Gianna Boero, Robin Naylor, and Jeremy Smith University of Warwick The economic returns to a degree: how great and how varied are they? (Is there are an earnings premium for a first or a 2.1?) Gianna Boero, Robin Naylor, and Jeremy Smith University of Warwick UoH 27th April 2016

Plan of Talk 1. Context 1:. Evidence and Policy. Context 2: Plan of Talk 1. Context 1: Evidence and Policy Context 2: Theory and Interpretation 2. Institutional Arrangements 3. Data and Methodology 4. Results 5. Conclusions and Further Work UoH 27th April 2016

Plan of Talk 1. Context 1:. Evidence and Policy. Importance of HE Plan of Talk 1. Context 1: Evidence and Policy Importance of HE Human Capital, R&D, Economic Growth HE Participation and Labour Supply Socio-economic Mobility/Persistence Political Economy: fees and funding Returns to Education Years of Schooling Qualification Levels Grades Performance HE Policy relevance of estimated returns in UK Dearing Report and evidence from Blundell et al. (2000: PTO) Browne Report UoH 27th April 2016

Average Graduate Premium in UK. Blundell et al. (2000): Average Graduate Premium in UK Blundell et al. (2000): NCDS1958 birth cohort 1991 hourly wage data Estimates where HE is Highest Educational Qualification Rich set of observable characteristics Assumes that: Individuals with different HE do not differ on average in unobservables. Results Graduate Earnings Premium (Relative to control group with 2+ A-levels) 17% Men 37% Women UoH 27th April 2016

Variation around Average. Subject. Higher for Science, Social Science Variation around Average Subject Higher for Science, Social Science (Harkness and Machin, inter alia) Institution Higher for ‘Elite’ HEIs (Chevalier et al., inter al.) Hence Differential Fees (Greenaway and Haynes) Prior Schooling (Naylor and Smith) Cohort Walker and Zhu, 2008 Expansion no effect on average Increased premium in highest quartile (Ability composition effect?) … Degree Class Premia (Not available in NCDS) UoH 27th April 2016

Interpretation of Graduate Earnings Premium. Human Capital Theory Interpretation of Graduate Earnings Premium Human Capital Theory Signalling/Screening/Sorting Theories Statistical Discrimination Short-run only? Employer Learning/Statistical Discrimination UoH 27th April 2016

Jo Johnson, Minister of State for Universities and Science, speaking about BIS’s Green Paper ‘Fulfilling our Potential: Teaching Excellence, Social Mobility and Student Choice’, stated that, “We want to encourage a (grade point average) system which provides greater information to employers about where attainment really lies. It needs to sit alongside, rather than replace, the honours degree classification… But there is a very big band, the 2.1 band. It disguises very considerable differences in attainment. You can be at the top of the band and then be 50 percentage points below and still be getting a 2.1. And students who worked hard should be able to signal to employers that’s what they’ve achieved.” (Cited on BBC News website 06/11/2015.) UoH 27th April 2016

Degree Class Premia:. Evidence from available data. BCS70. LFS Degree Class Premia: Evidence from available data BCS70 LFS USR/HESA GCS Background Classification of Honours Degrees: First Upper Second (>=2.1 => ‘Good’) Lower Second (=<2.2 => ‘Lower’) Third Pass (Non-honours) Classification Rules: Based on: Overall average Papers in class Final exams/coursework Viva Anecdotally: ‘Achieves’ vs ‘Is’! = HKT vs Signalling! UoH 27th April 2016

Estimated log wage premia (BCS70) Good degree premium over Lower   (1) (2) (3) (4) (5) Wages observed in year: 2000 Wages observed at age: 30 Good degree class premium relative to lower degree class 0.078 (0.007) 0.077 (0.008) 0.073 (0.012) 0.071 (0.014) 0.068 (0.019) Lower degree class premium relative to 2+ A-levels 0.119 (0.000) 0.105 (0.001) 0.107 0.103 0.109 Family background No Yes Ability at age 10 Ability at age 5 Non-Cognitive ability at ages 5, 10 Other controls No. of Obs 3046 R2 0.0814 0.0988 0.1029 0.1119 0.1188 Lower degree premium over A-levels Notes: p-values in parentheses. Ability controls include: BAS (verbal), BAS (numerical). Background controls include: parental income, parental social class, mother’s interest in education, father’s interest in education, mother’s education, father’s education. Other controls include: region (aged 10), gender, marital status and number of children, ethnicity. UoH 27th April 2016

Estimated log wage premia (LFS): selected birth cohorts in 1969-1971 Wages observed at: 2005-2012 Wages observed at age: 36-41 Good degree class premium (relative to lower degree class) 0.087 (0.001)   Lower degree class 0.188 (relative to 2+ A-levels) (0.000) Other controls Yes No. of Obs 2930 R2 0.152 UoH 27th April 2016

Estimated log-earnings premia (USR91, graduate cohort), birth cohort 1969-1971 USR-FDS: First Destination Median Occupational earnings Note: p-values in parentheses. Ability controls include: pre-University qualifications. Background controls include: social class of parents, school-type. Other controls include: gender, marital status, University attended and type of degree course. Earnings observed at: 1992 Earnings observed at age: 21-23 Good degree class premium relative to lower degree class 0.046 (0.000) 0.043 Ability and background controls No Yes Other controls No. of Obs. 22,459 R2 0.334 0.336 UoH 27th April 2016

Estimated log-wage premia (GCS1990, graduate): birth cohort 1968-1970 Estimated log-wage premia (GCS1990, graduate): birth cohort 1968-1970 Note: p-values in parentheses. Ability controls include pre-university qualifications, background controls include parental education, and other controls include age, gender, ethnicity, and marital status.   (1) (2) (3) (4) Wages observed at: 1991 1996 Wages observed at age 21-23 26-28 Good degree class premium relative to lower degree class 0.051 (0.014) 0.049 0.084 0.079 Ability and background controls No Yes Other controls No. of Obs 2839 3652 R2 0.127 0.131 0.115 0.119 UoH 27th April 2016

UoH 27th April 2016

Plan of Talk 1. Context 1:. Evidence and Policy. Context 2: Plan of Talk 1. Context 1: Evidence and Policy Context 2: Theory and Interpretation How might we interpret evidence of a premium by class of degree awarded? Why might any premium by degree class change across cohorts? UoH 27th April 2016

Hypothesis 1. Pay. But if Econometrician. observes only Degree Hypothesis 1 Pay But if Econometrician observes only Degree Class, then there appears to be a Premium by Class: might wrongly interpret this as a discontinuity. Average Mark UoH 27th April 2016

A Regression Discontinuity framework offers the prospect of being able to distinguish between the two hypotheses – but requires us to observe both degree classification and underlying marks. Hypothesis 2 Pay Employer regards Degree Class as a Signal of some dimension of ability. A discontinuity would be I indicative of signalling or statistical discrimination in the sense of the EL-SD a approach Average Mark UoH 27th April 2016

Regression discontinuity and degree class effects Also see: Regression discontinuity and degree class effects Also see: di Pietro (2012); Feng and Graetz (2015) We use individual student data on an anonymous university located somewhere near the centre of England… Anonymised DLHE returns for graduate cohorts of 2011/12, 2012/13, 2013/14. Matched by personal id to extensive individual student records. Data include: Age, gender, nationality, fees status, course, department, previous schooling, family background, degree class, marks per module per year. Labour market outcome, 5-digit SOC, SIC, salary, degree class, location… UoH 27th April 2016

3. Data and Methodology To date, we have data only for individuals who have responded to the DLHE in each cohort – we are waiting student records on all students in order to establish the extent to which DLHE respondents might differ in observable characteristics from non-respondents. The DLHE response rate is approximately 63%. Results to be presented today exploit data on graduates who are in full-time employment and have provided Research-Accessible personal salary information. The usable response rate to the salary question is approximately 41%. UoH 27th April 2016

Population of All Leavers n= 25950 DLHE Respondents n= 16360 (63%) Online/Post n= 6751 (41%) Telephone n= 9609 (59%) PG n= 3515(52%) UG n= 3265 (48%) FT-Employed n= 1691 (52%) Further Study n= 931 (29%) OLFU n= 6751 (19%) Salary Data n= 1404 (83%) UoH 27th April 2016

UG Students. Degree Class breakdown by:   UG Students. Degree Class breakdown by: All DLHE Respondents, FT-Emp, Salary Info Degree DLHE In FT Salary Class Resp Emp Info -------------------------------------------------- 1st 35% 34% 35% 2:1 53% 54% 53% 2:2 11% 11% 11% 3rd 2% 2% 2% n 3265 1691 1404 UoH 27th April 2016

UG n= 3265 UG HEU+3/4-yr+08-11start n= 2791 FT-Employed n= 1691 (52%) Salary Data n= 1404 (83%) Salary Data n= 1292 (85%) Analysis will be based on a sample which excludes OS students, any students starting later than 11/12 and any students on UG degrees other than 3 or 4 year duration. UoH 27th April 2016

Probability of receiving Treatment (= Good degree) Cut-off at 60 All UGs in responding to DLHE online/post: (except: OS, course duration ~=3|4) Scatter Plot: Bins=30 Based on n=2705 UoH 27th April 2016

Probability of receiving Treatment (= First) Cut-off at 70 All UGs in responding to DLHE online/post: (except: OS, course duration ~=3|4) Scatter Plot: Bins=30 n=2705 UoH 27th April 2016

Compliers and Non-compliers All: 2.1 versus 2.2 n=1707: Compliers=92% All: 1st versus 2.1 n=2422: Compliers=89%   <60 >=60 2.2 281 2 2.1 136 1288   <70 >=70 2.1 1422 2 1 272 726 Science: 2.1 versus 2.2 n=751 : Compliers=94% Science: 1st versus 2.1 n=1091 : Compliers=90%   <60 >=60 2.2 201 1 2.1 46 503   <70 >=70 2.1 548 1 107 435 UoH 27th April 2016

Density of log(annual pay)) UoH 27th April 2016

See Calonico, Catteneo and Titunik (2014) Data-driven RD plots See Calonico, Catteneo and Titunik (2014) Bin scatter plot of log(annual pay) versus Overall Average Mark All Cut-off 60 n=1193 UoH 27th April 2016

See Calonico, Catteneo and Titunik (2014) Data-driven RD plots See Calonico, Catteneo and Titunik (2014) Bin scatter plot of log(annual pay) versus Overall Average Mark Science Cut-off 60 n=646 UoH 27th April 2016

See Calonico, Catteneo and Titunik (2014) Data-driven RD plots See Calonico, Catteneo and Titunik (2014) Bin scatter plot of log(annual pay) versus Overall Average Mark IMSE-optimal evenly spaced method using polynomial regression (espr) All Cut-off 70 n=1193 UoH 27th April 2016

See Calonico, Catteneo and Titunik (2014) Data-driven RD plots See Calonico, Catteneo and Titunik (2014) Bin scatter plot of log(annual pay) versus Overall Average Mark IMSE-optimal quantile-spaced method using spacings estimators (qs) Science Cut-off 70 n=646 UoH 27th April 2016

Continuity (Gender) (RDPLOT) All P(2) UoH 27th April 2016

Continuity (FT-Employment) Cut-off 60 (RDPLOT) ALL P(4) UoH 27th April 2016

Continuity (FT-Employment) Cut-off 70 (RDPLOT) ALL P(4) UoH 27th April 2016

Continuity (FS) Cut-off 70 (RDPLOT) ALL P(4) UoH 27th April 2016

3. Data and Methodology Issue of manipulation/precision of control over assignment variable Student manipulation: Those on track for ‘borderline’ after Year 2 work hard to achieve 2.1, those below borderline reduce effort. So we’d see a trough in the density distribution. Note: manipulation of overall average much harder than of a single module Marker manipulation: Eg practice of avoiding ‘9s’ Exam Board manipulation: If Board uses ‘unobservable’ knowledge insight/rules/discretion which correctly assign individuals to treatment/control groups. “This person is a 2.1” UoH 27th April 2016

Density of Overall Average Mark All n=3210 UoH 27th April 2016

Plan of Talk 1. Context 1:. Evidence and Policy. Context 2: Plan of Talk 1. Context 1: Evidence and Policy Context 2: Theory and Interpretation 2. Institutional Arrangements 3. Data and Methodology 4. Results 5. Conclusions and Further Work UoH 27th April 2016

IV (2SLS): log(annual pay) on 2:1 Treatment ALL (2:1 vs 2:2) (Same as previous estimation: but excluding cases of 3rds/1sts in borderlines) Number of obs = 731 F( 6, 724) = 10.89 Prob > F = 0.0000 Total (centered) SS = 113.6869721 Centered R2 = 0.0796 Total (uncentered) SS = 73125.38036 Uncentered R2 = 0.9986 Residual SS = 104.6405362 Root MSE = .3783   ------------------------------------------------------------------------------- | Robust lannualpay | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- Upper Second | .1453312 .1657892 0.88 0.381 -.1796097 .4702722 overall_ave60 | .0027592 .0099878 0.28 0.782 -.0168166 .022335 female | -.1942985 .0288996 -6.72 0.000 -.2509408 -.1376563 startyr_e | -.0938844 .0470748 -1.99 0.046 -.1861493 -.0016194 startyr_1 | .0648903 .0445951 1.46 0.146 -.0225145 .1522952 startyr_3 | -.0514248 .045259 -1.14 0.256 -.1401308 .0372811 _cons | 10.01462 .1180723 84.82 0.000 9.783199 10.24603 So the cases of 1sts in the top borderline was driving an apparent effect of 2:1s (given a discontinuity around 70). 48=< Overall Average Mark <70 UoH 27th April 2016

IV (2SLS): log(annual pay) on 2:1 Treatment Science (2:1 vs 2:2) Second Stage regression Number of obs = 345 F( 3, 341) = 9.42 Prob > F = 0.0000 Total (centered) SS = 44.13720296 Centered R2 = 0.0855 Total (uncentered) SS = 34613.63128 Uncentered R2 = 0.9988 Residual SS = 40.36358091 Root MSE = .342   ------------------------------------------------------------------------------ | Robust lannualpay | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Upper Second | .2742169 .1543015 1.78 0.076 -.0282084 .5766422 overall_ave | -.0067806 .0109202 -0.62 0.535 -.0281838 .0146226 female | -.1356737 .0379965 -3.57 0.000 -.2101455 -.061202 _cons | 10.26969 .5676385 18.09 0.000 9.157143 11.38224 For First Stage regression see next slide 48=< Overall Average Mark <70 UoH 27th April 2016

IV (2SLS): log(annual pay) on 1st Class Degree Treatment ALL (1st vs 2:1) Number of obs = 1047 F( 9, 1037) = 25.15 Prob > F = 0.0000 Total (centered) SS = 165.4780731 Centered R2 = 0.1656 Total (uncentered) SS = 106057.3973 Uncentered R2 = 0.9987 Residual SS = 138.081668 Root MSE = .3632   -------------------------------------------------------------------------------- | Robust lannualpay | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- first | .1586433 .0730816 2.17 0.030 .0154059 .3018806 overall_ave70 | -.018867 .0116393 -1.62 0.105 -.0416795 .0039456 overall_ave70*d| .0428893 .0189292 2.27 0.023 .0057886 .0799899 overall_ave702 | -.0008714 .0005818 -1.50 0.134 -.0020117 .0002689 female | -.1295679 .0245841 -5.27 0.000 -.1777518 -.081384 sci | .2427035 .0340108 7.14 0.000 .1760436 .3093634 ssci | .3220823 .0371857 8.66 0.000 .2491997 .394965 startyr_3 | -.0268086 .0243803 -1.10 0.272 -.0745931 .020976 startyr_4 | .0312276 .036926 0.85 0.398 -.041146 .1036011 _cons | 9.774553 .0701673 139.30 0.000 9.637027 9.912078 58=< Overall Average Mark UoH 27th April 2016

IV (2SLS): log(annual pay) on 1st Treatment Science (1st vs 2:1) Number of obs = 540 F( 6, 533) = 10.93 Prob > F = 0.0000 Total (centered) SS = 60.61066027 Centered R2 = 0.0979 Total (uncentered) SS = 55388.29596 Uncentered R2 = 0.9990 Residual SS = 54.67445708 Root MSE = .3182   -------------------------------------------------------------------------------- | Robust lannualpay | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- first | .2181923 .1039298 2.10 0.036 .0144936 .4218909 overall_ave70 | -.022705 .0135287 -1.68 0.093 -.0492208 .0038107 overall_ave70*d| .0378093 .0197661 1.91 0.056 -.0009314 .0765501 overall_ave702 | -.0003334 .0005811 -0.57 0.566 -.0014724 .0008056 female | -.139969 .0302722 -4.62 0.000 -.1993014 -.0806366 startyr_e | .0488098 .0276974 1.76 0.078 -.0054761 .1030957 _cons | 9.942504 .0871684 114.06 0.000 9.771657 10.11335 Consistent with RD for 60 < Overall Average Mark < 70 58=< Overall Average Mark UoH 27th April 2016

5. Conclusions and Further Work (i) 5. Conclusions and Further Work (i) (Fuzzy) RD to estimate causal effect of degree class on earnings (ii) Evidence of Signalling in early careers Upper Second Class Premium For Science only: 27% First Class Premium on average: 16% Strongest for Science: 22% (iii) Other findings Large negative female intercept Premia for Science and Social Science over Arts/Humanities (iv) Further Work/Data 4-digit SOC average occupational earnings Telephone Responses More cohorts 3 ½ year follow-up HMRC link => Non-parametric results UoH 27th April 2016