Measuring graduate jobs

While there is no agreed upon definition of what sort of jobs are suitable for those with a degree (Green and Henseke, Reference Green and Henseke2016), a number of attempts have been made to define graduate jobs dating back to at least the late 1990s when student contributions to fees were introduced in the UK. These attempts to define graduate jobs can be seen as falling into four broad categories:

1. (1) Definitions based on pre-existing groupings in the Standard Occupational Classification (SOC) or the National Statistics Socio-economic Classification (NS-SEC) systems;

2. (2) Definitions that take into account the proportion of workers within occupational groups that hold certain levels of qualification, typically using SOC at a more detailed level;

3. (3) Definitions based on the skills used in different occupations;

4. (4) Definitions based on earnings.

Studies in the latter category have attempted to define jobs as suitable for graduates by either assigning a value based on the proportion of workers within a given occupational group that earn more than a given hourly rate (‘occupational earnings’ – Roksa and Levey, Reference Roksa and Levey2010), or simply estimating the effect of subject studied on the chances of being in the top 5% of earners (Sullivan et al., Reference Sullivan, Parsons, Green, Wiggins and Ploubidis2018). Such approaches are subject to the same limitations levelled at studies looking at the financial returns to a degree. Skills levels are not the only factor that determine wages and many valuable and highly-skilled jobs are not highly paid.

In the first approach listed above, occupations are assigned as graduate or non-graduate if they are in the top (MacMillan et al., Reference MacMillan, Tyler and Vignoles2015) or top two broad categories in the highest level of classification (analytic class or major group) in the NS-SEC (Bukodi and Goldthorpe, Reference Bukodi and Goldthorpe2011; De Vries, Reference De Vries2014)Footnote ¹ or SOC classification systems (Centre for Higher Education Research and Information [CHERI], 2002; Okay-Somerville and Scholarios, Reference Okay-Somerville and Scholarios2013). In some cases, specific occupations at a lower level of classification are included or excluded (e.g. Glover et al., Reference Glover, Fielding and Smeaton1996), or the definition is broadened out to include SOC major group 3 ‘Associate professional and technical’ occupations (Shury et al., Reference Shury, Vivian, Turner and Downing2017).Footnote ² These definitions have some appeal as they are easy to code and make use of the hierarchical structure of the SOC and NS-SEC classifications. However, they have been criticised as imprecise and somewhat arbitrary, particularly where the inclusion or exclusion of occupations do not reflect the proportion of incumbents that hold a degree (Alpin et al., Reference Alpin, Shackleton and Walsh1998).

Another problem with the above approach is that it fails to take into account a changing labour market and the growing professionalisation and ‘graduatisation’ of some occupations (Okay-Somerville and Scholarios, Reference Okay-Somerville and Scholarios2013). Attempts have been made to address this by defining graduate jobs by looking at the qualification levels of workers in different occupations, including by:

• defining occupations as graduate if the modal level of qualification of incumbents is degree-level or higher (Alpin et al., Reference Alpin, Shackleton and Walsh1998);

• scoring occupations according to the mean level of education (McKnight, Reference McKnight, Elias, McKnight, Purcell and Pitcher1999);

• classifying occupations as graduate jobs if the proportion of incumbents with a degree meets a given threshold (Elias and Purcell, Reference Elias and Purcell2004);

• assigning a value based on the proportion of workers in that occupational unit who have at least some HE (Roksa and Levey, Reference Roksa and Levey2010).

While all of these measures have value, in that they reflect an evolving labour market and capture the graduatisation of occupations, they are somewhat tautological (Green and Henseke, Reference Green and Henseke2016). If graduate jobs are defined as the jobs that graduates do, this risks conflating professionalisation of some occupations with growing underemployment in others.

The third type of classification listed above attempts to address this issue by classifying occupations based on the skills needed to perform different jobs. Using data from the British Skills and Employment Survey, Green and Henseke (Reference Green and Henseke2016) classified occupations as graduate or non-graduate by estimating the chances of incumbents in a given occupation reporting that (a) a degree (or higher) was ‘required’ to get the job and (b) the qualification was considered ‘essential’ or ‘fairly necessary’ to do the work competently. While the method used in this classification is transparent and replicable, meaning it can be reclassified as occupations and the labour market change, there are two main limitations. First, self-reports of current incumbents may overestimate the extent to which a degree is necessary to get the job and do the work (i.e. confirmation bias), and second, it is susceptible to grade inflation if a degree is increasingly perceived to be the norm due to an oversupply of graduates.

Using a slightly different approach, Elias and Purcell (Reference Elias and Purcell2013) (SOC(HE)2010_EP) scored all 369 occupations (4-digit) in the 2010 SOC on the use of three types of higher-level skill: specialist, orchestration or communication. The resulting classification distinguishes three types of graduate occupations:

1. (1) Experts – knowledge-intensive occupations that use specialist HE knowledge on a daily basis;

2. (2) Orchestrators – jobs that require individuals to draw on and orchestrate their own and others’ knowledge to evaluate information, assess options, plan, make decisions and co-ordinate the contributions of others to achieve objectives;

3. (3) Communicators – jobs requiring skills based on interpersonal skills, creative skills or high-level technological knowledge, and capacity to access, manipulate and communicate information effectively.

4. (4) All other occupations are classified as ‘Non-graduate’.

While the process used in this approach is time-consuming to replicate (Green and Henseke, Reference Green and Henseke2016) it not only avoids the tautological reasoning identified above but also has the advantage of enabling analysis of the relationship between degrees of different subjects – that provide different skills and knowledge – and the jobs that can potentially make use of these different skillsets.

It should be noted, however, that none of the above measures have been fully accepted in all quarters, particularly in the creative sector in HE. A definition of creative jobs was developed by researchers drawing on contributions from industry and policy stakeholders and a consortium of 26 higher education institutions (HEIs) with significant provision in creative arts and design (Ball et al., Reference Ball, Pollard and Stanley2010). This definition includes some occupations considered as non-graduate in previous definitions of graduate jobs (e.g. jewellery, glass, ceramics and textiles makers and technicians) along with many that would be considered graduate jobs (e.g. graphic designers, journalists, marketing professionals and senior managers of arts organisations), and so accounts for the diverse career aims of creative graduates.

Subject comparisons in graduate outcomes

Studies that have looked at subject differences in accessing graduate jobs (Table 1) tend to indicate that graduates of STEM and/or highly vocational subjects, such as medicine, engineering or law, tend to fare well in the graduate labour market, whereas those who studied art, humanities or social sciences tend to fare less well. Notable exceptions to this rule are biological and physical sciences whose graduates tend to fare less well than other sciences such as medicine, engineering and computer science (e.g. CHERI, 2002; Purcell et al., Reference Purcell, Elias, Atfield, Behle, Ellison, Luchinskaya and Tzanakou2012). Data from Futuretrack show considerable variation not only in the propensity but also the type of graduate jobs that graduates had 1-2 years after graduation (Purcell et al., Reference Purcell, Elias, Atfield, Behle, Ellison, Luchinskaya and Tzanakou2012), although this analysis did not control for personal characteristics and other factors.

Table 1. Summary of subject findings from studies using different measures of graduate jobs

Note: *in terms of securing graduate jobs.

These studies show that the selection of a suitable outcome measure is not neutral but has an important influence on the findings. Whatever measure is used, comparison creates winners and losers. Equally, focussing solely on the pecuniary gains of individuals will, by definition, tend to favour certain subjects more closely aligned with higher-paying occupations and industries while penalising subjects whose graduates may be willing to forego higher wages in order to do something worthwhile or creative (UCAS, 2021; Ball et al., Reference Ball, Pollard and Stanley2010). As current research and policy discourse seems preoccupied with earnings, it can appear that only subjects aligned with high-wage occupations and industries are of any value. A more nuanced understanding of the wider labour market value of HE in different subjects is therefore warranted.

From the aforementioned definitions of graduate jobs, two in particular have appeal for providing a wider contribution to debates about the value of degrees in different subjects. The Elias and Purcell (Reference Elias and Purcell2013) definition enables investigation of the extent to which graduates from different subjects secure jobs appropriate for their skills. Similarly, Ball et al.’s (Reference Ball, Pollard and Stanley2010) definition of creative occupations provides a useful measure of whether graduates secure creative work. This is important, not only to those who opt for creative subjects (Ball et al., Reference Ball, Pollard and Stanley2010) but also because of the strategic importance of the creative industries in UK policy (Department for Business Energy and Industrial Strategy [BEIS], 2018; HM Treasury, 2021).

Using these two wider measures of labour market outcomes provides the much-needed nuance currently lacking in debates about the value of HE.

Classifications of graduate jobs

The analysis uses two measures of labour market outcomes that enable us to explore the relative labour market experiences of graduates from different subjects: graduate jobs and creative jobs.

The measure of graduate jobs used in this analysis is the SOC(HE)2010_EP classification (Elias and Purcell, Reference Elias and Purcell2013):

• Expert;

• Orchestrator;

• Communicator; and

• Non-graduate.

The analysis uses this definition, first, in a binary manner (Graduate vs Non-graduate) and, second, in a more detailed way in order to provide a more nuanced understanding of the association between different subjects and different kinds of jobs.

The analysis also uses Ball et al.’s (Reference Ball, Pollard and Stanley2010) measure of creative jobs, in order to explore the association between different subjects and access to occupations in the creative sector.

Detailed descriptions of the occupations covered by each category can be found in Elias and Purcell (Reference Elias and Purcell2013) and Ball et al. (Reference Ball, Pollard and Stanley2010).Footnote ⁴ Examples of the most common occupations in each of the above categories found among graduates in our sample can be seen in Table 2.

Table 2. Common occupations in graduate and creative job groupings

Note: *The creative jobs measure is a separate categorisation from the SOC(HE)_EP definition, so some creative occupations are also found in other graduate and non-graduate job categories.

Descriptive analysis of other variables in the LDLHE shows that all four graduate outcome measures have internal validity. Graduates in all three graduate job categories in the SOC(HE)2010_EP classification were more likely than those in non-graduate roles to report that their qualification was needed to get the job and that they used the skills or knowledge developed during their degree (Figures 2 and 3). Likewise, graduates from creative subjects working in creative jobs 3.5 years after graduation were more likely than those working in non-creative occupations to indicate that their qualification was needed and that they used their skills/knowledge in their job.

Figure 2. Extent to which a degree was needed to secure graduates’ current main job (%).

Base: Working age graduates in employment (*creative arts subjects only)

Figure 3. Extent to which employers enable graduates to use skills gained during their degree studies (%).

Base: Working age graduates in employment (*creative arts subjects only)

It may also be worth noting at this stage that the above categorisation may reveal something about access to different types of power resources. While the Orchestrator category might be seen as aligning with Weberian notions of social class related to the economic order, the expert, communicator and creative categories are made up of occupations that hold relatively high status (Chan and Goldthorpe, Reference Chan and Goldthorpe2004) despite some occupations in the latter two commanding lower wages on average. While these questions are not the focus of this article, this may be an avenue of future enquiry.

Approach to analysis

Multivariate analysis was used to examine subject differences in the propensity to have a graduate or creative job as a main job 3.5 years after graduation, while controlling for a range of factors that have been shown to be associated with labour market outcomes from previous research (De Vries, Reference De Vries2014; Sullivan et al., Reference Sullivan, Parsons, Green, Wiggins and Ploubidis2018).

The analysis first uses logistic regression (LR) to estimate propensity to have a graduate job (Outcome_dummy _i = 1) compared to having a non-graduate job and then propensity to have a creative job compared to a non-creative job, as given by:

\begin{align}E\left( {Outcome\_dumm{y_i}} \right) &= Prob\left( {Outcome\_dumm{y_i} = 1} \right)\\ & = F\left( {Cha{r_i},\,Subjec{t_i},\,Erro{r_i}} \right)\end{align}

Multinomial logistic regression (MLR) was then used to estimate propensity to have an Expert, Orchestrator or Communicator job (c) compared to having a non-graduate job.

$$E\left( {SocH{E_i}} \right) = Prob\left( {SocH{E_i} = c} \right) = F(Cha{r_i},\,Subjec{t_i},\,Erro{r_i})$$

Where c ϵ {1,2,3}

The controls (Char _i ) used in all analyses were: gender, age at graduation, ethnicity, socio-economic background (NS-SEC), region of domicile, schooling (state Vs privately funded), prior attainment (UCAS tariff points), classification of degree and university mission groupFootnote ⁵ of HEI attended. Descriptive statistics and multicollinearity test information is presented in the Appendix (Supplementary Materials, Table A.1).

While the analysis does not allow us to demonstrate causality between subject of study and increase/decrease in the chances of securing a graduate job, due to endogeneity and selection effects into HEI and subjects (Belfield et al., Reference Belfield, Britton, Buscha, Dearden, Dickson, van de Erve, Sibieta, Vignoles, Walker and Zhu2018a), it does allow us to explore associations between subject of study and the relative chances of being in different types of graduate jobs 3.5 years after graduation.

A second limitation of our analysis relates to potential clustering effects. Such effects need to be accounted for when cases are sampled by cluster or where treatments were applied to clusters. While neither condition applies in our case, it is possible that within cluster correlation at the university level could bias standard errors in the model, affecting significance levels of coefficients. While it is possible that university level effects could mean that the error terms for individuals from the same university are correlated in some way there are no strong reasons to assume that such effects would be large. First, our sample is random and the regressor of interest (subject) is not fixed at the university level (Angrist and Pischke, Reference Angrist and Pischke2009). Second, individuals on the same course may take different options and have different teachers, thus no reason to assume teacher effects. And, third, the outcome of interest (occupation 3.5 years after graduation) is one step removed from any potential teacher effect (e.g. on test scores). However, while it was not possible to test for potential clustering effects at the university level, because a variable for institution attended was not available from HESA, a robustness check using bootstrapped standard errors was carried out (see Appendix, Supplementary Materials, Table A.3). The effect of bootstrapping on the standard errors was almost negligible lending credibility to our findings. Following Angrist and Pischke (Reference Angrist and Pischke2009) we report the analysis with the highest standard errors here, which are the results without bootstrapping.

Table 3. Propensity to have a graduate/creative job (binary logistic regression) and different types of graduate job (multinomial logistic regression)

Controls: Prior attainment, HEI mission group, classification of degree, schooling, socio-economic group, age, ethnicity, gender, region of domicile

Base: Working age first degree graduates in employment (no further study or qualifications) for whom UCAS tariff points are available (excludes medicine and dentistry, and veterinary science) (n=12,759)

Notes: Significance level P< †.10, *.05, **.01, ***.001)