Widening Participation in Higher Education: A Quantitative Analysis
1. Background and Policy Context
Education participation in the UK has risen steadily for the last half century (Figure
1), as measured by the proportion of students staying on past the compulsory school
leaving age and progressing to higher education.
Figure 1: Long-Term Trends in
100
Staying On at 16 and Participation in Higher Education (Age Participation
Index (API))
90
80
Staying On at 16
Age Participation Index
70
60
50
40
30
20
10
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
Source: DfES. Staying on rates refer to England. API includes Wales and Scotland.
Although participation has been rising, widening access remains a major concern, as
reflected in initiatives such as the HEFCE AimHigher scheme. Access to HE in the
UK remains disproportionately limited to those from higher socio-economic groups.
More than three quarters of students from professional backgrounds study for a
degree, compared to just 14% from unskilled backgrounds. HEFCE (2005) also note
the rise in gender inequality, as higher female attainment in school continues into
higher education. Further, there is persistent inequality by neighbourhood (HEFCE
2005), and between different ethnic minorities (Dearing 1997; Tomlinson 2001).
Recent policy developments will further affect trends in participation. Up front tuition
fees increased fears that poorer students would be deterred from participating in HE
(Callender (2003)). The 2004 Higher Education Act will introduce further change,
with higher and variable tuition fees alongside increased up front support for students.
It is not clear the impact this will have on access. In this context, we propose a
theoretically based empirical analysis of HE participation for different types of
student, particularly, students from lower socio-economic backgrounds, minority
ethnic groups, women, mature students and those entering HE with lower prior
attainment. The work will form an important baseline against which the effects of the
major changes to HE funding, which will mainly come into effect in 2006/07, can be
assessed.
2. Relevant Literature
There is a large empirical literature on widening access, much of which has focused
on the role of parental income, education and socio-economic status in determining
children’s likelihood of attending HE (Wolfe and Behrman, 1984; Glennerster, 1995;
Blanden and Gregg 2004, Gayle 2004). Recent research has suggested that inequality
of access to HE for disadvantaged students worsened in the UK during the 1980s and
early 1990s (Blanden et al. 2002, Galindo-Rueda, Marcenaro-Gutierrez and Vignoles
2004, Machin and Vignoles 2004). Glennerster (2001) also found evidence of a
strengthening of the relationship between social class and HE participation in the
1990s.
Differences in educational attainment between different groups of students emerge
early, particularly in terms of ethnic, gender and social class differences (Demack et
al. 2000, Schagen 1996). Figure 1 presents an analysis of DfES attainment data on
how achievement in Key Stages 1 to 4 varies by ethnic group and gender. Relatively
narrow differences between boys and girls of different ethnic groups at age 7 widen,
especially during secondary schooling, with Asian girls the highest, and Black boys,
the lowest average scorers in KS4 at age 16.
Figure 1: Average score at Key Stages 1 – 4 by ethnicity and gender
40
Key Stage points
35
30
25
20
15
10
KS1
KS2
KS3
F_Asian
F_Black
F_Mixed
F_Other
M_Black
M_Mixed
M_Other
M_White
KS4
F_White
M_Asian
Source: National Pupil Database combining PLASC 2003 and DfES attainment data
Figure 2 shows that for a given level of prior attainment (A level point score),
participation of young people in higher education does not vary significantly by socioeconomic background. Related to this is a strong evidence base on education
inequality in schools (Sammons 1995, Schagen 1996, Strand 1999, Gorard 2000).
A-level point score
Figure 2: Participation in HE at age 18 by A-level score and parents’ SEG
25+
13 to 24
1 to 12
0
10
20
30
40
50
60
70
80
Per cent
low er
higher
Source: Bekhradnia (2003) from DfES. Calculated from Youth Cohort Study data.
2. Research Objectives
The proposed research will add to knowledge in the following areas:
a. the determinants of the likelihood of entering HE
b. the quality and nature of the higher education experienced by
different types of student
c. the determinants of and barriers to progression in HE
The proposed research will identify at what stage in the lifecourse, and for which
groups of students, educational inequalities emerge. The lifecourse approach will
enable the research team to identify when opportunities to progress start to be closed
off to individuals, either through poor experience of learning, the need to enter the
work force or because non-aspirational decisions are made at a particular point in
time. This will help inform policy-makers designing strategies to widen access,
particularly on the timing and nature of school based initiatives. The research design
has been developed in collaboration with the Council for Industry and Higher
Education, who have identified a need for evidence to support employers adopting a
more diverse and inclusive approach to their recruitment of graduates and to inform
employer recruitment practices. This research will do just that, for example by
providing evidence on when and how otherwise able students decide not to progress
into HE, due to their family circumstances. The research will also inform employers
when they might usefully attempt to influence the HE choices of students (both in
terms of participation and choice of subject) and which students they should target.
Different elements of the research will be grounded in theory from education,
geography and economics. Although we do not set out an over arching theory, one
important research objective will be to develop an understanding of how economic
models might explain the success, or otherwise, of programmes designed to widen
participation in HE. This would contribute to a burgeoning economic theoretical
literature on educational inequality (e.g. Benabou 1996; De Fraja 2002).
3. Research Design
The proposed research will have three phases. Phase 1 will address questions relating
to the HE participation decision for disadvantaged students, ethnic minorities, men
and women, and students with lower prior attainment. It will identify when gaps in
educational attainment between different groups of students emerge. Phase 2 will
consider issues around the nature of the HE experience for different groups of
students, in terms of choice of subject and quality of institution. Phase 3 will analyse
progression in HE for different groups of students, including mature students.
4. Phase 1: The HE Participation Decision
Steep socio-economic, ethnic and gender gaps in HE entry and progression originate
early in life. We need to recognise the impact of schooling, and the existing 14-19
curriculum in shaping individuals’ attainment and aspirations regarding HE
participation1. To do this we need to know more about what will work to improve the
achievements and aspirations of young people from all backgrounds from an early
stage.
However, the evidence on life course determinants of HE entry and progression, have
been severely limited by lack of sufficiently large-scale data that contains information
about individuals throughout their schooling careers, through to their post-14, 16 and
18 choices, and throughout HE. This situation is about to change.
Our proposed approach involves using a new combination of large scale, individuallevel administrative data sets2, currently being created by DfES, to examine the
determinants of participation in higher education from the age of 11. The data would
contain information on the ethnicity, free-school meal status, birth date, postcode,
gender, and Key Stage results from KS2 (age 11) upwards of every state school
student in England who was in Year 11 in 2001/02 and who, if they so decided, would
have continued in post compulsory education in 2002/03 and 2003/04, and into
Higher Education in 2004/05 or 2005/06. This will be linked to these individuals’
Further Education (FE) and HE records, giving an indication whether they have
attended HE, and if so, what institution and subject they chose, and what support they
received in the form of grants and loans.
These data will give a comprehensive picture of the factors affecting children during
schooling, which contribute to their HE choices. It would provide information on
every single individual in the state system, enabling us to look at the paths of people
from disadvantaged and vulnerable groups, such as ethnic minorities (at a relatively
disaggregated level), students from deprived neighbourhoods, or with special needs.
Unlike previous work using individual level administrative data from HE records
alone (e.g. from HESA data), our analysis will be based on both participants and nonparticipants in HE, allowing robust conclusions to be drawn about the factors
determining HE participation.
1
As addressed in the TLRP project: Policy, Learning and Inclusion in the Learning and Skills Sector;
Coffield, Hodgson & Spours (2003-06).
2
See technical appendix 2 for a description of data to be used in the research.
For some of the analyses, particularly differences by gender, we will supplement the
data described above with information from the Longitudinal Survey of Young People
in England (LSYPE). The LSYPE gives rich data on the process by which 13 year old
children formulate their career ambitions and then tracks them over the period from
age 13 to 18.
The research questions
1. How does the likelihood of HE participation and the timing of HE entry vary
according to gender, ethnicity, SEN status, FSM status, neighbourhood of
origin, school type, date of birth, Key Stage 2, 3, GSCE and A level results?
2. When do the differences in attainment, which drive variations in the likelihood
of attending and progressing in HE, appear?
3. What is the role of early ability - measured by performance at KS2 – in
determining the decision to enter HE? Are there differences in the trajectories
of high ability students depending on their parents’ income (measured by FSM
status), and their geographical location?
4. What are the factors that explain why otherwise identical students in some
post-14 environments are more likely to enter HE than others?
4.1 The Role of Expectations
There is evidence that the effects of financial constraints on HE participation are
dwarfed by other family background characteristics (Chevalier and Lanot, 2002;
Heckman and Masterov 2004; Dearden et al 1997). An alternative explanation is that
disadvantaged pupils have different expectations about the costs of going to, and their
ability to benefit from, HE. We propose to analyse the role of expectations in
determining HE participation, using two instruments. The first is a measure of the
pupil’s own perception of his or her ability. The second is teacher perceptions of
pupils’ abilities. If pupils form low expectations of their abilities, or teachers offer
little encouragement, then pupils may be less likely to enrol in HE. If these
expectations are linked to social background, ethnic group or gender, then this has
implications for the accessibility of HE to these different groups. There is little
quantitative research on this issue. Reeves et al. (2001) found differences between
teacher assessment and pupil actual attainment at age 11 were not strongly related to
pupil background (except SEN). However, this was based on a relatively small
sample.
We recognise the limitations of quantitative evidence on complex psychological
issues such as confidence. However, understanding the determinants of pupil
confidence and teacher perceptions will usefully inform interventions by schools or
universities to target high achieving students who do not intend to continue into HE.
We will rely on data from the Programme for International Student Assessment
(PISA) for 2002 and 2003. The 2000 dataset focuses on literacy whilst in 2003 the
emphasis was on mathematics. For each survey between 2,500 and 3,000 observations
are available for the UK, providing a sample large enough for meaningful statistical
analysis. As well as family background characteristics and test scores, PISA includes
questions on “how confident do you feel about having to do the following
(mathematical) tasks?” This question is asked for 8 different problems so that an
index of confidence can be built. Subsequently a multivariate analysis of the
determinants of confidence, accounting for test performance, can be conducted to
show whether confidence varies across different groups of students in different school
settings.
For empirical analysis of teacher perceptions we draw on the combined administrative
data described above. These data record teachers’ assessments of each pupil’s national
curriculum attainment level, as well as their actual Key Stage test scores. The research
will model the relationship between pupil characteristics and teacher assessment
errors. Our initial investigation of this issue suggests that our hypotheses have some
merit: in 2003, teacher assessments of English at age 14 were lower than actual
attainment for 43% of low-income pupils (those eligible for free meals). For other
pupils the figure was only 32%. Teachers seem to underrate poor pupils. We will then
analyse the impact of teacher assessments on pupil participation in HE, and whether
this varies across different types of students.
Research Questions
5. Do differences exist across different student groups in terms of self and
teacher perceptions of ability?
6. Do teacher assessments of pupil ability influence the decision to enrol in HE
and do they play a more important role for some groups of students?
4.2 The Role of Locality
Theoretically, commuting or re-location location costs can impose high barriers to
university entrance, particularly for lower-income students. Some existing research
(e.g. Card 1995, Frenette 2004) supports the idea that people living further away from
universities are less likely to choose to continue to HE, and that this is a greater
problem for those from more disadvantaged backgrounds. This would imply lower
take up of HE amongst poorer school-leavers due to higher costs of attending HE for
this group, although changes to student support that lower the costs of HE for poorer
students may offset this problem. It also means that a school-leaver from a
disadvantaged background is less likely to enrol in a top-ranked university than a
school-leaver with identical credentials from a wealthier background, if top-ranked
universities are on average further away from the school-leavers’ homes (which is
likely to be the case since top-ranked universities are generally widely spaced). There
is also evidence that locality matters more for some ethnic/ gender groups. Dale et. al
(2002) found that South Asian women in Lancashire were more likely to be allowed
to enter HE if they could live at home. Furthermore, given that more students from
poorer backgrounds live at home and attend HE part-time, proximity is clearly
differently important for different groups of students.
Finding evidence on whether distance matters would identify if there is explicit
geographical inequality in access. Moreover, these distance effects may interact with
social background. If distance works against disadvantaged school-leavers in terms of
HE participation, then expansion of numbers on the current network of HE
institutions will favour those from better social backgrounds and would support a
policy to reduce these costs for low-income students, for example, by extending the
role of distance learning or increasing living grants for poorer students.
The analysis will be based on various distance metrics, including straight-line distance
and accessibility via the road/rail network. The study will use econometric analysis of
the combined administrative data set described earlier, which includes pupil postcode
information on both students who go on to HE and those who do not. Additional data
will come from the 2001 Census and Ordnance Survey geographical data products.
We aim to characterise the ‘catchment areas’ of different types of university in terms
of the spatial distribution of the family homes of university entrants, and there is
scope for visualising these using GIS software. We will then be able to measure
whether students enrolling in HE from poor social backgrounds attend universities at
closer distances than more advantaged school leavers with similar observable
qualifications. We can then measure the influence of distance, pupil disadvantage and
their interactions on the decision to attend university and choice of institution.
Research Questions
7. How important is distance from a university in determining HE participation?
8. How does locality affect the HE participation decision of different groups of
students, particularly those from disadvantaged backgrounds?
9. For a given level of prior attainment, what is the influence of distance, family
background and their interactions on the decision to attend a top-rank
university?
5. Phase 2: The HE Experience
Phase 2 analyses the nature of students’ participation in HE by prior attainment,
gender, age, ethnicity and socio-economic background. It will focus on the subject
and institutional choices made by these groups of students. The nature of students’ HE
experience is as important as whether or not they attend HE, given that we know that
the employability and wages of graduates from different subjects and institutions
varies considerably (Chevalier and Conlon, 2003, Smith, McKnight and Naylor,
2000). For example, the major source of wage differences between male and female
graduates with similar characteristics is the subject of their degree (Brown and
Corcoran, 1997; Daymont and Andrisani, 1984; Loury, 1997; and Machin and Puhani,
2003). The subjects chosen and quality of HE accessed by different groups of
students will also be an increasingly important policy issue as variable tuition fees are
introduced, giving rise to a potential cost/ quality trade off in HE.
The research will explore the interaction of prior attainment, socio-economic
background, ethnicity and gender in determining choice of degree subject and who
has access to the ‘best’ courses at universities, using the HESA student data set and
the combined administrative data set described in phase 1. This will be matched with
‘hard’ data on course quality, including QAA teaching scores, RAE scores, postuniversity employment rates and earnings returns. With these data we can assess
whether the determinants of course and institution sorting among students are
changing over time, focusing post 1998 following the first fee increase.
This phase of the research will also have an international dimension, examining the
way in which choice of degree subject by gender has altered through time in different
countries. The analysis will use micro-data from three countries (Labour Force Survey
in Britain, Enquete Emploi in France and the Mikrozensus in Germany). We also
plan to look at administrative data on subject of study in higher education over time
for each country so as to derive a time series on changing subject of degree study for
male and female graduates.
Research Questions
10. How does choice of degree subject and institution vary by gender, ethnicity,
socio-economic background, age and prior attainment?
11. To what extent is there ‘equality of access’ to ‘high quality’ institutions for
different groups of students?
12. To what extent does prior attainment determine access to the highest quality
courses?
13. Are women moving increasingly into higher rewarding subjects of degree and
is the pace of change different across countries?
6. Phase 3: Progression in HE
Progression within higher education is an important policy issue (Parry, 2002), but
there is substantially less quantitative evidence on this issue. Dearing (1997) cited
evidence that students from disadvantaged backgrounds progressed in HE as well as
more advantaged students (Hogarth et al. 1997) but more recent evidence is less
optimistic, suggesting that completion rates are lower for such groups (HEFCE 1999).
Nationally, HEFCE reports non-completion rates of 17% but with substantial
variation across institutions (HEFCE, 2000). We intend to use the administrative data
described in phase 1 to analyse rates of higher education progression for different
types of student, focusing particularly on differences by gender, ethnicity, age on
entry, prior attainment and socio-economic background.
The project will build on existing research on this issue both from the UK
(Arulampalam, Naylor and Smith (2005); Davies and Elias, 2003; Yorke 2001) and
elsewhere (Beekhoven, De Jong and Van Hout, 2000), exploiting the newly available
administrative data to look at this issue for school leavers. This database will not
however, include mature students or progression for students who take a few years out
before continuing their higher education. We will therefore also analyse progression
using the stand-alone HESA database of student records for 1998-2004. These HESA
data includes information on the social class of the students’ parents and the postcode
of the student’s parental home address. Using this postcode information, HESA data
can be merged with the 2001 Census data to provide detailed socio-economic
characteristics of the individual’s neighbourhood. Thus for both school leavers and
older students we will be able analyse progression rates by subject, institution and
socio-economic background.
Research questions:
14. Does progression in HE vary by socio-economic background, for a given level
of prior attainment?
15. Has the nature of progression changed, as higher education becomes part time
and potentially intermittent in nature?
16. Are particular groups of students at risk of failing to progress?
17. Do progression rates vary by subject and by institution, for a given level of
prior attainment?
6. Project Management and Deliverables
Project management responsibilities are set out in section 11. Quality assurance will
be ensured by the strength of the research team itself, which has an excellent track
record in delivering high quality quantitative research, and via the proposed Advisory
Committee, described in section 20. The research team has collaborated with one
another extensively under the auspices of the CEE, and has a proven collaborative
track record in this area, as shown by their recent Princeton University Press book
summarising the research of the CEE to date (Machin and Vignoles, 2005).
Key impacts from the research are described in section 18 and our dissemination
strategy in section 21. Results will be published in internally refereed discussion
papers (with executive summaries for each paper), followed by publication in peerreviewed journals. All papers will be available on the CEE website and there will be a
non-technical conference to disseminate results.
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