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Licensed Unlicensed Requires Authentication Published by De Gruyter October 22, 2020

Charging for Higher Education: Estimating the Impact on Inequality and Student Outcomes

  • Ghazala Azmat EMAIL logo and Ştefania Simion
From the journal The B.E. Journal of Economic Analysis & Policy
https://doi.org/10.1515/bejeap-2020-0099

Abstract

Over the last two decades, undergraduate university education in England moved from being state-funded and free for students, to costing all students substantial amounts in tuition fees. In this paper, using detailed administrative longitudinal microdata that follow all students attending state schools in England (approximately 95% of student population), we causally show that, despite the substantial reforms, enrollment fell only by 0.5 percentage points, where the effect is largely borne by those in wealthier groups, reducing the enrolment gap across socio-economic groups. Since tuition fees were introduced in conjunction with the government offering generous means-tested maintenance (cash) grants, as well as loans, our results highlight the importance of reducing financing constraints. Beyond enrollment, we find that the reforms have limited impact on students’ higher education choices, such as relocation decisions, university choice, and field of study. Finally, by tracking the students after graduation, we show similarly small effects on labor market outcomes.

Keywords: higher education; inequality; tuition fees; means-tested support; career outcomes
JEL codes: I22; I23; I29; J30
Corresponding author: Ghazala Azmat, Sciences Po and CEP (LSE), Paris, France, E-mail:

We would like to thank participants of the FEDEA workshop on Higher Education Financing, UCD Workshop on Economics of Education, CEPR Higher Education Workshop, Royal Holloway University of London Workshop on Education and Expectations, seminar participants at CEP (LSE), CREST, OFCE (Sciences Po), Rotterdam University, Stockholm University, Uppsala University, York University, DIW (Berlin), BGSE Economics of Education Summer School, ERMAS 2018, EALE 2018, Moray House School of Education University of Edinburgh, Dauphine University, OECD Paris, HEC Paris, University of Montpellier, Sorbonne – University Paris 1, University of Dundee, ESPE 2019, EEA/ESEM 2019, University of Bristol for comments and suggestions. All errors are our own.

Appendix A

Table A.1:

Robustness check – university enrollment.

AllAllAllAllAllAll
[1][2][3][4][5][6]
2006 HE reform0.004***−0.016***−0.010***−0.003**0.004***−0.003***
(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)
Trend0.007***0.0000.014***
(0.000)(0.001)(0.001)
Trend squared0.001***−0.008***
(0.000)(0.001)
Trend cubic0.001***
(0.000)
Ln (cohort size)0.005
(0.013)
2nd Tercile of Ln (cohort size)0.014***
(0.001)
3rd Tercile of Ln (cohort size)−0.000
(0.001)
Constant0.235***0.232***0.234***0.231***0.1710.235***
(0.000)(0.000)(0.001)(0.001)(0.174)(0.000)
Observations2,816,2552,816,2552,816,2552,816,2552,816,2552,816,255
R-squared0.0000.0000.0000.0000.0000.000

  1. The outcome is a categorical variable equal to 1 if the student is enrolled into an English university at age 18 and 0 otherwise. The regressions refer to the 2006 reform and include data on 1st year UG students enrolled between 2004/2005 and 2009/2010. *Denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level. Robust standard errors clustered at the school enrolled (at age 16) level in parentheses.

Table A.2:

Robustness check – university enrollment – five socio-economic categories.

AllAllAll
[1][2][3]
2006 HE reform−0.004***−0.016***−0.016***
(0.001)(0.002)(0.002)
2006 HE reform°×°2nd quintile0.007***0.007***
(0.002)(0.002)
2006 HE reform°×°3rd quintile0.012***0.011***
(0.002)(0.001)
2006 HE reform°×°4th quintile0.018***0.017***
(0.002)(0.001)
2006 HE reform°×°5th quintile0.021***0.019***
(0.002)(0.002)
2nd Quintile0.004−0.001−0.012*
(0.008)(0.009)(0.006)
3rd Quintile0.001−0.007−0.014*
(0.011)(0.011)(0.008)
4th Quintile−0.010−0.022*−0.027***
(0.013)(0.013)(0.009)
5th Quintile−0.011−0.024*−0.028***
(0.014)(0.014)(0.010)
Trend−0.012***−0.012***−0.013***
(0.001)(0.001)(0.001)
Trend squared0.003***0.003***0.002***
(0.000)(0.000)(0.000)
Ln (cohort size)0.246***0.246***0.297***
(0.021)(0.021)(0.017)
ControlsYesYesYes
Education controlsNoNoYes
Neighborhood FEYesYesYes
School FEYesYesYes
Observations2,815,5422,815,5422,815,531
R-squared0.1930.1930.524

  1. The outcome is a categorical variable equal to 1 if the student is enrolled into an English university at age 18 and 0 otherwise. The regressions refer to the 2006 reform and include data on 1st year UG students enrolled between 2004/2005 and 2009/2010. The neighborhood FE are defined using the lower layer super output are where the student was residing at age 16 (we use around 32,400 regions). The school FE are defined as the school attended by the student at age 16, when they sat the GCSEs. The controls are female and white categorical variables. The education controls are the number of full GCSE taken, the number of A* marks in GCSE, the number of A marks in GCSE, the number of B marks in GCSE, the number of C marks in GCSE and the number of D marks in GCSE. *Denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level. Robust standard errors clustered at the school enrolled (at age 16) level in parentheses.

Table A.3:

Robustness check – university enrollment – localized effect.

AllAllAll
[1][2][3]
2006 HE reform−0.002**−0.012***−0.009***
(0.001)(0.002)(0.001)
2006 HE reform × medium SES0.013***0.007***
(0.002)(0.002)
2006 HE reform × low SES0.016***0.010***
(0.002)(0.002)
Medium SES−0.074−0.083*−0.081***
(0.047)(0.047)(0.030)
Low SES−0.065−0.081−0.061*
(0.050)(0.050)(0.033)
ControlsYesYesYes
Education controlsNoNoYes
Neighborhood FEYesYesYes
School FEYesYesYes
Observations947,920947,920947,909
R-squared0.2210.2210.549

  1. The outcome is a categorical variable equal to 1 if the student is enrolled into an English university at age 18 and 0 otherwise. The first three regressions refer to the 2006 reform and include data on 1st year UG students enrolled between 2005/2006 and 2006/2007. The neighborhood FE are defined using the lower layer super output are where the student was residing at age 16 (we use around 32,400 regions). The school FE are defined as the school attended by the student at age 16, when they sat the GCSEs. The controls are female and white categorical variables. The education controls are the number of full GCSE taken, the number of A* marks in GCSE, the number of A marks in GCSE, the number of B marks in GCSE, the number of C marks in GCSE and the number of D marks in GCSE. *Denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level. Robust standard errors clustered at the school enrolled (at age 16) level in parentheses.

Table A.4:

Robustness check – university enrollment – free school meal.

AllAllAll
[1][2][3]
2006 HE reform−0.004***−0.006***−0.006***
(0.001)(0.001)(0.001)
2006 HE reform × FSM0.012***0.011***
(0.001)(0.001)
FSM−0.081***−0.089***−0.013***
(0.001)(0.001)(0.001)
Trend−0.012***−0.012***−0.013***
(0.001)(0.001)(0.001)
Trend squared0.003***0.003***0.002***
(0.000)(0.000)(0.000)
Ln (cohort size)0.245***0.245***0.299***
(0.021)(0.021)(0.017)
ControlsYesYesYes
Education controlsNoNoYes
Neighborhood FEYesYesYes
School FEYesYesYes
Observations2,814,9492,814,9492,814,938
R-squared0.1960.1960.524

  1. The outcome is a categorical variable equal to 1 if the student is enrolled into an English university at age 18 and 0 otherwise. The regressions refer to the 2006 reform and include data on 1st year UG students enrolled between 2004/2005 and 2009/2010. The neighborhood FE are defined using the lower layer super output are where the student was residing at age 16 (we use around 32,400 regions). The school FE are defined as the school attended by the student at age 16, when they sat the GCSEs. The controls are female and white categorical variables. The education controls are the number of full GCSE taken, the number of A* marks in GCSE, the number of A marks in GCSE, the number of B marks in GCSE, the number of C marks in GCSE and the number of D marks in GCSE. *Denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level. Robust standard errors clustered at the school enrolled (at age 16) level in parentheses.

Table A.5:

Robustness check – university enrollment – no London universities.

Pr(enroll into uni)Ln(distance btw home and uni)Pr(same commuting area)
[1][2][3][4][5][6][7][8][9]
2006 HE reform−0.005***−0.015***−0.014***−0.030***−0.031***−0.041***0.008***0.007***0.009***
(0.001)(0.002)(0.001)(0.008)(0.009)(0.008)(0.002)(0.003)(0.003)
2006 HE reform × medium SES0.011***0.009***−0.0040.0020.004*
(0.001)(0.001)(0.007)(0.007)(0.002)(0.002)
2006 HE reform × low SES0.017***0.016***0.0130.027***−0.001−0.003
(0.001)(0.001)(0.010)(0.009)(0.003)(0.003)
Medium SES−0.007−0.014**−0.013**−0.042−0.040−0.0490.0160.0140.016
(0.007)(0.007)(0.005)(0.039)(0.039)(0.039)(0.011)(0.012)(0.011)
Low SES−0.010−0.020**−0.015**−0.019−0.026−0.0470.0100.0100.014
(0.009)(0.009)(0.007)(0.068)(0.068)(0.066)(0.020)(0.020)(0.020)
Trend−0.011***−0.011***−0.012***0.0100.010−0.000−0.001−0.0010.001
(0.001)(0.001)(0.001)(0.008)(0.008)(0.008)(0.002)(0.002)(0.002)
Trend squared0.003***0.003***0.002***0.0000.0000.002−0.001−0.001−0.001**
(0.000)(0.000)(0.000)(0.001)(0.001)(0.001)(0.000)(0.000)(0.000)
Ln (cohort size)0.236***0.236***0.280***−0.095−0.0950.110−0.045−0.045−0.080**
(0.021)(0.021)(0.017)(0.126)(0.126)(0.125)(0.037)(0.037)(0.037)
ControlsYesYesYesYesYesYesYesYesYes
Education controlsNoNoYesNoNoYesNoNoYes
Neighborhood FEYesYesYesYesYesYesYesYesYes
School FEYesYesYesYesYesYesYesYesYes
Observations2,748,3972,748,3972,748,386602,691602,691602,691602,691602,691602,691
R-squared0.1860.1860.5160.3440.3440.3740.3520.3520.362

  1. The outcome in the first three columns is a categorical variable equal to 1 if the student is enrolled into an English university at age 18 and 0 otherwise. The outcome in columns [4] to [6] is the geographical distance between the student’s home measured at age 16 and the university enrolled in at age 18, expressed in km. The outcome in columns [7] to [9] is a categorical variable equal to 1 if the student is enrolled into a university located in the same commuting area as their residency at age 16. The regressions refer to the 2006 reform and include data on 1st year UG students enrolled between 2004/2005 and 2009/2010. The regressions do not include any London based university. The neighborhood FE are defined using the lower layer super output are where the student was residing at age 16 (we use around 32,400 regions). The school FE are defined as the school attended by the student at age 16, when they sat the GCSEs. The controls are female and white categorical variables. The education controls are the number of full GCSE taken, the number of A* marks in GCSE, the number of A marks in GCSE, the number of B marks in GCSE, the number of C marks in GCSE and the number of D marks in GCSE. *Denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level. Robust standard errors clustered at the school enrolled (at age 16) level in parentheses.

Table A.6:

Robustness check – probability to pursue a field of study.

MedicineSTEMSocial sciencesLanguagesArts and education
[1][2][3][4][5][6][7][8][9][10]
2006 HE reform0.001−0.0040.0020.006**0.0010.006*0.001−0.003−0.005**−0.005**
(0.003)(0.003)(0.003)(0.003)(0.003)(0.003)(0.002)(0.002)(0.002)(0.003)
2006 HE reform × medium SES0.005**−0.005**−0.008***0.005**0.002
(0.003)(0.002)(0.003)(0.002)(0.002)
2006 HE reform × low SES0.013***−0.012***−0.007**0.004*0.002
(0.003)(0.003)(0.003)(0.002)(0.003)
Medium SES0.026*0.023−0.032**−0.030**−0.0020.0040.002−0.0020.0050.006
(0.016)(0.016)(0.013)(0.013)(0.016)(0.016)(0.012)(0.012)(0.014)(0.014)
Low SES0.054**0.045*−0.030−0.027−0.034−0.0250.0140.009−0.005−0.003
(0.025)(0.026)(0.022)(0.022)(0.027)(0.027)(0.018)(0.018)(0.020)(0.020)
Trend0.016***0.014***0.0030.0000.0050.007**−0.014***−0.017***−0.009***−0.006**
(0.003)(0.003)(0.003)(0.003)(0.003)(0.003)(0.002)(0.002)(0.002)(0.002)
Trend squared−0.002***−0.002***−0.000−0.000−0.000−0.0010.002***0.002***0.001***0.001***
(0.000)(0.000)(0.000)(0.000)(0.001)(0.001)(0.000)(0.000)(0.000)(0.000)
Ln (cohort size)−0.153***−0.125***−0.117***−0.085**−0.346***−0.383***0.301***0.342***0.315***0.251***
(0.045)(0.045)(0.040)(0.040)(0.048)(0.048)(0.033)(0.033)(0.035)(0.035)
ControlsYesYesYesYesYesYesYesYesYesYes
Education controlsNoYesNoYesNoYesNoYesNoYes
Neighborhood FEYesYesYesYesYesYesYesYesYesYes
School FEYesYesYesYesYesYesYesYesYesYes
Observations669,924669,924669,924669,924669,924669,924669,924669,924669,924669,924
R-squared0.0810.0840.1470.1550.0740.0870.0800.0950.0860.109

  1. The outcome is a categorical variable equal to 1 if the student is pursuing a specific field of study 0 otherwise. The outcome in the first two columns is the probability to pursue a degree in Medicine, Dentistry or allied subjects. The outcome in columns [3] and [4] is the probability to pursue a degree in STEM. The outcome in columns [5] and [6] is the probability to pursue a degree in Social Sciences. The outcome variable in columns [7] and [8] is the probability to pursue a degree in Languages, while the outcome variable in columns [9] and [10] is the probability to pursue a degree in Arts or Education. For a detailed description of each field of study see Table C.2 in Appendix C. The regressions refer to the 2006 reform and include data on 1st year UG students enrolled between 2004/2005 and 2009/2010. The neighborhood FE are defined using the lower layer super output are where the student was residing at age 16 (we use around 32,400 regions). The school FE are defined as the school attended by the student at age 16, when they sat the GCSEs. The controls are female and white categorical variables. The education controls are the number of full GCSE taken, the number of A* marks in GCSE, the number of A marks in GCSE, the number of B marks in GCSE, the number of C marks in GCSE and the number of D marks in GCSE. *Denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level. Robust standard errors clustered at the school enrolled (at age 16) level in parentheses.

Appendix B

Table B.1:

Definition of the eligibility for maintenance grants by year and family income.

YearFull maintenance grantPartial maintenance grantNo maintenance grant
2004≤£15,000£15,001–£22,500>£22,500
2005≤£15,000£15,001–£22,500>£22,500
2006£17,500£17,501–£37,425>£37,425
2007≤£17,910£17,911–£38,330>£38,330
2008≤£25,000£25,001–£60,005>£60,005
2009£25,000£25,001–£60,005>£60,005

  1. The grouping is based on the information provided by The Student Loan Company (available at http://www.slc.co.uk/official-statistics/full-catalogue-of-official-statistics/student-support-for-higher-education-in-england.aspx) and in Dearden et al. (2014).

Appendix C GCSE

For the period under analysis, the grading system of the GCSEs changed. Based on the information provided by Ofsted and Ofqual, the following scales were used in the calculation of the grades obtained in the GCSE in English and in Maths:

Table C.1:

Grading system GCSEs.

Panel A: Single awards
GradeA*ABCDEFG
Old points (before 2004)87654321
New points (2004 onwards)5852464034282216
Panel B: Double awards
GradeA*A*A*AAAABBBBCCCCDDDDEEEEFFFFGGG
New points (2008 onwards)585552494643403734312825221916

  1. Double Award GCSE subjects are certificated on a fifteen-point scale for the first time in the June 2008 examination. For the Double Awards, the grade is recorded twice on the certificate to indicate that the results in these specifications have the same status as GCSE grades in two other single-certificate subjects. Source Ofsted, Ofqual.

Undergraduate Degree Definition

The undergraduate students who represent the student population considered in this analysis are formed or two categories of students: first degree and other undergraduate degree. According to HESA, the First degree includes first degrees with or without eligibility to register to practice with a Health or Social Care or Veterinary statutory regulatory body, first degrees with qualified teacher status (QTS)/registration with the General Teaching Council (GTC), enhanced first degrees, first degrees obtained concurrently with a diploma and intercalated first degrees. Other undergraduate includes qualification aims below degree level such as Foundation Degrees, diplomas in HE with eligibility to register to practice with a Health or Social Care regulatory body, Higher National Diploma (HND), Higher National Certificate (HNC), Diploma of Higher Education (DipHE), Certificate of Higher Education (CertHE), foundation courses at HE level, NVQ/SVQ levels 4 and 5, post-degree diplomas and certificates at undergraduate level, professional qualifications at undergraduate level, other undergraduate diplomas and certificates including post registration health and social care courses, other formal HE qualifications of less than degree standard, institutional undergraduate credit and no formal undergraduate qualifications. The coding also accounts for the mapping between the old and the new codes which was introduced in 2007/2008[28].

Field of Study

In the HESA data there are 20 major field of study pursued at higher education level, but we group the fields of study in five groups as below in order to increase precision.

Table C.2:

Coding of field of study.

JACS subject groupsFive subject groups
Medicine and DentistryMedicine, Dentistry and Allied Subjects
Other Medical SubjectsMedicine, Dentistry and Allied Subjects
Biological SciencesMedicine, Dentistry and Allied Subjects
Veterinary Sciences and AgricultureMedicine, Dentistry and Allied Subjects
Physical SciencesSTEM
Maths and Computer SciencesSTEM
EngineeringSTEM
TechnologySTEM
Architecture, Building and PlanningSTEM
Social SciencesSocial Sciences
LawSocial Sciences
Business and AdministrationSocial Sciences
Mass Communication and DocumentationLanguages and History
Linguistics and ClassicsLanguages and History
European LanguagesLanguages and History
Modern LanguagesLanguages and History
History and Philosophical StudiesLanguages and History
Creative Arts and DesignArts, Education, other
EducationArts, Education, other
CombinedArts, Education, other

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Received: 2020-04-06
Accepted: 2020-10-05
Published Online: 2020-10-22

© 2020 Walter de Gruyter GmbH, Berlin/Boston

From the journal
The B.E. Journal of Economic Analysis & Policy
The B.E. Journal of Economic Analysis & Policy
Volume 21 Issue 1

Journal and Issue

Articles in the same Issue

Frontmatter
Incumbent and Entrant Bidding in Scoring Rule Auctions: A Study on Italian Canteen Services
Did Right-To-Work Laws Impact Income Inequality? Evidence from U.S. States Using the Synthetic Control Method
Sometimes Your Best Just Ain’t Good Enough: The Worldwide Evidence on Subjective Well-being Efficiency
On the Intergenerational Transmission of STEM Education among Graduate Students
The Effect of Health Insurance on Young Adult HIV Testing: Evidence from the Affordable Care Act Dependent Coverage Mandate
Charging for Higher Education: Estimating the Impact on Inequality and Student Outcomes
New Evidence of the Effect of Beijing’s Driving Restriction and Other Olympic-Year Policies on Air Pollution
Classroom Composition and Student Academic Achievement: The Impact of Peers’ Parental Education
The Effects of the State Prepaid Tuition Program on College Attainment
Changes Over Time in the Cost of Job Loss for Young Men and Women
Is Environmental Tax Harmonization Desirable in Global Value Chains?
The Economic Impact of State Splitting in Brazil
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