Low-value contracts, and what they tell us about high-value data

If we are to get serious about improving pay in universities, Sophie Crouchman suggests we start by getting better at collecting and sharing data

Sophie Crouchman is Strategic Projects and Research Manager for Universities Human Resources (UHR).

What is working in higher education worth? David Kernohan’s recent analysis seems to suggest that in some cases, not very much at all.

HESA data reveals that small numbers of academic staff are employed on contracts at or below the government’s National Living Wage.

How can this be? Much has been written about academic salaries recently, and the aim of this article isn’t to unpick the arguments around whether academic staff are paid well, or fairly. But when the data suggests that there are academic roles in our HE institutions which pay so little, it would be remiss to ignore these facts. As someone who likes nothing more than delving into the fine detail of the HESA staff record, I’d like to offer some thoughts on the conclusions we can draw from David’s analysis.

Only teaching?

My first thought was that it’s telling that the vast majority of low paid contracts are teaching only – suggesting to me that these are most likely to be junior teaching positions, possibly on fixed term contracts. We know that many universities chose quickly to employ “graduate teaching assistants” or equivalent positions at a scale not seen previously, due to the massive volatility in undergraduate admissions in the last few years.

Bumper student recruitment and Covid restrictions between 2020 and 2022 when many HEIs enrolled more students than they forecast led to a scramble rapidly to increase staff teaching capacity. I suspect some of the recruits during that period were recent post-graduates who found themselves responsible for running lab classes, tutorials or workshops, in duplicate, triplicate or more to meet demand. It isn’t possible from the data to be certain of this, because HESA does not discriminate between different types of teaching contracts, and it’s not possible to view the data at a more granular level.

ACEMPFUN for all the family

My love of HESA fields is something of a standing joke at UHR. If you ever meet me at an event I’m sure to bring it up in my “what’s your favourite HESA field” chat opener (yes I am the life and soul of the party) and I am delighted to have met a couple of other ACEMPFUN fans recently. ACEMPFUN stands for Academic Employment Function and it is used to determine whether academic staff are employed on a contract which is teaching-only, research-only, research and teaching or neither teaching nor research (the latter is rarely used).

This field is crucial for many reasons, not least because it allows us to determine what different groups of academic staff look like in terms of demographics. It provides a richness to the HESA staff data which is hugely helpful in analysis, though the dataset overall still has its limitations – which I’ll come back to later. Identifying what “type” of academic staff are most likely to be employed on low-value contracts is only part of the story, knowing what work they’re actually doing is something only the employing HEIs can adequately describe.

I am truly passionate about the value of the HESA staff dataset to the sector as a whole and to individual institutions. At UHR we know from talking to our members – HR professionals in HEIs right across the UK – that the submission of the HESA staff record every autumn is a significant task. HR teams work hard with colleagues in strategic planning and finance to ensure that the data they return is accurate and reflects the work that is carried out in their institutions. Some universities are making strides in improving the efficiency of their data collection and at UHR we’re sharing this good practice with our members, but what comes after the return is submitted? When the virtual button is pressed and the spreadsheets go quiet?

Up to the benchmark

In an ideal world, the analysis would begin – an HEI’s individual return is a rich source of data and the only source that can be benchmarked with the whole sector when HESA publishes its open data and update Heidi Plus. Having invested so much time ensuring the return is correct, it would be remiss for institutions not to review the information it reveals about their workforce – how many of your staff declare a disability; what is their nationality; what qualifications do they have; where were they employed before they were recruited, and so on. Increasing organisational understanding of the workforce is essential to progress the renewed sector focus and drive on diversity and inclusion, and to address the recruitment difficulties that HEIs are facing – particularly to professional services posts.

HESA has just released a summary of the responses to its recent consultation on their major review of the staff dataset. Currently, the return of data on “non-academic” staff (i.e. those employed in HE professional services) is not compulsory for English HEIs which means that sector benchmarking of these staff is hindered by data gaps. This non-academic staff group makes up almost half of the HE sector workforce. The majority of respondents to the consultation would support the reintroduction of non-academic staff data as a mandatory element of the staff return. I sincerely hope that HESA acts on this feedback and take further steps to make the staff dataset even richer and more valuable.

At UHR we can see first-hand how HR teams are increasingly using people analytics to drive decision-making. One of my roles is actively to support our members in this space through sharing good practice, connecting members and providing resources and tailored CPD. Spreading the word about the insight that HESA data can provide, as well as its shortcomings, is a crucial part of this.

One response to “Low-value contracts, and what they tell us about high-value data

  1. The comment about non-academic staff is interesting as my University has a significant contingent of staff who are professional practitioners working on contract work for the University. They do all the same work as an academic apart from teaching. Their research is contract research not ref returnable. They ensure our work is delivered on time or to the quality required which are both difficult for academics who are pulled in different directions by a large range of masters. Further we have a set of professional project managers who manage large scale projects. Plus we have the more standard professional services. So just lumping all non-academics into one bucket does not seem the right way of deliver high quality analytics.

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