Returning from earning: UK graduates returning to postgraduate study, with
particular respect to STEM subjects, gender and ethnicity
Steve d’Aguiar (formerly of Higher Education Funding Council for England)
Neil Harrison (Faculty of Arts, Creative Industries and Education, University of the West of
England)
Abstract: It has been argued by some (e.g. the Confederation of British Industry [CBI]) that
graduates lack the skills that render them employable. In particular, graduates of science,
technology, engineering and mathematics (STEM) subjects are often portrayed as being unready for
the world of work.
This paper uses three large-scale national datasets from the UK to explore this assertion,
including the results of the Destinations of Leavers from Higher Education surveys. It reports
analysis of 22,207 individuals who graduated from their first degree in 2007, and works from the
hypothesis that those entering the workforce and then returning for taught postgraduate study are
primarily doing so due to underemployment in the period following graduation.
The study uses binary logistic regression and finds that a range of educational, demographic
and employment-based variables have a significant relationship with the propensity to return for
taught postgraduate study. Of particular note, those returning tend to be high-achievers from elite
universities in low-skill work after graduation, as well as women and those from minority ethnic
communities; this suggests a mix of individual and structural factors at work. In addition, STEM
graduates were significantly less likely to return, apparently challenging the argument advanced by
the CBI.
Keywords: higher education, STEM, work-readiness, underemployment, DLHE
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Background
An ongoing discourse around a perceived under-preparation of graduates for employment
continues, both in the UK (e.g. Moreau and Leathwood 2006; Tomlinson 2007; Holmes 2013;
Tymon 2013) and other developed countries (e.g. Martin et al. 2000). This discourse generally
makes reference to a set of so-called ‘soft’ skills, such as the ability to present ideas and interact
with colleagues (Leitch 2006; The Royal Society 2006; Henderson et al. 2010). Concerns have
been expressed, notably from the Confederation of British Industry (CBI), that this underpreparation is particularly true of graduates in science, technology, engineering and mathematics
(STEM) subjects (CBI 2008; Smith 2007).
However, this appears to be challenged by the UK Commission for Employment and Skills
(UKCES) and their Employer Skills Survey conducted in 2011 (UKCES 2012a,b). This involved
over 87,000 telephone interviews with diverse businesses across the UK, and investigated, inter
alia, graduates’ readiness for work and skill shortages. Only 14 per cent of English employers
considered that those leaving higher education were poorly prepared for work (UKCES 2012a,b).
Similarly, the Institute of Directors reported that just 9 per cent of respondents to a survey of 500
directors were dissatisfied with graduates knowledge and skills (IoD 2007). Indeed, evidence for the
under-preparedness of graduates appears to be largely anecdotal (The Royal Society 2006; Smith
2007; Henderson et al. 2010), although employers continue to feel that universities could do more
(Lowden et al. 2011).
The particular interest in STEM subjects derives from a widely-held belief that they are ‘vital for
our economy to enable the UK to do well as a nation’ (House of Lords 2011, 1); similar sentiments
are also expressed by Smith (2009). Both Henderson et al. (2006) and Smith (2007) report that
employers believe that modularised learning in universities has contributed to a decline in
graduates’ scientific knowledge, such that the quality of STEM graduates has fallen, rather than the
quantity. Mellors-Bourne et al. (2011) report a perception that many STEM graduates are lacking
in core scientific knowledge and workplace experience, with the CBI’s survey of senior executives
revealing that ‘42% of firms consider STEM graduates lack the right skills’ (2008, 7).
There are also concerns that there are not enough graduates in STEM subjects and, in particular,
groups such as women (Roberts 2002; Kirkup et al. 2010; Regan and Dillon 2012; Smith 2012),
some ethnic minorities (Elias et al. 2006; DeWitt et al. 2011; Smith and White 2011) and those
from lower socioeconomic backgrounds (Smith and White 2011; Campaign for Science and
Engineering 2012) are under-represented on many (especially high status) courses, although women
now outnumber men overall in STEM subjects (Ratcliffe 2013).
Given the policy interest around the preparation for employment, relatively little empirical work has
been undertaken. The focus here is, therefore, on the analysis of secondary national quantitative
data, with particular reference to the hypothesis that STEM graduates are, on average, less prepared
for graduate employment than their peers who studied non-STEM subjects. It uses data from the
Destinations of Leavers from Higher Education (DLHE) surveys, combined with student-level data
held by the Higher Education Statistics Agency; there has been little previous research conducted
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using the DLHE dataset, which is collected from graduates six months and three-and-a-half years
after completion, notable exceptions being Chevalier (2008), Gittoes (2009), Norton (2008) and
Woodfield (2011).
Problematising work-readiness and underemployment
This study is situated within the broader discourse around graduate employability. Space precludes
a full discussion of this much-debated concept (see Holmes 2013 for an historical overview), but a
commonly-used definition is ‘a set of achievements – skills, understandings and personal attributes
– that makes graduates more likely to gain employment and be successful in their chosen
occupations’ (Yorke 2004, 8). Here we primarily engage with a more limited (and recent) concept
of ‘work-readiness’, in the sense of students being able to ‘hit the ground running’ without the need
for additional training in the initial period of employment (Mason, Williams and Cranmer 2009).
As evidenced by the CBI and UKCES reports cited above, employers argue for the value of workreadiness in reducing costs, maintaining continuity and increasing productivity (Boden and Nedeva
2010). Discussions have mainly focused on the ‘soft’ skills associated with the contemporary
workplace, particularly around working with other people and marshalling and communicating
information and ideas. It is also asserted that there are discipline-specific elements of workreadiness, for example in healthcare (Walker et al. 2013), accountancy (Jacklin and De Longe
2009), engineering (Jollands, Jolly and Molyneaux 2012) and science (Coll and Zeegwaard 2006).
Specifically, in relation to STEM graduates, there are concerns about cross-cutting science skills
around scientific principles, laboratory practice and applied mathematics (Roberts 2002; Henderson
et al. 2010).
Within this concept of work-readiness, graduates are constructed as needing both academic
credentials and a wider (and, arguably, ill-defined – see Hinchliffe and Jolly 2011 or Tymon 2013)
set of skills, knowledge, behaviours and dispositions that simultaneously contribute to their
‘attractiveness’ to employers. Employers therefore expect graduates to be able to evidence their
work-readiness through the information provided in job applications, through the interview process
(and associated test exercises) and through their performance once in post. A weakness would
therefore hamper the individual’s ability to secure work or to progress at the expected pace in the
early stage of their career. In addition, graduates may possess different levels of career
management skills and therefore have suboptimal approaches to marketing themselves to employers
(Bridgstock 2009).
A lack of such work-readiness could occur across whole programmes, subject areas or universities
(the publication of comparative statistics being partly the rationale behind the DLHE), but also at
the individual level, with graduates achieving a strong academic result, but failing to acquire the
skills or specialist knowledge valued by employers; Mellors-Bourne et al. (2011) provide some
evidence for this. In other words, a graduate’s work-readiness can be enhanced or compromised by
features of their degree programme (e.g. insufficient content focused on contemporary employment
or opportunities to develop practical skills within the curriculum) or by their own interaction with it
in terms of the skills and knowledge resulting (Cranmer 2006; Mason, Williams and Cranmer
2009). Rather than being a binary condition, work-readiness can more usefully be seen as a form of
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positional capital, but, in the sense used here, excludes other potential positional advantages such as
the prestige attached to the university attended.
Work-readiness may therefore feed into (but not define) the ability of graduates to secure work at an
appropriate level and thrive within this environment in the first few years of their career; this is a
phenomenon that is widely-recognised by students themselves (Moreau and Leathwood, 2006;
Tomlinson 2007, 2008; Tymon 2013). In a highly-competitive labour market, low work-readiness
could therefore compromise the potential to secure or maintain graduate-level employment, leading
to the individual becoming ‘underemployed’ in a role that is less skilled than their degree (or degree
class) would suggest.
Indeed, evidence for widespread underemployment is strong. Chevalier and Lindley (2009)
compared two cohorts of students spanning a period of rapid expansion in higher education in the
UK: those who graduated in 1990 and in 1995. They concluded that the proportion working in roles
not requiring a degree nearly doubled between the two cohorts (from 7 percent to 11 percent) and
that there was a wider group of graduates in jobs only recently requiring degrees. Drawing on their
longitudinal study of 17,000 undergraduates, Purcell et al. (2012) estimate that as many as 40
percent spent significant periods in what they define as ‘non-graduate work’ in the two years after
university, while Mosca and Wright (2011a) provide a figure of 37 percent six months after
graduation, for the 2002/03 cohort. Smetherham (2006) provides similar evidence based on the
perceptions of graduates, finding around one-in-five felt that they were qualified for, and capable
of, more demanding work. Indeed, Scurry and Blenkinsopp’s (2011) review of the literature
suggests that there are both objective and subjective elements to underemployment – a sense in
which some jobs are inherently ‘non-graduate’ (see Elias and Purcell 2004), while other jobs may
cause the graduate to feel that they are working below their qualification or ability level.
Poor work-readiness could therefore be one explanation for underemployment. However, this
argument has its critics. Moreau and Leathwood (2006) argue that an over-focus on the individual’s
work-readiness is a misframing, as it fails to respect the structural inequalities that provide
differential access to graduate jobs. This is supported by Wilton (2011), who uses a sample of
business graduates to demonstrate a disjuncture between self-reported employability skills and the
likelihood of underemployment after graduation. Others (e.g. Boden and Nedeva 2010) point out
that complaints about graduate skills has been a popular refrain from employer groups for several
decades and suggest that this discourse may reflect more about power relations between employers,
government and universities than the abilities of individual students. There are also concerns about
the subjective and unmeasurable nature of employers’ requests (Hinchliffe and Jolly 2011, Tymon
2013).
More broadly, Brown (2003) argues that after a period of post-industrial realignment in the UK (and
elsewhere in the developed world), the number of highly-skilled jobs has stagnated, producing an
‘opportunity trap’ in which an ever-growing pool of graduates emerging from an expanded higher
education sector is funnelled into a labour market of fixed size. With the supply of graduates
significantly outstripping the demand from traditional employers, an highly competitive labour
market is established, especially for the most prestigious and lucrative jobs. In this environment,
even the possession of a first class degree does not guarantee employment at the desired level,
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where employers are able to filter candidates ruthlessly by various forms of positional advantage,
including work-readiness. The net result is a large surplus of qualified graduates finding
themselves displaced into non-graduate work and ‘institutionally disappointed’ (Brown 2013); this
may occur within a particular field (e.g. forensic science, which produces more graduates each year
than the total jobs in the discipline: Tobin 2011) or geographical location (see Mosca and Wright
2011b). In the long run, this ‘opportunity trap’ sees previously non-graduate occupations redefined
as graduates come to predominate and blurring the lines between the objective and subjective forms
of underemployment (Scurry and Blenkinsopp 2011).
In the short-term, it is important to consider which groups are more likely to find themselves
positionally disadvantaged. Wilton (2011) finds that women and graduates from minority ethnic
communities report the greatest skills gains from higher education, but the poorest early career
experiences in terms of level of work, satisfaction and pay. In similar vein, Moreau and Leathwood
(2006) argue that factors such as gender, ethnicity, age and disability status may trigger active (e.g.
preferential employment of certain groups) and passive (e.g. an unconscious drive to recruit more
‘people like us’ who will ‘fit in’) forms of discrimination, regardless of their work-readiness.
Furthermore, Cranmer (2006) finds that reputational status is held to confer significant advantage to
those passing through elite universities; a position supported by Boden and Nedeva (2010), who
argue that lower status universities strive to substitute this disadvantage with work-readiness
initiatives. This reputational factor is likely to further disadvantage graduates from minority ethnic
communities who continue to be under-represented in elite universities. These critical voices
therefore argue that structural inequalities have more role in defining patterns of graduate
employment than work-readiness.
Holmes (2013) rejects both the work-readiness approach and the critical response as simplistic and
poorly grounded in empirical data. Instead, he proposes a ‘processual’ model which stresses a
negotiated transaction between graduate and prospective employers, in which the former attempts to
convince the latter that they fit their definition of ‘graduateness’. This will inevitably differ
between employers and may be informed by the structural inequalities discussed above, but it also
encompasses aspects of the graduate themselves, what they are able to evidence and how they
present themselves. This finds support in Tomlinson’s (2007) distinction between ‘careerists’ and
‘ritualists’, whereby the former seek to accrue positional advantage (e.g. through experiences
outside of the formal curriculum) in contrast to the latter who are unwilling to ‘play the game’ and
resign themselves as a result to lower status employment, at least in the first instance. Furthermore,
Tymon (2013) notes that some of the ‘skills’ identified as supporting employability are actually
personality constructs and may therefore be immune from the very concept of skills development.
Under Holmes’s (2013) ‘processual’ conceptualisation, success in the graduate labour market might
reasonably be seen as function of both work-readiness and the ability and willingness of the
individual to compete in the marketplace, partly through their qualifications, but also through how
they engage with a range of structural and agentic challenges. As a result, there is a danger that
graduates may (a) fail to secure work in their chosen career, (b) find themselves in work at a level
that they feel is too low, and/or (c) fail to progress within their career at an appropriate rate. Indeed,
Holmes (2013) predicts a group of such individuals which he defines as having a ‘failed identity’ in
the graduate labour market. We now consider what responses they might have to this situation.
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Returning to study
As a result of widespread underemployment and a congested graduate labour market, some
graduates are likely to feel an imperative to undertake taught postgraduate courses, either to obtain
additional work-related skills or to differentiate themselves from others. The latter is a precursor to
‘credential inflation’ (Brown 2003), whereby competition within the ‘opportunity trap’ drives
individuals to acquire additional qualifications to distinguish themselves from others which, over
time, drives up the credentials expected for a given occupation without the nature of the work
changing.
The Royal Society considers that for both the manufacturing and research and development sectors,
‘an important route to such professional jobs is through a higher degree’ (2006, 43). The view that
students need to differentiate themselves from their peers, including by obtaining postgraduate
qualifications, is also held by students themselves (Tomlinson 2008). Brooks and Everett (2009,
346) report that graduates considered a return to postgraduate study as a “felt need to ‘compensate’
for poor performance, ‘specialise’ to gain more work-related skills and ‘gain the edge’ within a
mass system of higher education”, while Bowman (2005, 241) identifies a group of graduates
‘coming back’ into taught postgraduate courses and seeking distinction in the labour market:
“Some of these students were frustrated at not being able to get into work in their subject
area. Others had been doing jobs that they could not have got without doing their degree, but
their experiences had not matched their expectations of ‘graduate level’ work.”
This paper, therefore, works from the premise that a return to postgraduate study (especially in the
context of a taught course) after a period of employment is a natural and widespread response by
graduates who find themselves underemployed, either objectively or subjectively (Scurry and
Blenkinsopp 2011). As such, the sequence of graduating, working for up to three years and then
returning to a postgraduate course could be seen as a marker for either low work-readiness or
structural factors in underemployment. Using DLHE data, Mosca and Wright (2011a) find that
underemployment after six months is a strong predictor for underemployment after three-and-a-half
years, so returning to study may be one means of avoiding this.
There are, of course, myriad other reasons why individuals might act in this way. A return to
postgraduate study could represent a natural aspect of career progression, where specialisation or
wider skills are required after two or three years of graduate work experience. More prosaically, in
the climate of rising costs, some individuals may deliberately enter the labour market with the
intention of returning to study once they have accumulated sufficient savings. Indeed, there could
be a range of highly personal factors at work, like the desire for a radical career change or a failed
self-employment venture. Conversely, returning to study is not the only option open to
underemployed graduates, who may prefer to ‘work their way up’ through an organisation or
establish their own business. However, the existence of large-scale national datasets does allow the
basic act of returning to study to be examined with the aim of testing which factors might
predispose an individual to return, which may give clues about the motives behind doing so.
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Research question
This study compares two groups of graduates across the first three-and-a-half years after completing
their undergraduate course. The main group of interest comprises those returning to taught
postgraduate study after a period of employment (or unemployment) – referred to as ‘returners’.
The larger comparison group comprises those who did not return to taught postgraduate study,
remaining within the labour market throughout the first three-and-a-half years – referred to as
‘leavers’.
The primary research question was therefore to determine whether which graduates were more
likely to be returners and why. The specific questions addressed were:
1. How do the populations of returners and leavers contrast across: occupation after six months;
gender; age; ethnicity; undergraduate degree classification; undergraduate degree subject; whether
they undertook a sandwich placement; and status of university at which their undergraduate degree
was undertaken?
2. Which of the factors listed in 1 above are significantly associated with the likelihood of a return
to taught postgraduate study?
3. Do STEM graduates have a statistically higher propensity to return than graduates of other
subject areas?
4. Are the factors that are significantly associated with the likelihood of a return to taught
postgraduate study the same for STEM and non-STEM subjects?
Methodology
Population of interest
Using data for 2007/08 entrants, the Higher Education Policy Institute and the British Library
[HEPI/BL] (2010) classify 61 percent of the 179,283 UK (i.e. not EU or other international)
postgraduate students as being on a taught course, either full-time or part-time and ranging from
postgraduate certificates to masters degrees. A further 14 percent were undertaking a postgraduate
certificate in education (PGCE), while 9 percent were undertaking research degrees, with the
remainder on a heterogeneous range of professional and specialist courses. The median age for a
new taught postgraduate student was 24 for full-time students and 33 for part-time students,
demonstrating that a period in work between undergraduate and postgraduate study is relatively
common. While the gender profile of taught postgraduates appears to be largely static over time
(with 55 percent being women), the proportion from minority ethnic groups rose from 16 percent in
2000-01 to 20 percent in 2007-08.
This study concentrates on the progression of graduates into taught postgraduate study following a
period of either employment or unemployment. The population is restricted to UK students (i.e. not
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EU or other international) and those who pursued a full-time first degree programme ending in
2006-07. Three other exclusions were made:

Those progressing immediately from their first degree studies on to postgraduate
programmes at the Short DLHE census date. It is surmised that they had planned to
undertake further academic study during the course of their degree, possibly with a specific
career pathway in mind. As such, they fall outside the tight focus of this study as they have
not been withing the graduate labour market and are not reacting to their experiences of it.

Those undertaking research degrees, as the majority will be seeking entry to academic
careers as opposed to those in the wider economy. As such, they comprise a rarefied
subgroup of postgraduates that are drawn primarily from the academic elite and value a
distinct set of personal skills.

Those undertaking a PGCE, as they are seeking a distinct and highly-defined career path in
entering the teaching profession. In particular, they were excluded from this study as they
potentially blur the distinction between STEM and non-STEM careers – e.g. a primary
teacher with a mathematics specialism.
The definition of STEM subjects used here will be that used by the Higher Education Funding
Council for England (HEFCE), which broadly speaking covers medicine and medical
technology/science, biological and physical sciences, mathematics and computing, and engineering
and technology. It should be noted, however, that there are different views on what constitutes a
STEM subject. For instance, the Council for Industry and Higher Education consider that sports
science, psychology, architecture and building and planning fell within the category of STEM
(CIHE 2009), all subjects which HEFCE consider not to be STEM.
Data sources
The data for this research has been obtained from the Higher Education Statistics Agency (HESA)
and has come from three separate sources: the HESA student data collection and the two DLHE
surveys. For simplicity and clarity, the latter will be referred to as the ‘Short’ DLHE (undertaken
six months after graduation) and the ‘Long’ DLHE (undertaken three years after the Short DLHE).
All of the data is available as SAS datasets and this was the statistical software package used for
analysis.
The HESA student data is obtained from submissions from Higher Education institutions containing
details of individual students and their studies and is conducted annually. The Short DLHE is a
census conducted by Higher Education institutions on UK and EU domiciled graduates. As with the
HESA student data, this is collected annually. The Long DLHE is carried out by a research agency
(IFF Research – see Shury et al. 2011) on behalf of HESA and comprises a sample of those students
surveyed in the Short DLHE who have given their consent to be resurveyed. This sample is
structured to ensure that enough responses can be obtained to allow analysis by subgroups of
interest. Data is collected biennially and has been conducted four times; with those students
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graduating in the 2002-03 academic year forming the first survey, and those from the 2006-07
academic year being the latest published to date.
For the purposes of this research a subset of the data from all three sources has been compiled. The
three datasets are therefore the 2006-07 HESA Student collection, the 2006-07 Short DLHE
(undertaken in early 2008), and the 2006-07 Long DLHE (undertaken between November 2010 and
March 2011). The subset consists of those UK domiciled graduates who completed the Long DLHE
survey and whose qualification aim was a first degree when they completed their studies in the
2006-07 academic year, but who were also not studying, either full-time or part-time, on the 200607 Short DLHE census date. The three datasets can be easily linked using unique personal which
allow the longitudinal tracking of individuals through their higher education experiences and into
the DLHE surveys. Figure 1 shows the relationship between the three datasets, showing how the
sample was constructed.
[Figure 1 here]
To recap, then, the leaver group comprised those who completed a first degree in 2006-07, were not
studying six months later (at the Short DLHE survey) or three-and-a-half years later (at the Long
DLHE survey), and had not completed a taught postgraduate course in between the two surveys.
Conversely, the returner group comprised those who completed a first degree in 2006-07 and were
in the labour market (i.e. not studying) at the Short DLHE survey, but who were either studying a
taught postgraduate course at the Long DLHE survey or had done so between the Short and Long
DLHE surveys.
The total number of UK respondents to the Long DLHE survey in 2006-07 who completed their
first degree through full-time study was 30,070. Of these, 7,115 were studying at the Short DLHE
census date and have therefore been excluded. A further 748 were excluded as they returned to a
research degree or PGCE course, leaving 22,207 individuals within our sample, comprising 19,882
leavers (89.5 percent) and 2,325 returners (10.5 percent). The dummy variable allocating
individuals into one of these two groups served as the dependent variable in the logistic regression
analysis presented below.
Independent variables were constructed from the combined dataset on the basis that (a) there was a
good a priori basis to hypothesise from theory or previous research that they would be related to
decisions about postgraduate education, and (b) that data was readily available in a usable and
reliable form. The only variable that was excluded for the second reason was the individual’s
socio-economic status prior to their first degree, where the data available is too incomplete and of
questionable reliability and validity. The eight variables included were therefore:

Job at the Short DLHE snapshot date (SOC): derived from the Standard Occupational
Classification (SOC) codes. Although comprising over 600 categories, the coding is
hierarchical and therefore simple to collapse into just four categories;
Managerial/professional, Associated professional/technical, Semi/unskilled, and Unknown.
It is assumed that graduate occupations fall into the first two categories and not into the
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third. As such, graduates in a semi/unskilled job six months after graduation might be
considered to be markedly underemployed.

Gender (GENDER): coded Male or Female. Purcell et al. (2012) find that women are
more likely to be in non-graduate work in the period immediately after university.

Age (MATURE): Woodfield (2011), using DLHE data, finds that mature students are
moderately advantaged in the graduate labour market once other factors are controlled for,
partly due to their pre-existing work experience, which would suggest that they are less like
to return to postgraduate study; this is supported by Purcell et al (2012). Age was included
in the study through a binary variable indicating if a student was mature (coded Yes) or
young (coded No), derived from the date of birth and the date on which study for the first
degree commenced. Mature students were defined as being 21 years of age or more on
commencement of their first degree.

Ethnicity (ETH): Rafferty and Dale (2008) and Rafferty (2012) present data that graduates
from minority ethnic communities have poorer graduate employment outcomes and that
these appear to be worsening over time, although this is partially contradicted by Purcell et
al (2012). The ethnicity classification on the Short DLHE dataset collapsed down from 21
categories into three categories: White, other known ethnicity (coded as BME for ‘black and
minority ethnic group’) and Unknown. The sample size regrettably precluded a more
nuanced approach to ethnicity.

Class of first degree (DEG): the degree classification variable was collapsed down to five
separate categories: First class; Upper second class; Lower second class; Third class and
Pass; and Unclassified. The final group comprises graduates in subjects in which
classifications are not given (e.g. medicine), rather than those that failed to achieve a pass.
Purcell et al (2012) found that degree class was strongly linked to the likelihood of being in
non-graduate work.

STEM or non-STEM first degree (STEM): a binary variable denoting whether the content
of the individual’s first degree comprised at least 50% STEM subjects (i.e. single subject, at
least one subject in a joint degree, the major component in a major/minor combination, or at
least two of the three in a triple combination), following HEFCE’s definition (coded Yes);
others were coded No.

Placement year (SANDWICH): a binary variable reflecting whether the individual
undertook a placement/sandwich year as part of their first degree, as it is widely held that
this experience provides a boost to employability (e.g. Wilton 2012; Moores and Reddy
2012). This includes placements within a working context (i.e. with an employer), but not
those undertaken on the basis of ‘study abroad’. This was coded Yes or No.

Institution attended for first degree (UNITYPE): a binary variable denoting whether the
first degree provider was designated as a university prior to 1992 (Pre-1992) university or
not (Post-1992). Within the UK context, this acts as a broad proxy for institutional status
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within the higher education sector. Consideration was given to using a more nuanced
definition, but continuing debate about appropriate measures of status made this too
contentious to be useful. It should also be noted that the absence of a dedicated variable is
likely to see this variable acting as a crude proxy for a socio-economic status, with
individuals from advantaged backgrounds being disproportionately found in elite
universities.
Descriptive statistics
Table 1 provides a description of the final dataset which was used for the statistical modelling.
[Table 1 here]
Analysis
The analysis was carried out in two separate stages. For Stage 1, the whole dataset was used, with
the leaver/returner dummy as the dependent variable and using the eight independent variables as
potential explanatory factors. In addition, five two-way interaction terms were added as potential
factors on the basis that a coherent a priori case could be made for a particular combination and its
potential impact on educational decisions: DEG/SOC, DEG/GENDER, DEG/ETH, STEM/ETH and
STEM/GENDER.
In Stage 2 of the analysis, the data was split into two subsets; those respondents who had studied a
STEM first degree and those who had studied a non-STEM degree. Again, progression to taught
postgraduate study was the dependent variable, with the seven remaining independent variables and
relevant interaction terms as potential explanatory factors; both subsets of the data were then
analysed in parallel.
Logistic regression analysis (Aitkin et al. 1989; Armitage et al. 2002; Cody and Smith 1997;
Dobson 1990) was used to model which factors were statistically significant in whether an
individual was a returner or leaver. Logistic regression is the most appropriate technique where the
dependent variable is dichotomous in nature and where the researcher is interested in predicting the
likelihood of an individual case falling into one of the two categories. In this instance, the model
predicts which factors are associated with graduates being more likely to be a returner.
All of the variables used in the analysis are categorical or ordinal, so dummy variables were created
in order to conduct the regression analysis (Cody and Smith 1997). A ‘stepwise’ approach was
used to determine the most parsimonious collection of explanatory variables for each model, adding
(or removing) variables one at a time until further changes did not significantly increase (or
decrease) the explanatory power of the model as a whole. Throughout this study, the 5 percent
significance level was used.
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In logistic regression models, the most useful measure of effect size for each value of the
independent variables is the ‘odds ratio’. An odds ratio of 1 signifies that the value (e.g. Female)
has no impact on the odds of being in the group of interest (i.e. returners) relative to the reference
group (i.e. Male). An odds ratio of greater than 1 signifies that, in this example, women are more
likely than men to be returners, while less than 1 would signify that they are less likely. In cases
where the group of interest is unusual (i.e. less than 10 percent), the odds ratio provides a
reasonable approximation of the increased likelihood of being within the key category of the
dependent variable that is associated with the given category of the independent variable, relative to
the reference category (Zhang and Yu 1998). As the overall likelihood of being a returner is only
marginally over 10 percent, this approximation is felt to hold relatively well and the odds ratio is
therefore reported as an effect size in terms of relative likelihood, with the proviso that it will
provide a slight overestimate. Continuing the example in the previous paragraph, an odds ratio of
1.40 for women can be read as a 40 percent greater likelihood of being a returner, while an odds
ratio of 0.70 could be read as being 70 percent as likely or, perhaps more intuitively, that men were
43 percent more likely, 1.43 being the inverse of 0.70.
Results
Stage 1: full dataset
Seven of the independent variables were found to have a statistically significant effect (with the
exception of MATURE) and so all were included in the final model shown in Table 2. Two
interaction terms (DEG/SOC and STEM/GENDER) were also significant, entering the model as the
final two ‘steps’ in the model.
[Table 2 here]
The main effect variables which made individuals significantly less likely, ceteris paribus, to be
returners were having undertaken a sandwich placement (OR = .587), having attended a post-1992
university (OR = .623) and studying a STEM subject (OR = .676). In other words, those who had
not taken a sandwich placement were 70 percent more likely to return, while those who attended a
pre-1992 university were 61 percent more likely and those with a non-STEM degree were 48
percent more likely, all else being equal. In addition, being from a minority ethnic community had
an odds ratio of 1.308, translating to a 31 percent higher likelihood of returning.
[Table 3 here]
The interpretation of the variables involved in the interaction terms is not straightforward in
regression models where there are categorical variables, and particularly where there are more than
two categories. Table 3 shows compound estimated odds ratios for STEM and GENDER. It can be
seen that women with a STEM degree are 48 percent more likely to return than men with a STEM
degree, all else being held constant. In non-STEM subjects, women’s increased propensity is 11
percent; this is not statistically significant, but very close to being so. Approaching this from the
opposite perspective, men with a non-STEM degree were (coincidentally) 48 percent more likely to
Page 12 of 29
return than men with a STEM degree. Therefore, the propensity for returning is generally higher
for women and for those holding a non-STEM degree, but particularly for women with STEM
degrees and men with non-STEM degrees.
[Table 4 here]
Table 4 presents the estimated odds ratios for DEG and SOC, relative to those with unclassified
degrees. A consistent picture emerges among those with first or upper second class degrees,
whereby the odds ratio of returning increases relative to the declining status of the job held six
months after graduation. This effect is greatest for those with first class degrees in semi/unskilled
jobs, who were over three times as likely to return. A similar trend appears to be operating for
those with lower second class degrees, albeit that this is not statistically significant. Those with a
third class or pass degree in professional/managerial jobs were nearly half as likely to return. In
summary, those combining low status jobs and higher degree classifications were significantly more
likely to return to study, whereas those combining high status jobs and low degree classifications
were significantly less so.
Stage 2: STEM first degree
Stage 2 of analysis splits the sample into two sub-samples: those studying a STEM subject and
those who did not. Among the STEM graduates, the seven remaining independent variables were
included in the model, the exceptions being MATURE and DEG. None of the interaction terms
were significant. The results are shown in Table 5.
[Table 5 here]
The basic pattern of relationships for the STEM group closely mirrors that for the sample as a
whole as reported above. Among this group, the factors with the greatest effects are having an
unskilled or semi-skilled job at the Short DLHE (83 percent greater likelihood of returning
compared to those in professional or managerial roles) and being female (53 percent greater
likelihood). Taking a sandwich year and/or studying at a post-1992 university were associated with
leaving, while coming from a minority ethnic community was associated with returning.
Stage 2: Non-STEM first degree
All seven variables were entered into the logistic regression model for those graduates whose first
degree was in a non-STEM subject, as reported in Table 6. Once again, none of the interaction
terms were significant.
[Table 6 here]
The broad relationship between the predictor variables and the propensity to return for postgraduate
study is similar – both to the STEM sub-sample and the sample as a whole. The largest effect size
Page 13 of 29
was derived from having a first class or upper second class degree (146 percent and 77 percent
more likely to return compared to an unclassified degree, respectively) or having undertaken a
sandwich year (82 percent more likely to leave). Women, those in semi-skilled or unskilled work at
the Short DLHE, those from pre-1992 universities and those from minority ethnic communities
were again significantly more likely to return, all else being equal. In this model, age was also a
statistically significant predictor, with mature students being less likely to return.
It is particularly notable that while the direction of effect is identical between the models for the
STEM and non-STEM sub-samples, the effect sizes vary dramatically. For example, among STEM
graduates, the strongest predictor for being a returner is whether or not the graduate was able to
secure a high-status job on graduation, while degree classification was not significant. Conversely,
degree classification was the most important predictor for non-STEM graduates, while initial postgraduation employment had much less effect.
Discussion
The regression models detailed above provide evidence around a number of key factors in whether
a graduate returns to taught postgraduate study after a period in the labour market. However, it is
important to remember that, as with any quantitative cohort study, the analytical technique herein is
not able to illuminate issues of causality with any degree of certainty. Instead, this discussion is
restricted to exploring plausible mechanisms that may provide explanations for some of the effects
described in the results section above; the move beyond conjecture must await future studies.
All of the posited factors have a significant effect in one or more models and the direction of that
effect is consistent across both the STEM and non-STEM subsamples. Relative to being a leaver,
the returners were consistently more likely to be women, people from minority ethnic communities,
those who had good degrees and those who originally studied in pre-1992 universities. They were
also more likely to be those who had low-skill jobs six months after graduation, but less likely to be
mature students or those who had undertaken a sandwich year as part of their degree. Returners
could therefore be collectively typified as high-achievers from prestigious universities who did not
secure appropriately high-status work after graduation, with gender and ethnicity also being
mediating factors. However, this is not the whole story, as the sample also includes many returners
who do not fit this profile, including those who were able to secure graduate-level jobs in the
professional/managerial or associate professional categories.
Indeed, there are many reasons why returners may return, as laid out earlier in this paper. They may
have been unable to find a suitable job after graduation in the ways described by Chevalier and
Lindley (2009), who identify the phenomenon of graduates being (or feeling – Scurry and
Blenkinsopp 2011) overqualified for their current job, but not immediately capable of finding a
better job. This provides an interesting dissonance between high academic achievement and low
employment achievement in the period immediately following graduation and may lend some
credence to the hypothesis that some otherwise successful graduates are not leaving university
sufficiently work-ready. As Brooks and Everett (2009) report, this behaviour may also occur when
the individual perceives that they underperformed as an undergraduate and could re-establish their
credentials as a ‘high flyer’ through postgraduate study. Bennett and Turner’s (2012) report for the
Page 14 of 29
Higher Education Academy describes that nearly 60 percent of taught postgraduate students had
decided to take their course in order to improve their employment prospects, while almost as many
cited progression in their current career.
As noted above, there may be more prosaic reasons for returning. The relationships identified
above could be explained, at least in part, by high-achieving graduates spending one or more years
in the labour market to save up for their postgraduate studies, although it is hard to see how this
would be widespread on the wages afforded in low-skill work. Some possibly return purely out of
interest, although the cost of postgraduate courses, even part-time, would suggest that this group is
small. The nature of quantitative research is such that these alternative explanations are untestable
within the existing dataset.
The most marked relationship within the dataset was between those taking and not taking sandwich
years as part of the degree. The latter were nearly twice as likely to be returners, with a similar
effect being identified for both STEM and non-STEM subjects. Given that the purpose of the
sandwich degree is to provide valuable workplace experience, it would appear that this strategy was
largely successful for students. Age was a predictor only in the context of those holding non-STEM
degrees, which is perhaps slightly surprising. Using DLHE data, Woodfield (2011) found that
mature graduates tended to be advantaged as they are more likely to have accrued work skills and
experience through their life prior to university; they were also more likely to have entered higher
education with a functional and career-led motivation. Both of these factors would appear to argue
in favour of mature graduates finding appropriate work soon after graduation. One could also posit
that their increased age means that extending their education further would provide a lower
perceived payback given the remaining working years ahead of them. On this basis, it might have
been imagined that age would have played a greater role in the regression models, although the
threshold for being in the ‘mature’ category was only 21 years of age on entry and this may have
limited its effect. Notably, age was not a significant predictor for returning among STEM
graduates.
The reason why graduates of pre-1992 universities should be more likely to return to take taught
postgraduate courses is not obvious given their positional advantage through reputation (Cranmer
2006; Boden and Nedeva 2010). Firstly, it is important to remember that this is a relatively crude
bifurcation of the sector and that its role within the regression model may be more about the
socioeconomic profile of the two groups rather than the pedagogic practices within them. Croxford
and Raffe (in press) provide strong evidence that pre-1992 universities continue to be dominated by
students from the middle classes. Graduates of these universities are therefore more likely to have
the material resources within the family to permit a return to postgraduate study, a greater
disposition toward extended education (Ball 2003a,b) and sense of academic self-belief (Sullivan,
2006), as well as being able to capitalise on the reputational status of their alma mater (Wakeling
2005). If finding themselves underemployed, returning to study is a comfortable solution.
Conversely, while working class graduates may be less likely to find themselves in graduate-level
work, even once university status has been controlled for (Gordon 2013), the same response may
not appear feasible. Indeed, there is some evidence to suggest that working class students have
lower expectations of graduate employment and therefore resigned to less prestigious occupational
Page 15 of 29
trajectories. For example, Davies et al. (2013) report that this group are more likely to choose
subjects with a low wage premium, while Tomlinson (2007) finds they are more likely to be
‘ritualists’ that reject the ‘game-playing’ of high-stakes graduate employment. Meanwhile,
Bathmaker et al. (2013) argue that they are often aware that their prospects are limited by a range of
structural factors, including access to internships and the recruitment practices of some graduate
employers (Browne 2010). As such, working class students may be less likely to find themselves
‘subjectively’ underemployed in the way described by Scurry and Blenkinsopp (2011), while also
lacking the material resources to return if they do.
An alternative, and not incompatible, explanation may be that the former polytechnics’ continuing
tradition of offering more vocationally-focused courses (either in content or delivery) promotes the
ability to swiftly find work at an appropriate level; Martin et al. (2000) found that graduates from
vocational programmes felt better prepared for work, while Boden and Nedeva (2010) note the
efforts that these universities place on employability, albeit mainly within less prestigious career
paths. Mason, Williams and Cranmer (2009) argue that while the value of such employability skills
efforts appears limited, curriculum links to employers have a strong benefit in terms of graduate
outcomes (Lowden et al. 2011). Indeed, if vocationally-focused courses are not providing graduates
with smooth access to careers, then their very purpose needs rethinking.
However, given the uncertainty surrounding the role of this variable in the regression model, it is
impossible to come to any firm conclusion about the relationship between university status and
returning, confining us here to conjecture. Nevertheless, the relationship itself is consistent and
strong, with pre-1992 university graduates being nearly twice as likely to return as post-1992
university graduates, once other factors like degree classification are controlled for.
The phenomenon of women and graduates from minority ethnic communities being
disproportionately disposed to return to taught postgraduate courses is consistent across the
regression models; while this possibility was acknowledged in the selection of variables, the
strength of the effect was not anticipated and it therefore warrants further comment. In general,
both groups currently show a higher relative demand for education. For example, female
participation in higher education has recently increased at a faster rate than has male participation
(Regan and Dillon 2012), while [removed for review] draws a connection between the size of
minority ethnic communities in an area and the prevailing demand for higher education; Wakeling
(2009) and HEPI/BL (2010) find that they also have a vibrant demand for taught postgraduate
degrees. Shah et al. (2010) explore why some minority ethnic communities place high value on
education, providing them with a form of ‘ethnic capital’ that sees them extend their education to
overcome perceived barriers to employment.
Given that the regression models control for degree classification and university status, there are
clearly other factors at work that impact on the educational decisions of women and graduates from
minority ethnic communities. Indeed, research evidence suggests that these groups experience (or
anticipate) forms of discrimination in the workplace which lead them to increase their qualifications
in order to compete effectively. Taylor, Charlton and Ranyard (2012) find that their sample of a
mixture of graduates and final year students predicted that women and minority ethnic graduates
would have more difficulty in securing graduate employment and that this effect was additive
Page 16 of 29
between the two groups. Similarly, Browne (2010) finds evidence of apparent discrimination
against both groups in access to the most prestigious ‘fast-track’ graduate training schemes, while
Connor et al (2004) report that minority ethnic students generally anticipate employment difficulties
after graduation. Wilton (2011) reports these groups feeling more skilled at the end of their
business degrees, but finding themselves in lower-status and less lucrative work on graduation.
Indeed, there is good evidence for graduates from minority ethnic communities commonly working
in roles significantly below those predicted by their qualification level (Rafferty and Dale 2008;
Rafferty 2012), with Cassidy and Wright (2008) finding that such underemployment causes
psychological and health issues, which may act as a further stimulus to return. These factors could
collectively begin to explain the patterns within the regression models, with women and graduates
from minority ethnic communities having a higher basic demand for education, potentially driven
by experience or fear of discrimination and the negative affect of underemployment. As Brown
(2003, 157) asserts, “…in the opportunity trap, some are more trapped than others.”
Importantly, the relative effect of gender and ethnicity was stronger among the STEM graduates.
For example, among this group, women and those from minority ethnic communities were 53
percent and 48 percent more likely to be returners than men and white graduates respectively. This
compares with equivalent figures of 12 percent and 25 percent for non-STEM graduates. Both
women and minority ethnic graduates are in a nuanced position with respect to STEM subjects.
Women have been traditionally under-represented (Smith 2012), but have recently come to
predominate overall (Ratcliffe 2013). Their representation is now very strong in biology, medicine
and veterinary science, but weak within engineering and technology, with approximate equity with
men in mathematics and physical sciences. There is a similarly mixed pattern by ethnicity, with
minority communities well-represented in medicine and technology, but under-represented
elsewhere (Smith and White 2011).
One possible explanation for this study’s findings is that these groups choose subjects that are less
likely to yield graduate work, due to over-supply and labour market congestion. For example,
Purcell et al. (2012) note that the incidence of underemployment is particularly high among biology
graduates – a group in which women predominate – but low in male-dominated engineering and
technology. The fact that STEM programmes that disproportionately attract women also provide
the least certain access to graduate jobs could go some way to explaining the results of this study.
Alternatively,, whatever processes are working to make women and BME graduates return to
postgraduate study in general may be operating more strongly within STEM subjects. Despite
recent shifts in student numbers, STEM careers are often still viewed as a white, male stronghold
(e.g. DeWitt et al. 2011; Mendick and Moreau, 2013). Indeed, official figures analysed by Kirkup
et al. (2010) demonstrate the reality behind this perception. As of 2008, women comprised only 12
percent of the science, engineering and technology workforce, with just 5 percent of working
women engaged in the sector compared to 31 percent of men. Among professionals, women
comprise 55 percent of the medical sector, but only 16 percent across other science, engineering and
technology disciplines. Interestingly, ethnicity appears to play a mediating role, with women from
minority ethnic communities being slightly more likely to work in science (8 percent), engineering
and technology than women from the white majority community, while their male counterparts are
somewhat less likely to do so (22 percent). On a positive note, they find some modest progress
Page 17 of 29
towards equality. However, there is continuing evidence of widespread hegemonic, discriminatory
and exclusionary practices operating in some occupations (e.g. see Porter 2013 or Geek Feminism
2014); it is clear how this could effectively limit opportunities for women and graduates from ethnic
minority communities in the ways in which Moreau and Leathwood (2006) describe.
All else held equal, STEM graduates were significantly less likely to be returners than non-STEM
graduates, suggesting that STEM graduates were better equipped for the labour market than those
pursuing other disciplines. This appears to contradict the assertion that STEM graduates are poorly
prepared for the workplace, at least in relative terms (CBI 2008; Henderson et al. 2010; Smith 2007;
The Royal Society 2006). It does not, of course, mean that there were not many examples of STEM
graduates returning to postgraduate study; the data in Table 1 show that 8.8 percent of STEM
graduates returned, compared with 11.1 percent of non-STEM graduates. However, it does suggest
that the particular focus on STEM subjects might be misguided when contextualised across higher
education as a whole.
Interestingly, degree classification and age were not significant predictors for returning to
postgraduate study among STEM graduates, whereas they were for non-STEM graduates.
Conversely, status of job after graduation was much more important in determining STEM
graduates behaviour, perhaps suggesting a two-tier funnelling of STEM graduates into strongly
defined professional or non-professional pathways, with graduates in the latter considering
postgraduate study as a means of changing their fortunes for the better. This is clearly an area that
would bear more research.
Limitations
This study is limited by its use of secondary quantitative data, which can only illuminate broad
trends and not the rationales underpinning the actions of individual students. In particular, there
may be alternative explanations for some of these relationships; for example, an unknown
proportion of returning will be explained by involuntary entry to the labour market to raise funds for
postgraduate study. However, these individuals are indistinguishable within the dataset from those
entering the labour market and finding that it does not meet their expectations. Unpicking these
various motivations must be left to future studies..
Similarly, the act of returning must be linked to some extent with the act of continuing into taught
postgraduate courses directly from the first degree; the latter obviously constrains the set of students
available to do the former. For example, Purcell et al. (2012) provide evidence that men, those
from high status universities, some ethnic groups and those with high degree classifications are
more likely to continue in this way. This could partly explain why there are more women who
return, as men are more likely to continue with their education without entering the labour market
(Wakeling 2005).
The study was limited in scope by the resources available. As well as the issue of continuing, it has
not been possible to examine the timing of returns over the three year period between the Short and
Long DLHE censuses. These data do exist and a future study would be able to construct a dataset
Page 18 of 29
to explore this further. Similarly, this study deliberately excluded several groups of postgraduates
study from consideration, on the basis of distinctly different patterns of behaviour.
Finally, it would have been useful to have had data on socio-economic status at entry to
undergraduate study. However, the quality and coverage of the data available (see [removed for
review]), especially for mature students and those from the lowest socio-economic groups,
continues to make this form of analysis problematic, especially in the context of regression
modelling, which relies on largely complete datasets for reliable inference. It is likely that socioeconomic background is a factor in determining access to the graduate labour market and postgraduation educational decisions; any future studies will also need to engage with this shortfall in
the secondary national data that is available. As noted above, we believe that the university status
data provide at least a partial proxy, such that the regression models are not significantly
compromised.
Conclusions
This study sits within a context of an ongoing public discourse about the efficacy of undergraduate
degrees in preparing students for the world of work, especially within science, technology,
engineering and mathematics. It is asserted, by the CBI among others, that graduates are having to
return to postgraduate study in order to upgrade their skills and thereby make themselves attractive
to employers. This study provides new evidence on which individuals return to higher education
after initially rejecting the option to continue their studies, based on national secondary data from
22,207 graduates who completed their courses in July 2007. It finds that there are clear predictors
for this behaviour derived from a range of demographic, academic and employment-based
variables.
In particular, two particular groups of interest emerge with a higher-than-average propensity to
return. The first comprises highly-qualified individuals who find themselves in low-skill work
following graduation and who are presumed to quickly identify that they are objectively
underemployed. We tentatively suggest that this group may provide evidence for issues with workreadiness, given that they appear to be academic high-achievers who have acquired a ‘failed
identity’ (Holmes 2013) and turn to postgraduate study to rectify it.
The second group comprises women and graduates from minority ethnic communities, especially
within STEM subjects. Given the widely-acknowledged structural inequalities within the
employment market, it is not surprising that this study adds to the evidence base, although the scale
of the effect was marked. We also note Tomlinson’s (2007) argument that these individuals may
initially set their sights lower (due to an anticipation of losing out in high-pressure recruitment
environments), so this finding may reflect a subjective form of underemployment once experience
of the workplace has firmed up ideas of appropriateness and led to frustration of believing that one
is working at too low a level.
We also note the significance of sandwich years in discouraging returning, presumably due to the
practical skills acquired. Meanwhile, the finding that non-STEM graduates are more prone to return
Page 19 of 29
to taught postgraduate study than their STEM counterparts tends to contradict the assertion that
STEM graduates are less work-ready than their non-STEM peers.
These findings prompt some interesting questions for policy and practice. Why are some highachieving graduates finding themselves unable to find skilled employment after graduation? What
more, if anything, could universities do to smooth their transition into the world of work – either
through curriculum design or careers support? How can the status of the sandwich year be boosted
in the context of increased tuition fees? Is the use of postgraduate study by women and graduates
from ethnic minority communities indicative of discriminatory labour market practices?
In some ways, this study cuts to the heart of the purpose of higher education and particularly the
undergraduate degree. In particular, our findings add weight to the contention that women and
those from ethnic minority communities are finding and/or perceiving it difficult to compete on a
level playing field; this feature appears particularly strong within STEM subjects. There may,
therefore, be important questions of equality that the CBI and other representative organisations
need to ask of industry and business about their own practices
Page 20 of 29
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Page 25 of 29
Figure 1: Relationship of datasets
All students graduating in 2006-07
Short DLHE
respondents
Long DLHE
respondents
Full-time
UK students
not studying
at the Short
DLHE
Sample
(N=22,207)
Table 1: Descriptive Statistics
Leavers (89.5%)
SOC
GENDER
MATURE
ETH
DEG
STEM
SANDWICH
UNITYPE
Managerial/professional
Assoc. prof./technical
Semi/unskilled
Unknown
Female
Male
Yes
No
White
BME
Unknown
First class
Upper second class
Lower second class
Third class and pass
Unclassified
Yes
No
Yes
No
Pre-1992
Post-1992
Returners (10.5%)
Frequency
6332
5181
4641
3728
11241
8641
3672
16210
16172
3454
256
2871
9694
5032
852
1220
5938
13944
3104
16778
Percentage
31.8%
26.1%
23.3%
18.8%
56.5%
43.5%
18.5%
81.5%
81.3%
17.4%
1.3%
14.4%
48.8%
25.3%
4.3%
6.1%
29.9%
70.1%
15.6%
84.4%
Frequency
643
579
675
428
1483
842
368
1957
1841
449
35
428
1280
442
44
126
579
1746
209
2116
Percentage
27.7%
24.9%
29.0%
18.4%
63.8%
36.2%
15.8%
84.2%
79.2%
19.3%
1.5%
18.4%
55.1%
19.0%
1.9%
5.4%
24.9%
75.1%
9.0%
91.0%
10466
9416
52.6%
47.4%
1507
818
64.8%
35.2%
Page 26 of 29
Table 2: Stage 1 logistic regression parameter estimates
Parameter
Intercept
SOC (reference = Man/prof)
GENDER (reference = Male)
ETH (reference = White)
DEG (reference = Unclassified)
STEM (reference = No)
SANDWICH (reference = No)
UNITYPE (reference = Pre-92)
SOC/DEG (reference = Man/prof
and Unclassified)
Assoc. prof/tech
Semi/unskilled
Unknown
Female
BME
Unknown
First
Upper second
Lower second
Third/pass
Yes
Yes
Post-92
Ass/tech and First
Ass/tech and
Upper second
Ass/tech and
Lower second
Ass/tech and
Third/pass
Semi/unskilled
and First
Semi/unskilled
and Upper second
Semi/unskilled
and Lower second
Semi/unskilled
and Third/pass
Unknown and
First
Unknown and
Upper second
Unknown and
Lower second
Unknown and
Third/pass
STEM/GENDER (reference = No
and Male)
Odds
ratio
-1.9546
-.5158
-.6310
-.9792
.1071
.2683
.2391
.1118
-.0574
-.2808
-.6660
-.3914
-.5327
-.4740
Standard
Error
.1266
.2853
.3471
.5243
.0553
.0576
.1847
.1442
.1305
.1562
.3307
.0790
.0767
.0480
.597
.532
.376
1.113
1.308
1.270
1.118
.944
.755
.514
.676
.587
.623
<.001
.071
.069
.062
.053
<.001
.195
.438
.660
.072
.044
<.001
<.001
<.001
.7388
.3124
2.093
.018
.6124
.2969
1.845
.039
.5015
.3205
1.651
.118
1.0516
.4956
2.862
.034
1.2095
.3759
3.352
.001
1.0449
.3560
2.843
.003
.8430
.3719
2.323
.023
.4370
.5511
1.548
.428
1.1728
.5478
3.231
.032
1.1829
.5319
3.264
.026
.9242
.5466
2.520
.091
.0744
.7589
1.077
.922
.2824
.1049
1.326
.007
Estimate
Yes and Female
Page 27 of 29
p
Table 3: Odds ratio estimates for STEM and GENDER interaction
1.476*
STEM subject and Female (reference = Male)
1.113
Non-STEM subject and Female (reference = Male)
.897
STEM subject and Female (reference = not STEM)
.676*
STEM subject and Male (reference = not STEM)
* = significant at the 5% level
Table 4: Odds ratio estimates for DEG and SOC interaction, relative to Unclassified degree
Upper
Lower
Third/
First
Second
Second
Pass
1.118
0.944
0.755
0.514*
Prof/man
2.341*
1.742*
1.247
1.471
Assoc. prof/tech
*
*
3.748
2.684
1.754
0.795
Semi/unskilled
*
*
3.613
3.082
1.903
0.553
Unknown
* = significant at the 5% level
Page 28 of 29
Table 5: Stage 2 STEM logistic regression parameter estimates
Parameter
Intercept
SOC (reference = Man/prof)
GENDER (reference = Male)
ETH (reference = White)
SANDWICH (reference = No)
UNITYPE (reference = Pre-92)
Estimate
-2.5918
.2343
.6016
.0258
.4269
.3942
.1096
-.4348
-.3226
Assoc. prof/tech
Semi/unskilled
Unknown
Female
BME
Unknown
Yes
Post-92
Standard
Error
.0856
.1256
.1183
.1288
.0890
.1028
.3771
.1362
.1052
Odds
ratio
1.264
1.825
1.026
1.533
1.483
1.116
.647
.724
p
<.001
.062
<.001
.841
<.001
.001
.771
.001
.002
Table 6: Stage 2 non-STEM logistic regression parameter estimates
Parameter
Intercept
SOC (reference = Man/prof)
GENDER (reference = Male)
MATURE (reference = No)
ETH (reference = White)
DEG (reference = Unclassified)
SANDWICH (reference = No)
UNITYPE (reference = Pre-92)
Estimate
Assoc. prof/tech
Semi/unskilled
Unknown
Female
Yes
BME
Unknown
First
Upper second
Lower second
Third/pass
Yes
Post-92
Page 29 of 29
-2.4115
-.0086
.2161
.0325
.1146
-.1604
.2213
.2720
.9017
.5706
.1142
-.5199
-.6005
-.4941
Standard
Error
.1870
.0809
.0723
.0729
.0555
.0721
.0696
.2125
.1851
.1782
.1851
.2734
.0932
.0547
Odds
ratio
.991
1.241
1.033
1.121
.852
1.248
1.313
2.464
1.769
1.121
.595
.549
.610
p
<.001
.916
.003
.655
.039
.026
.002
.200
<.001
.001
.537
.057
<.001
<.001