The first iteration of any new dataset is both more interesting and less useful than any subsequent release.
It is less useful because we have no way to put the findings in context, and no way to make any judgement about trends and directions of travel. As all survey instruments generate flawed reflections of reality and Graduate Outcomes (never, ever “GO”!), we need multiple iterations to understand what the response rate is telling us. So the headline figures – that 60 per cent of UK graduates were in full time employment (87 per cent in full-time employment or further study), or that there is an eight percentage point gap between full-time employment by ethnicity – lack context.
As with most good large-scale surveys – this being the largest annual social survey in the UK – graduate outcomes questions have been cognitively tested so we can be fairly confident that the responses are reliable in themselves.
And it is important to remember that, even though the coverage of this survey is fairly huge, it is just a survey. We don’t know – and shouldn’t pretend to know – about “all graduates”. The response rate for the UK is 52 per cent (55 per cent if you include partial responses), dropping to 47 per cent (54 per cent) for all graduates. And this is a population survey (all possible responders were contacted), where a decision has been made not to weight, for this year at least.
New data, new concepts
Talking to graduates about their experiences 15 months after they graduate from their first degree has a very interesting side-effect. It is entirely possible to squeeze a Masters, or other one-year postgraduate course, in during that time – and another qualification (or another year out of the workforce) could have an impact on employment opportunities. The key term is “significant interim study” – by default the presentation of much of this data excludes graduates with this experience.
We’re also introduced to the concept of work type – the nature of the contractual (or other) basis of employment. The default filter separates out paid employment from voluntary work – but there is more detail available. As well as open ended contracts (the dominant mode), we see short and long fixed-term contracts, temping as a separate category, and also things like zero hours, internships, and volunteering.
For data about graduates are working we can flip between seeing those who defined work as an activity, or their most important activity. This is one to be applied cautiously – the concept of importance is one laden with meanings and needs to be seen in the context of the data you are looking at.
SICs and SOCs and salaries
The Standard Industrial Classification (SIC) and Standard Occupational Classification (SOC) will be familiar from the bad old days of DLHE (the Destination of Leavers from Higher Education survey, which Graduate Outcomes replaces). The former defines the industry (what the employer of a graduate does), the latter the skill level based on comparing a job title against a standard list.
You’ll also recall the fun we had understanding precisely which job titles are “graduate jobs” (usually mid- or high-skilled in the new HESA analysis) using SOCs. Suffice it to say that the venerable SOC is at least arguable on many points – much of the initial concerns about this release concerned the way graduate responses were coded to SOCs.
This is where policy happens. Don’t look at ministerial statements, look at data definitions.
On this basis, the slant towards “high skilled” occupations in science students as compared to arts and humanities students (at 61 per cent, creative arts students are least likely to be in high skilled occupations, but more likely than most to be in a “skilled trade” occupation. HESA should be applauded for offering us the full list of classifications rather than the three categories.
Likewise, the slight majority of creative arts graduates in retail occupations needs more qualification, both in terms of the likelihood of artists to work multiple jobs and in the way these jobs are coded in SIC. But it is also worth noting that the difference between the proportion of creative arts graduates in full time employment and a similar proportion of biological science graduates is a single percentage point.
We also get salary data (based on graduate reports rather than tax receipts as in LEO), in £3,000 wide bands. Women are disproportionately represented at the lower end of this scale, men at the top, putting paid (hopefully) to the persistent myth that the gender pay gap is due entirely to career breaks. Wonderfully, LEO data for the same year is available, offering us the chance to compare reporting against reality.
Eighty-six percent of surveyed graduates reported that they felt their current activity was meaningful (be that work, further study or other activity). Eighty percent reported that their current activity matched their future plans – which could be seen as an indication of careers being started – and seventy two percent reported that they were utilising what they learnt in their studies in their current activity.
Sadly, we don’t get these responses split by subject – data that would be genuinely fascinating to compare against media shibboleths about arts students in particular. The construction of meaning within post-graduation activity is a hugely complex sociological question, these questions offer us merely a glimpse into a topic probably better addressed with qualitative methods.
There are a few points where I found myself looking for numerical information where only proportions were provided. I don’t necessarily need to know how many graduates reported a particular outcome, more to get a sense of where I might be comparing a larger group with a much smaller one.
And there are a few presentational choices that prompt questions – I struggled greatly with figure 9 on understanding the impact of classifications – hopefully the attempt below is clearer.
But this – global pandemic and data-distorting economic slowdown notwithstanding – looks like developing into a very interesting dataset. It already feels more robust than DLHE, and the choice of questions and categories are well designed.