Regional variation in the design and nature of graduate work: a first look
When we released the methodology underlying our new statistical measure of the design and nature of work in June, we invited feedback from data users concerning the value of the new measure and how the measure might best be used in HESA data publications. The responses to the consultation were broadly positive, but included a few further questions about the construction of our measure as well as suggestions for how the measure could be used to enhance understanding of graduate employment. This insight brief is an opportunity for us to present some high-level preliminary data based on geographical analysis of the new measure – an area which our users identified as being of particular interest – and to outline how we plan to expand on this analysis in future work. Before we can proceed to our discussion of the data, however, we must first revisit our methodology in order to respond to some of the questions raised by our users.
Developing a composite measure
The graduate voice questions upon which our measure is based are a new set of questions designed for the Graduate Outcomes survey, which were not included in the predecessor surveys to Graduate Outcomes. The questions were designed with input from the sector, in response to our users’ desire for measures of graduate success which take into account graduates’ own views of their experiences, but they have not, as yet, been widely used in analysis of Graduate Outcomes data. While consultation respondents reacted favourably to our decision to use the graduate voice questions, some also expressed a desire for clarification of the rationale for combining the three questions into a composite measure.
Our work with the graduate voice questions stems both from user interest in a range of different outcomes measures and from a UK-wide policy interest in employment quality. In all four nations of the UK, policy makers have made an effort to define the elements of fair work; although policy bodies define and measure fair work in slightly different ways, there is broad agreement that the quality of employment depends not on any single aspect of work, but on the interplay of a variety of different employment characteristics, including, but not limited to pay and benefits, terms of employment, work-life balance, and the nature of the work itself.
Although we refer to our composite measure for the sake of simplicity as a measure of fair work, the three graduate voice questions relate to just one of the dimensions of employment quality as defined by the Measuring Job Quality Working Group. The statistical analysis which we carried out in the initial stages of developing our measure confirmed that meaningful work, skills utilisation, and career progression all relate to the same underlying construct, although that construct is labelled differently by policy bodies across the UK. In Scotland, meaningful work, skills utilisation, and career progression all tie in with the ‘fulfilment’ aspect of good work; in Wales they fall under the category of ‘opportunity’; and the UK-wide Measuring Job Quality Working Group includes them as part of ‘job design and the nature of work’.
For statistics to be valuable, according to the Code of Practice for Statistics, they must not only be relevant to users, but they must also be communicated clearly. In their work on measuring and communicating the dimensions of employment quality, the Measuring Job Quality Working Group suggest that composite measures may help users navigate a complex, multi-dimensional topic like employment quality. Once we determined that the graduate voice questions related to the same dimension of employment quality as defined by the Measuring Job Quality Working Group, further analysis revealed that the three variables could be reduced to a single, composite measure. We believe that this composite measure will allow us to communicate to our users some of the stories about graduate employment contained in the graduate voice data.
The geography of fair work
In our consultation with data users, we asked what kinds of analysis based on the new measure our users would find most valuable. In addition to requests for analysis showing the fair work scores for groups with different demographic characteristics – by sex, age, ethnicity, disability status, etc. – there were a number of requests for geographical analysis of the data. Respondents were interested both in detailed geographical data and in analysis of the employment outcomes for graduates with different mobility characteristics.
Our users’ desire for geographical analysis taps into current policy concerns across the UK. In Scotland’s 2015 Economic Strategy, the promotion of fair work sits alongside the linked goals of maximising attainment for students from all backgrounds and ensuring that economic opportunities extend across the whole of Scotland. In England, recent years have seen a focus on ‘levelling up’, driven by the fact that outcomes can vary for residents of different areas, with residents of coastal towns, for example, facing different prospects than those which are available to residents of large metropolitan areas. In Northern Ireland, a concern surrounding skills utilisation in the workforce is linked to the proportion of Northern Irish graduates who leave the country for employment, while in Wales there is a similar concern that Welsh graduates frequently feel the need to leave Wales in search of good jobs.
A range of recent publications have looked at patterns in graduate mobility, including where graduates are most likely to move in search of employment, which graduates are most likely to move, and the costs and benefits of such moves. Many of these studies have looked specifically at the financial returns which accrue to graduates who move to take up employment, noting that graduates who move tend to earn higher salaries than those who work in the same region where they grew up. Some publications, however, focus on the benefits to regions of graduate retention, and note – in line with the recent focus on employment quality – that graduates may be motivated by reasons other than financial gain when they decide where to work. By using our new measure to explore the nature of work reported by graduates employed in different geographical areas, we hope to be able to contribute fruitfully to discussions of graduate mobility and the prospects for graduates in different parts of the UK.
We have now carried out some preliminary investigations into the fair work scores (calculated as the average of each graduate’s responses to the three graduate voice questions) of graduates working in different regions. Our sample consists of 281,240 graduates from 2017/18 and 2018/19 whose data was also collected in either our Student or Student Alternative records. The sample is restricted to graduates whose only activity recorded in the Graduate Outcomes survey was paid employment in the UK and who responded to all three graduate voice questions. We excluded those residing prior to entering higher education and/or subsequently working in Guernsey, Jersey or the Isle of Man, along with any individuals whose region of residence or employment we could not identify.
For each region, we have calculated scores for graduates working in that region, separated into those who were living in that region before entering higher education (‘stayers’), and those who lived elsewhere (‘movers’). For this initial analysis, we do not include the region in which graduates studied; our population of ‘stayers’ thus includes those who attended higher education away from their home region and then returned as well as those who remained in their home region for both study and work, and our population of ‘movers’ includes both those who left their home region to study and did not return and those who studied in their home region before moving. Although we hope in subsequent work to look at smaller geographical areas – and although I will explore some of the limitations of region-level data in my discussion – looking in the first instance at the English administrative regions plus the devolved nations allows us to explore the potential for our new measure to reveal interesting stories about graduate experiences of work.
Table 1: Movers and stayers by region of employment
|Region of employment||Stayers||Movers|
|Number||Mean fair work score||Number||Mean fair work score|
|Yorkshire and The Humber||14,575||4.01||6,075||4.04|
|East of England||13,075||3.95||6,295||4.11|
Figure 1: The proportion of movers and stayers by region of employment
Figure 2: Mean fair work score by region of employment and mobility status
Notes: Possible scores run from 1 to 5. The chart axis does not start at 1. 95% confidence intervals for Figure 2 have been generated using the Student’s t-distribution.
Looking at the data displayed above, we can make a few initial observations. Firstly, although stayers outnumber movers for all regions, the proportion of stayers to movers varies (Figure 1). To take the most extreme contrast, while there are only slightly more stayers in London than there are movers into London, almost 27 Northern Irish graduates stay in Northern Ireland to work for each graduate who moves to Northern Ireland to take up employment. Wales and Scotland both also have higher proportions of stayers to movers than we see in any of the English administrative regions, with just under four and just under nine stayers for each mover in Wales and Scotland, respectively.
Secondly, movers score higher than stayers by our new measure in every region but Northern Ireland, where stayers score slightly higher (Figure 2). The magnitude of the difference varies, however, as do the overall fair work scores for movers and stayers in each region. In Scotland, Wales, and Northern Ireland, differences between movers and stayers by our measure are relatively small and are not statistically significant. Stayers in Scotland, Wales, and Northern Ireland, moreover, have relatively high fair work scores when compared to stayers in many other regions. Stayers also score relatively highly in the North East and Yorkshire and the Humber, and the difference between movers and stayers in Yorkshire and the Humber is not statistically significant. Movers to the West Midlands, the East of England, London, and the South East, however, score much higher than stayers in those regions, with movers to the West Midlands having the highest fair work scores of any group. Overall, fair work is available across the UK, but moving to a new region in search of work, which may indicate a more active decision to work in a certain place, seems in most regions to be correlated with higher quality employment.
Much previous discussion of graduate mobility has focused on the pull of London. London is interesting by our measure; while movers to London score considerably – and statistically significantly – higher than stayers in London, who have the lowest mean fair work scores of the various groups we analyse, both movers and stayers employed in London have fair work scores which are below the averages for movers or stayers in many other regions. London is noted for particularly high graduate salaries, but our analysis suggests that the financial returns to working in London are not necessarily matched by the nature of the employment which graduates find in the capital.
Next steps for the fair work measure
The analysis contained in this insight brief is preliminary, based on high-level data covering relatively large geographical areas. What this preliminary analysis cannot show us is how many of our ‘stayers’ have in fact moved within their home region, nor can we see the effect of such moves on the design and nature of graduate employment. The regions used in our analysis all cover a variety of different localities, and graduate labour markets in these localities may vary. Thus, for example, a graduate originally from rural northern Scotland who ends up working in Edinburgh or Glasgow will appear in our analysis as a stayer, as will an English graduate from Blackpool who takes up a job in Manchester. Despite remaining within their home regions, these two hypothetical graduates will each have moved to work in a different area with different graduate opportunities.
As we continue to develop analyses based on our new measure, one important step will therefore be to incorporate more granular geographical data. In order to support more detailed geographical analysis – both of the design and nature of graduate work and of other graduate data – HESA has recently developed a derived location field which will enable us to look at location of employment at the county or unitary authority level. Further analysis of our new measure based on this more granular location field will allow us to consider the importance of intra-regional moves as an aspect of graduate mobility and will also provide a more detailed picture of the opportunities available to graduates in different areas.
Place of study is also an important aspect of graduate mobility, and there is substantial policy interest in the role which higher education providers can play in encouraging graduates to stay in their region of study. Analysis of the relationship between the design and nature of work and more detailed patterns of graduate mobility – comparing, for example, the fair work scores of those stayers who also studied in their home region with the scores of stayers who studied outside their home region before returning to work – may add another dimension to our understanding of the importance of moving for work or study in determining graduate outcomes.
While comparing the nature of the work done by graduates in different areas or with different mobility patterns is valuable, such comparisons only tell us part of the story. The Graduate Outcomes survey, however, provides us with a range of different variables which may provide some useful context for our measure. Looking at our measure in conjunction with other self-reported factors, such as, for example, graduates’ main reasons for taking a job, could yield useful insights into graduate priorities and decision-making processes. At the same time, investigating the relationship between our measure of fair work and other employment characteristics, such as salary (which contributes to a separate dimension of employment quality), SIC (Standard Industrial Classification), and SOC (Standard Occupational Classification), could help to contextualise some frequently used outcomes measures. Such analysis could in turn help policy makers, employers, and others supporting graduates through the transition to the workplace, helping to ensure that all graduates have the opportunity to progress from higher education into fair and decent work.
 The full strategy document can be found at https://www.gov.scot/publications/scotlands-economic-strategy/. Fair work, attainment, and regional cohesion are discussed in Section 2.3 (pp. 59-68).
 In Northern Ireland: https://www.northernireland.gov.uk/sites/default/files/publications/nigov/ni-economic-strategy-revised-130312_0.pdf (see especially Section 5.28); in Wales, graduate retention was one of the issues raised by the Economy Minister at a recent economic summit: https://gov.wales/moving-welsh-economy-forward-team-wales-recovery-built-all-us-economy-minister.
 See, among others, Future of Cities: Graduate Mobility and Productivity, a 2016 report published by the Government Office for Science (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/510421/gs-16-4-future-of-cities-graduate-mobility.pdf); The Great British Brain Drain: Where graduates move and why, a 2016 report published by the Centre for Cities (https://www.centreforcities.org/wp-content/uploads/2016/11/16-11-18-The-Great-British-Brain-Drain.pdf); Home and Away: Social, ethnic and spatial inequalities in student mobility, a 2018 report from the Sutton Trust (https://www.suttontrust.com/wp-content/uploads/2019/12/Home_and_away_FINAL.pdf); London calling? Higher education, graduate mobility and early-career earnings, a 2021 report published by the Department for Education and the Institute for Fiscal Studies (https://ifs.org.uk/uploads/Higher-education-geographical-mobility-and-early-career-earnings.pdf); and Staying local: Understanding the value of graduate retention for social equality, a 2021 report produced jointly by the Bridge Group and the UPP Foundation (https://upp-foundation.org/wp-content/uploads/2021/09/Staying-local-graduate-retention-in-regions-embargoed.pdf).
 In their 2019 report, ‘There’s no place like home’: An exploration of graduate attitudes toward place and mobility, Cunningham and Christie consider the case of graduates living and working in the North West (https://luminate.prospects.ac.uk/media/d2c5e4bc-08c6-4d19-a6d7-dddb0ea8d289/hecsu-research-theres-no-place-like-home.pdf); the recent UPP report discusses graduate motivations more generally.
 Since HESA does not directly collect data for students studying at further education colleges in England, Scotland, and Northern Ireland, we have not included those students in our sample.
 The dataset we have used for this analysis was collected by HESA, and the sample we extracted was originally developed to support a collaborative academic research project with an external partner. Providers whose data was included in this dataset had given explicit permission for their data to be used for research purposes. This dataset therefore did not include a small number of the providers that subscribe to provide data for the HESA Student records, i.e., those which did not permit onward use of their data for such purposes. (Further information regarding onward use categories can be found at https://www.hesa.ac.uk/support/provider-info/subscription/onward-use.) These providers comprise only a very small portion of the total sample. The dataset also does not include College HE in England, Scotland, or Northern Ireland, where FE colleges do not submit data to the HESA Student records.
 We should note that, although we describe graduates as ‘moving to’ or ‘staying in’ a region for work, our data shows region of employment, not residence, and it is possible that some graduates (whether we identify them as movers or stayers) will be employed in a region in which they are not resident.
 Our use of ‘movers’ and ‘stayers’ thus differs from the influential taxonomy developed by Charlie Ball in his 2015 report, Returners, Stayers, Loyals and Incomers: Graduate migration patterns (https://hecsu.ac.uk/assets/assets/documents/hecsu_graduate_migration_report_january_15.pdf), since Ball’s categories include place of study as well as place of residence and subsequent work. Ball’s ‘stayers’, those who move away from their home region to attend higher education and then remain in the area in which they studied, count as ‘movers’ according to our two-way classification.
 We find that each region of England has a higher proportion of movers when compared with the proportion in any one of the devolved administrations, and that these differences are statistically significant.
 Recent analysis by the Office for Students (https://www.officeforstudents.org.uk/media/45bc055c-a06b-4ea6-a344-0b050cacca3a/geography-of-employment-and-earnings-autumn-2021-update.pdf) provides more detail about the different graduate labour markets that exist within administrative regions.
 See the Future of Cities report cited above, the 2021 UPP Foundation report, the work of the Civic University Network (https://civicuniversitynetwork.co.uk/the-civic-network/), and recent analysis from the Office for Students on place-based inequalities and the role of higher education (https://www.officeforstudents.org.uk/publications/place-matters-inequality-employment-and-the-role-of-higher-education/). For taxonomies of graduate migration which include place of study, see not only Ball’s Returners, Stayers, Loyals and Incomers: Graduate migration patterns (https://hecsu.ac.uk/assets/assets/documents/hecsu_graduate_migration_report_january_15.pdf), but also Cunningham and Christie’s There’s no place like home (https://luminate.prospects.ac.uk/media/d2c5e4bc-08c6-4d19-a6d7-dddb0ea8d289/hecsu-research-theres-no-place-like-home.pdf), which extends Ball’s classification system.