The IOE’s new Centre for Education Policy and Equalising Opportunities (CEPEO) launches its website today, along with a new blog. Here, Gill Wyness. the centre’s deputy director, shares its first post
Getting more students into higher education (HE) is an important element of governments’ strategies for increasing human capital. Consequently, much academic research has been devoted to examining policies that aim to encourage students into university, particularly those from disadvantaged backgrounds.
But less attention has been given to the types of universities students enrol in. Given the high returns for those who attend selective universities and subjects, understanding whether students from disadvantaged backgrounds enrol in less selective courses, which are likely to have lower returns, is important for equalising opportunities.
In a recent research project, colleagues Lindsey Macmillan and Stuart Campbell of UCL Institute of Education and Richard Murphy (University of Texas at Austin) and I examine this question, asking to what extent students are mismatched to their courses, and what are the drivers of mismatch.
We find a significant amount of mismatch in the English HE system, with around 16-25% of students under or overmatching. We also find stark SES (socio-economic status) and gender differences in the courses that students attend. Those from disadvantaged backgrounds are more likely to enrol in courses that are less selective than their high SES counterparts, even when they have similar A-level grades. And women are more likely to enrol in courses that have lower returns in the labour market than similarly qualified men.
Our research exploits linked administrative data, tracking students from school into higher education for the cohort who attended university in 2008. We look at two types of mismatch: undermatch, where students attend courses that are less selective than might be expected based on their grades, and overmatch: where students attend courses that are more selective than might be expected based on their grades.
And we examine mismatch along two dimensions: attainment-based match, where we effectively test the extent to which students choose courses that might be expected given their academic attainment up to that point, and earnings-based match, where we effectively test the extent to which students choose courses that are likely to generate earnings in line with their academic attainment.
We find a significant amount of mismatch in the English system, with around 16-25% of students under or overmatching. We find that students from low SES backgrounds are more likely to undermatch than those from rich backgrounds. At every point on the attainment distribution they attend less academically prestigious courses, and courses with lower earnings potential, than those from high SES backgrounds. This has important implications for equity, and for equalising opportunities.
We also find that female students attend courses that are just as academically selective as male students. But across the distribution of ability, female students attend courses which are lower-earning than men. Again, this has important implications for equity and for the gender pay gap, which is an important policy area.
So what should policymakers do? We examine three important factors which might drive this mismatch in an attempt to work out potential policy solutions – degree subject choice, geographical factors, and school factors.
Accounting for the subject studied does not reduce the socio-economic gap in match. In other words, when students are of similar attainment and studying the same subject, low SES students study it at a lower quality institution. Thus, we can conclude that a key driver of SES inequalities in match is the choice of institution attended.
However, the subject choice does account for most of the gender gap in earnings match; the fact that women attend courses with lower earnings than men is largely driven by the subjects that women choose, rather than the institution. 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. This implies that it may be important to educate women in particular about the economic returns associated with different subjects.
We find little evidence that location is a driving force in mismatch. High attaining, low SES students are much more likely to attend universities close to home, but among all students living close to home, an SES gap remains. High attaining low SES students going to universities near home tend to choose a post-1992 institution, whereas high attaining high SES students staying near home tend to choose a nearby Russell Group university. Interestingly, however, those low SES students who move away to attend university face no match penalty, suggesting they are better informed or more motivated students.
The one factor that does seem to explain the SES gap in match is the school attended. We find that school-related factors account for the majority of mismatch among low SES students. This implies that factors correlated with high school such as peers, school resources, and sorting play an important role in student match.
Bearing all this in mind, one obvious policy solution would be to improve the level and quality of information available to undermatched students. For example, students could be provided with information on the entry requirements and labour market returns to different courses at key decision-making ages (Belfield et al, 2018). However, simply offering information (e.g. on the different returns associated with different institutions) may not be enough to resolve these issues. Studies have shown that those who gain the most from this type of information may be the least likely to consume it, and to be effective information has to be carefully targeted (McNally, 2016).
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, Sanders et al., 2017). 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. Regardless, providing information, advice and guidance, in a targeted way that tries to break down existing barriers in terms of both understanding and perceptions, can only result in more informed choices.