1. 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

2. 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

3. 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

4. Average Graduate Premium in UK. Blundell et al. (2000):
Average Graduate Premium in UK Blundell et al. (2000): NCDS1958 birth cohort 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) % Men 37% Women
UoH 27th April 2016

5. 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, Expansion no effect on average Increased premium in highest quartile (Ability composition effect?) … Degree Class Premia (Not available in NCDS)
UoH 27th April 2016

6. 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
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7. 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

8. Degree Class Premia:. Evidence from available data. BCS70. LFS
Degree Class Premia: Evidence from available data BCS 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

9. 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

10. Estimated log wage premia (LFS): selected birth cohorts in 1969-1971
Wages observed at:
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

11. Estimated log-earnings premia (USR91, graduate cohort), birth cohort 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

12. Estimated log-wage premia (GCS1990, graduate): birth cohort 1968-1970
Estimated log-wage premia (GCS1990, graduate): birth cohort 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

13. UoH 27th April 2016

14. 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

15. 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

16. 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

17. 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

18. 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

19. Population of All Leavers n= 25950
DLHE Respondents
n= (63%)
Online/Post
n= (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%)
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20. 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 % 34% %
2: % 54% %
2: % 11% %
3rd % 2% % n
UoH 27th April 2016

21. 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

22. 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

23. 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

24. 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

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

26. 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

27. 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

28. 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

29. 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

30. Continuity (Gender)
(RDPLOT)
All P(2)
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31. Continuity (FT-Employment) Cut-off 60 (RDPLOT) ALL P(4)
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32. Continuity (FT-Employment) Cut-off 70 (RDPLOT) ALL P(4)
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33. Continuity (FS)
Cut-off 70
(RDPLOT)
ALL P(4)
UoH 27th April 2016

34. 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

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

36. 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

37. 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 =
F( 6, 724) =
Prob > F =
Total (centered) SS = Centered R2 =
Total (uncentered) SS = Uncentered R2 =
Residual SS = Root MSE =
| Robust
lannualpay | Coef. Std. Err z P>|z| [95% Conf. Interval]
Upper Second |
overall_ave60 |
female |
startyr_e |
startyr_1 |
startyr_3 |
_cons |
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

38. IV (2SLS): log(annual pay) on 2:1 Treatment Science (2:1 vs 2:2)
Second Stage regression
Number of obs =
F( 3, 341) =
Prob > F =
Total (centered) SS = Centered R2 =
Total (uncentered) SS = Uncentered R2 =
Residual SS = Root MSE =
| Robust
lannualpay | Coef. Std. Err z P>|z| [95% Conf. Interval]
Upper Second |
overall_ave |
female |
_cons |
For First Stage regression see next slide
48=< Overall Average Mark <70
UoH 27th April 2016

39. IV (2SLS): log(annual pay) on 1st Class Degree Treatment
ALL (1st vs 2:1)
Number of obs =
F( 9, 1037) =
Prob > F =
Total (centered) SS = Centered R2 =
Total (uncentered) SS = Uncentered R2 =
Residual SS = Root MSE =
| Robust
lannualpay | Coef. Std. Err z P>|z| [95% Conf. Interval]
first |
overall_ave70 |
overall_ave70*d|
overall_ave702 |
female |
sci |
ssci |
startyr_3 |
startyr_4 |
_cons |
58=< Overall Average Mark
UoH 27th April 2016

40. IV (2SLS): log(annual pay) on 1st Treatment Science (1st vs 2:1)
Number of obs =
F( 6, 533) =
Prob > F =
Total (centered) SS = Centered R2 =
Total (uncentered) SS = Uncentered R2 =
Residual SS = Root MSE =
| Robust
lannualpay | Coef. Std. Err z P>|z| [95% Conf. Interval]
first |
overall_ave70 |
overall_ave70*d|
overall_ave702 |
female |
startyr_e |
_cons |
Consistent with RD for 60 < Overall Average Mark < 70
58=< Overall Average Mark
UoH 27th April 2016

41. 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