorting out the ‘mismatch’ issue.

    Did the authors look at the issue of predicted grades? Could it be that students from low SES backgrounds are less likely to be predicted the high grades and so may not be aware at the point of application that there is even a ‘mismatch’? Does this study support calls for PQA?

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