Love your staff and they’ll love you back. Staffing profiles may have remained stable over recent years, but this doesn’t mean things have just been ticking over in the same old ways. The recently published 2016/17 HESA Staff record provides details of staff employment at UK Higher Education providers as at 1 December 2016.
Analysis points at a stable number of academic staff in recent years, suggesting little change in resource allocation in Higher Education. However, digging deeper with our own dataset analysis we uncover some more interesting findings that suggest it might be time to show certain staff a little more love. It also has a bearing on how universities might view future staff resource allocations.
Staff digging deep?
Consulting our own HE financial benchmarking data, developed over 20 years, we would initially support this stability of staff numbers analysis. Academic delivery pay costs have remained stable over the last five years at 32% of income. However, deeper analysis shows that over the last five years this stability in academic delivery pay costs is the product of two factors: average pay levels per Full-Time Employee (FTE) rising by 3%; but being offset by a 4% fall in staff numbers relative to growth in core income. Therefore, and this will come as no surprise for those working in Higher Education, the workforce is digging deep and becoming more productive.
"It might be time to show certain staff a little more love"
Academic Support Staff reaping the rewards?
It might be a fairly obvious conclusion to make, but where are our HE providers reaping such productive gains? Our data also suggests academic support pay costs (academic management, technicians, placement co-ordinators, academic dept administrators) have remained fairly stable at 9% of core income. However; their average pay levels per FTE have risen rising by 7% over that same five year period, whereas their staff numbers fell by 8% relative to growth in core income. Academic Support Staff certainly appear to be increasing their output with wages reflecting their contribution to HE productivity.
Which departments are seeing investment?
And what of departments? If staff, on average, are being more productive how does this affect spend by department? Again, looking at the same five year period, on a cost per output basis (eg per FTE student, per staff FTE etc) Library, Registry, HR and Marketing costs have all remained fairly stable; IT and Student Services costs have increased by 25% and 23% respectively; whereas Facilities costs have fallen by 26%. Interpretation of this data may well differ between different universities and professions, but one could draw the conclusion that HE is moving towards a more technology-enabled approach for Student Services, which in turn is impacting the cost base of Facilities Departments.
"Academic Support Staff certainly appear to be increasing their output, with wages reflecting their contribution to HE productivity."
What does this mean for Heads of Planning, Strategy and Resources?
Given this further analysis, three questions university leaders and planners might want to ask themselves are: are they investing in the right staff in the right departments to remain competitive; how does their resource allocation support their student outcomes strategy; and do they have the necessary data and analysis to inform those strategies?
How do universities get hold of the wider data and analysis?
This commentary is simply a snapshot in response to the recent publication of the HESA Staff Record report; we share the full range of results with our Benchmarking customers, who, due to our methodology, are able to compare their institution with institutions of differing sizes on a reliable basis and using a host of benchmarks as they see fit.
When it comes to HE analysis, we’re pretty thorough to say the least - our standard analysis is a full academic year's worth of income, pay cost and non-pay cost, covering over 750 measurements, and over 13 functional areas.
See how universities use the full power of our Benchmarking analysis to inform budget and planning strategy, optimise resource allocation and run sophisticated income generation models