Is Improving Access to University Enough? Socio‐Economic Gaps in the Earnings of English Graduates
| Author | Neil Shephard,Lorraine Dearden,Jack Britton,Anna Vignoles |
| Date | 01 April 2019 |
| DOI | http://doi.org/10.1111/obes.12261 |
| Published date | 01 April 2019 |
328
©2019 TheAuthors. OxfordBulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.
Thisis an open access article under the ter ms of the CreativeCommons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properlycited.
OXFORD BULLETIN OF ECONOMICSAND STATISTICS, 81, 2 (2019) 0305–9049
doi: 10.1111/obes.12261
Is ImprovingAccess to University Enough?
Socio-Economic Gaps in the Earnings of English
Graduates*
Jack Britton,†Lorraine Dearden,†,‡ Neil Shephard§ and
Anna Vignoles¶
†Institute for Fiscal Studies, London, UK (e-mail: jack b@ifs.org.uk)
‡Institute of Education, University College London, London, UK (e-mail: l.dearden@ifs.
org.uk)
§Department for Economics and Department of Statistics, Harvard University, Cambridge,
MA, USA (e-mail: shephard@fas.harvard.edu)
¶Department of Education, University of Cambridge, Cambridge, UK (e-mail: av404@
camb.ac.uk)
Abstract
Much research and policy attention has been on socio-economic gaps in participation at
university, but less attention has been paid to socio-economic gaps in graduates’ earnings.
This paper addresses this shortfall using tax and student loan administrative data to inves-
tigate the variation in earnings of English graduates by socio-economic background. We
find that graduates from higher income families (with median income of around 77,000)
have average earnings which are 20% higher than those from lower income families (with
median income of around £26,000). Once we condition on institution and subject choices,
this premium roughly halves, to around 10%.The premium g rowswith age and is larger for
men, in particular for men at the most selective universities.We estimate the extent to which
different institutions and subjects appear to deliver good earnings for relatively less well
off students, highlighting the strong performance of medicine, economics, law, business,
engineering, technology and computer science, as well as the prominent London-based
universities.
JEL Classification numbers: I23, I24, I26, J62.
*Many civil servants and policy makers have helped us gain access to the data which is the core of this paper.
We must thank in particular Daniele Bega, Dave Cartwright, Nick Hillman, Tim Leunig and Lord Willetts who
were all crucial in making this project happen. Wealso thank Jonathan Cribb, Paul Johnson and participants at the
RES and AASLE conferences. We solely are responsible for any errors. We also thank the British Academy (grant
CH00096.0000) and the ESRC (grant ES00116.0000) and the Nuffield Foundationfor financial suppor t.The Nuffield
Foundationis an endowed charitable trust that aims to improve social well-being in the widest sense. It funds research
and innovationin education and social policy and also works to build capacity in education, science and social science
research. The Nuffield Foundationhas funded this project, but the views expressed are those of the authors and not
necessarily those of the Foundation. HM Revenue & Customs (HMRC) and Student Loans Company (SLC) have
agreed that the figures and descriptions of results in the attached document may be published. This does not imply
HMRC’s or SLC’s acceptance of the validity of the methods used to obtain these figures, or of any analysis of the
results. Copyright of the statistical results maynot be assigned. This work contains statistical data from HMRC which
is Crown Copyrightand statistical data from SLC which is protected by Copyright, the ownership of which is retained
Socio-Economic Earnings Gaps 329
I. Introduction
Higher education is seen as a potentially crucial tool for social mobility, providing a
possible route for students from lowerincome family backgrounds to achieve labour market
success and higher earnings. Consequently, there have been numerous government policies
around the world focussed on improvingaccess to university degrees for those from poorer
households. However, there is relatively little evidence on whether this should be the
primary focus of governments trying to improve social mobility.
Consistent with most countries around the world, in England educational achievement
and higher education access varies substantially by the level of parental income, with many
fewerstudents from poorer backgrounds attending university, particularly the highest status
institutions (Chowdry et al., 2013; Ermisch, Jantti and Smeeding, 2012). However, little is
known about the differences in earnings between graduates from poorer and richer family
backgrounds. Further, primarily due to data limitations, the question of whether differences
in earnings still exist conditional on university and subject choice, has remained largely
unanswered.1In this paper, we are able to address these shortfalls in the literature by
making use of a unique administrative database that tracks the earnings of graduates into
their mid thirties.
We use a data set that consists of anonymized individual level-administrative taxable
earnings data supplied by Her Majesty’s Revenue and Customs (HMRC), linked to in-
formation on students’ higher education (university or college) from the English Student
Loan Company (SLC). The latter is an institution supported by the state to provide loans
to students to fund their higher education. The HMRC and SLC data sets are hard linked
using a national identification number (National Insurance number2) and we have access
to a 10% random sample. We study cohorts of students who entered higher education from
1999 to 2005, and focus on the same students’ear nings between2008/09 and 2013/14. This
allows us to follow graduates through their most crucial career developing years and well
into their thirties. We also use Higher Education Statistics Agency (HESA) data which
we can match at the subject-institution (rather than individual) level. This includes the
socio-economic background and prior academic achievement of the students studying the
same subject in the same institution. This allows us to add further controls that capture
differences in the demographics of students in a given university and subject, although we
acknowledge that this does not eliminate ability bias in returns or deal with differential
selection into courses across individuals from different socio-economic backgrounds.
A common problem with administrative data is a limited set of background character-
istics for individuals.3We also face these limitations, and do not directly observe parental
income for individuals in our sample. However, we are able to infer a simple binary mea-
by SLC.The research data sets used may not exactly reproduce HMRC or SLC aggregates. The use of HMRC or SLC
statistical data in this work does not imply the endorsement of either HMRC or SLC in relation to the interpretation
or analysis of the information.
1The exceptions include a number of papers that investigateretur ns to privatevs. state secondary school education
in the UK, conditional on universityeducation (e.g. Crawford et al., 2016), and Chetty et al. (2017) which investigates
variation in returns to attending university by parental income in the US.
2This is the key individual identifier for all taxes, social security and student loans.
3The availability of linked administrative data has improved dramatically in the UK in recent years. The Lon-
gitudinal Educational Outcomes data (LEO) allows the linkage of entire education histories of individuals to their
earnings records. However,these data are currently available only for government research.
©2019 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and JohnWiley & Sons Ltd.
330 Bulletin
sure of parental income based a student’s SLC record, which notes the amount each student
borrowed in their first year of study. For English students starting university before 2006,
the amount individuals were eligible to borrow was linked in a monotonic way to their
parental income. We identify people as being from a higher income household if they are
borrowing exactly the maximum amount an individual from a higher income household is
eligible for in their first year of study.4This consists of approximately 20% of borrowers,
which in the paper we refer to as the richer group. The remaining 80% of borrowers are of
course relatively poorer, rather than poor in an absolute sense. Indeed, based on a sample of
borrowers in the Family Resources Survey, we estimate that the median parental earnings
of these groups is around £77,000 for the richer group and around £26,000 for the rest
(2018 prices).
Clearly our parental income measure is likely to haveissues with measurement; people
from poorer households might borrow the rich maximum, people from richer households
might not borrow the rich maximum, and we are unable to say anything at all about the
roughly 15% of people who attend universitybut choose not to bor row,which is likely to be
weighted towards those from higher income households. Given these measurement issues
– all of which are likely to bias down our estimates – we find considerable differences in
earnings between graduates from richer and relatively less well off family backgrounds.
These differences roughly halve once we condition on subject and institution choices but
remain economically important at around 10%, and are statistically significant. These
socio-economic differences also exist right through the earnings distribution and are larger
at the bottom and top of the earnings distribution, suggesting family wealth is particularly
good at both protecting graduates against very poor outcomes and providing them with
opportunities for very high earnings. The conditional differences grow with age and are
somewhat smaller for Science,Technology,Engineering and Mathematics (STEM) or Law,
Economics and Management/Business (LEM) as compared to other subjects. They are also
particularly pronounced for men from the most selective universities.
These findings are descriptive, but clearly important for policy. Data limitations mean
we are unable to control for: individual-level qualifications;5degree outcomes, such as
completion and degree classification (i.e. grades); progression onto (and timing of) post-
graduate study; and early career occupation choices. These, along with differences in non-
cognitive skills and the networks of those from richer and poorer backgrounds should be
the subject of future research into understanding the drivers of these earnings differences,
and could have important implications for firms, universities and policy.
Finally, we follow Chetty et al. (2017) by estimating ‘social mobility scorecards’,
which measure the extent to which different universities appear to help students from
relatively poorer backgrounds get into the top fifth of graduate earners (specifically the
‘mobility score’ is the probability of a course admitting a poorer student multiplied by
the probability that the student goes on to enter the top fifth of the earnings distribution).
4There were subsequent changes to both tuition fees and student support that took effect from 2006 – see section
III for more detail. These changes do not affectour results, however, as we focus on the first-year borrowingof people
who entered university before 2006.
5The period we are looking at was before the big increase in ‘contextualised admissions’ policies whereby uni-
versities make lower offersto students who had attended certain schools, typically those in poorer neighbourhoods.
This suggests it would be more important to control for individual qualifications for later cohorts.
©2019 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and JohnWiley & Sons Ltd.
To continue reading
Request your trial