1. The economic returns to a degree: how great and how varied are they? (And what might they tell us?) Gianna Boero, Robin Naylor, and Jeremy Smith University of Warwick UEA 7th March 20161

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 UEA 7th March 20162

3. 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 UEA 7th March 20163

4. 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 UEA 7th March 20164

5. Variation around Average SubjectHigher for Science, Social Science (Harkness and Machin, inter alia) InstitutionHigher for ‘Elite’ HEIs (Chevalier et al., inter al.) Hence Differential Fees(Greenaway and Haynes) Prior Schooling(Naylor and Smith) CohortWalker and Zhu, 2008 Expansion no effect on average Increased premium in highest quartile (Ability composition effect?) … Degree Class Premia(Not available in NCDS) Expansion UEA 7th March 20165

6. Interpretation of Graduate Earnings Premium Human Capital Theory Signalling/Screening/Sorting Theories Statistical Discrimination Short-run only? Employer Learning/Statistical Discrimination UEA 7th March 20166

7. How might we interpret any variation in graduate returns by Grades/Performance? Again: HKT vs Signalling Labour market has various ‘pools’ or non-continuities Employers of some types of occupation recruit only from ‘Graduate pool’ Criteria for successful hiring? Cognitive skills Non-cognitive skills Measured by: Degree Class Transcript/HEAR Subject/HEI Interview Internship/experience Are these abilities: Revealed? (US: Arcidiacono) Signalled?(UK: opposite of US?) UEA 7th March 20167

8. 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.) UEA 7th March 20168

9. 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(= ‘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! UEA 7th March 20169

10. Estimated log wage premia (BCS70) UEA 7th March 201610 (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 (0.000) 0.103 (0.001) 0.109 (0.000) Family background NoYes Ability at age 10 No Yes Ability at age 5 No Yes Non-Cognitive ability at ages 5, 10 No Yes Other controls Yes No. of Obs 3046 R2R2 0.08140.09880.10290.11190.1188 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. Good degree premium over Lower Lower degree premium over A- levels

11. Estimated log wage premia (LFS): selected birth cohorts in 1969-1971 UEA 7th March 201611 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 R2R2 0.152

12. 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. UEA 7th March 201612 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 (0.000) Ability and background controlsNoYes Other controlsYes No. of Obs.22,459 R2R2 0.3340.336

13. 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. UEA 7th March 201613 (1)(2)(3)(4) Wages observed at:1991 1996 Wages observed at age21-23 26-28 Good degree class premium relative to lower degree class 0.051 (0.014) 0.049 (0.014) 0.084 (0.014) 0.079 (0.014) Ability and background controlsNoYesNoYes Other controlsYes No. of Obs2839 3652 R2R2 0.1270.1310.1150.119

14. Estimated log-earnings premia (USR; HESA: selected cohorts) and by university type graduates aged 21-23 UEA 7th March 201614

15. UEA 7th March 201615

16. 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? UEA 7th March 201616

17. Hypothesis 1 Pay Average mark ‘reveals’ ability/productivity to Employer and this is rewarded in the labour market. So when we have data on average mark, we estimate an average mark effect… Average Mark UEA 7th March 201617 Contrast with Arcidiacono on US context of high schools vs colleges

18. Hypothesis 1 Pay Average mark ‘reveals’ ability to Employer. If Econometrician also observes this mark, then estimated coefficient reflects ‘true’ effect of ‘ability’ on pay. Average Mark UEA 7th March 201618

19. 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 UEA 7th March 201619

20. Hypothesis 2 Pay Employer regards Degree Class as a Signal of some dimension of ability, over and above the average mark, or does not see (or heed) the average mark. Average Mark UEA 7th March 201620

21. Hypothesis 2 Employer regards Degree Class as a Signal Payof some dimension of ability. But if Econometrician observes only degree class, then we cannot rule out alternative interpretation of (steep) gradient in mark (Hypothesis 1). Average Mark UEA 7th March 201621

22. 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 Iindicative of signalling or statistical discrimination in the sense of the EL-SD aapproach Average Mark UEA 7th March 201622

23. 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 Iindicative of signalling or statistical discrimination in the sense of the EL-SD aapproach Average Mark UEA 7th March 201623

24. Signal-Noise Ratio There might be a difference across subjects in the Signal- Noise Ratio associated with the extent to which the overall average mark indicates ability/productivity. There is some evidence that marks in quantitative subjects have a higher S-NR in terms of cognitive ability. Nature of quantitative material Nature of quantitative tests Labour market value of quantitative skills/abilities UEA 7th March 201624

25. UEA 7th March 201625 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?

26. UEA 7th March 201626 0 1 a f(a) O 30 A 40 L 16 H 14 0.15 1 0.50 0.78 0.93 Ability distribution across broad educational groups; 1995 characterisation.

27. UEA 7th March 201627 0 1 a f(a) O 30 A 40 L 16 H 14 0.15 1 0.50 0.78 0.93 Ability distribution across broad educational groups; 1995 characterisation.

28. UEA 7th March 201628 0 1 a f(a) O 30 A 40 L 16 H 14 0.15 1 0.50 0.78 0.93 Ability distribution across broad educational groups; 1995 characterisation.

29. 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 UEA 7th March 201629

30. 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… UEA 7th March 201630

31. Degree classification Typically, based on performance in modules in years 2 and 3. Overall mark in each module comprises marks in end-of-year examinations and in coursework assessments. All marking is anonymous. Exam boards consider cases anonymously. Each overall module mark is classified as follows: The student’s final degree classification is based on the marks achieved in the individual modules. UEA 7th March 201631

32. Degree classification Historically, the student’s final degree classification is based on two aspects of the module marks: Overall average mark across modules Distribution of module marks by class of mark However, for cohorts entering from 2008, the harmonisation of classification criteria across all departments means that the overall average mark is the main driver of the final classification: In addition, there are rules for ‘borderline’ cases within 2 percentage points of each critical minimum mark. These rules are based on the class distribution of marks, marks in the final year, and marks in core modules. There is also a system of mitigation. UEA 7th March 201632

33. 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 UEA 7th March 201633

34. 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%. UEA 7th March 201634

35. 3.Data and Methodology UEA 7th March 201635 GraduatesPopulationRespondentsResponse rate Total responses by post/online % of responses by post/online 2011/128323531263.82%240445.26% 2012/138839562363.62%267047.48% 2013/14 -------------- Total 8788 ------------- 25950 5425 ------------- 16360 61.73% ------------- 63.04% 1677 ------------- 6751 30.91% ------------- 41.27%

36. UEA 7th March 201636 FT-Employed n= 1691 (52%) Population of All Leavers n= 25950 DLHE Respondents n= 16360 (63%) Telephone n= 9609 (59%) PG n= 3515(52%) UG n= 3265 (48%) Online/Post n= 6751 (41%) Further Study n= 931 (29%) Salary Data n= 1404 (83%) OLFU n= 6751 (19%)

37. UEA 7th March 201637 All 3265 UGs (responding to DLHE online/post) Degree Class Freq. Percent ------------------------------ 1st 1,147 35.13 2:1 1,709 52.34 2:2 358 10.96 3rd 51 1.56 ------------------------------ Total 3,265 100.00

38. UEA 7th March 201638 UG Students. Degree Class breakdown by: All DLHE Respondents, FT-Emp, Salary Info Degree DLHEIn FT Salary ClassRespEmpInfo -------------------------------------------------- 1st 35%34% 35% 2:1 53%54% 53% 2:2 11%11% 11% 3rd 2%2% 2% -------------------------------------------------- n326516911404

39. UEA 7th March 201639 Degree Class breakdown by Faculty ClassArts Science Soc SciTotal 1 st 268596 283 1147 37%41% 26%35% 2:1424604 6811709 59%42% 62%52% 2:228208 122358 4%14%11%11% 3 rd 046551 0% 3% 0%2% Total7201454 10913265

40. UEA 7th March 201640 FT-Employed n= 1691 (52%) UG n= 3265 Salary Data n= 1404 (83%) UG HEU+3/4-yr+08-11start n= 2791 FT-Employed n= 1517 (54%) 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.

41. UEA 7th March 201641 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

42. UEA 7th March 201642 Probability of receiving Treatment (= Good degree) Cut-off at 60 Science UGs in responding to DLHE online/post: (except: OS, course duration ~=3|4) Scatter Plot: Bins=30 n=1293

43. UEA 7th March 201643 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

44. UEA 7th March 201644 Probability of receiving Treatment (= First) Cut-off at 70 Science UGs in responding to DLHE online/post: (except: OS, course duration ~=3|4) Scatter Plot: Bins=30 n=1293

45. UEA 7th March 201645 <60>=60 2.22011 2.146503 Compliers and Non-compliers All: 2.1 versus 2.2 n=1707: Compliers=92% All: 1st versus 2.1 n=2422: Compliers=89% Science: 1st versus 2.1 n=1091 : Compliers=90% Science: 2.1 versus 2.2 n=751 : Compliers=94% <70>=70 2.15481 1107435 <60>=60 2.22812 2.11361288 <70>=70 2.114222 1272726

46. UEA 7th March 201646 Scatter plot of log(annual pay) versus Overall Average Mark All n=1193(based on 1292, trimmed by payband)

47. UEA 7th March 201647 Density of log(annual pay))

48. UEA 7th March 201648 Scatter plot of log(annual pay) versus Overall Average Mark Science n=646

49. UEA 7th March 201649 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

50. UEA 7th March 201650 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 polynomial regression. (qspr) All Cut-off 60 n=1193 All (again) Cut-off 60

51. UEA 7th March 201651 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

52. UEA 7th March 201652 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

53. UEA 7th March 201653 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

54. UEA 7th March 201654 Continuity (Gender) (RDPLOT) All P(2)

55. UEA 7th March 201655 Continuity (Start_year) (RDPLOT) ALL P(2)

56. UEA 7th March 201656 Continuity (FT-Employment) Cut-off 60 (RDPLOT) ALL P(4)

57. UEA 7th March 201657 Continuity (FS) Cut-off 60 (RDPLOT) ALL P(4)

58. UEA 7th March 201658 Continuity (FT-Employment) Cut-off 70 (RDPLOT) ALL P(4)

59. UEA 7th March 201659 Continuity (FS) Cut-off 70 (RDPLOT) ALL P(4)

60. 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” UEA 7th March 201660

61. UEA 7th March 201661 Density of Overall Average Mark All n=3210

62. UEA 7th March 201662 Density of Overall Average Mark Science n=1449

63. 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 UEA 7th March 201663

64. UEA 7th March 201664 Source | SS df MS Number of obs = 731 -------------+------------------------------ F( 8, 722) = 13.64 Model | 14.9292466 8 1.86615582 Prob > F = 0.0000 Residual | 98.7577255 722.136783553 R-squared = 0.1313 -------------+------------------------------ Adj R-squared = 0.1217 Total | 113.686972 730.155735578 Root MSE =.36984 ------------------------------------------------------------------------------- lannualpay | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- Upper Second |.1089436.059589 1.83 0.068 -.0080449.2259321 overall_ave60 |.0070743.0047128 1.50 0.134 -.0021782.0163268 female | -.1446318.028905 -5.00 0.000 -.2013798 -.0878839 | startyr | 9 | -.0911597.0453318 -2.01 0.045 -.1801577 -.0021618 10 | -.0697949.0461467 -1.51 0.131 -.1603926.0208029 11 | -.0165909.0543893 -0.31 0.760 -.123371.0901892 | Science |.183276.0382249 4.79 0.000.1082307.2583212 Soc Sci.2628758.0403385 6.52 0.000.183681.3420707 | _cons | 9.843886.0669234 147.09 0.000 9.712498 9.975273 ------------------------------------------------------------------------------- OLS of log(annual pay) on 2:1 Treatment ALL (2:1 vs 2:2) 48=< Overall Average Mark <70

65. UEA 7th March 201665 Source | SS df MS Number of obs = 345 -------------+------------------------------ F( 6, 338) = 6.22 Model | 4.3918592 6.731976533 Prob > F = 0.0000 Residual | 39.7453438 338.117589774 R-squared = 0.0995 -------------+------------------------------ Adj R-squared = 0.0835 Total | 44.137203 344.128305823 Root MSE =.34291 ------------------------------------------------------------------------------- lannualpay | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- Upper Second |.2530753.0758185 3.34 0.001.1039398.4022108 overall_ave60 | -.0053114.0066282 -0.80 0.423 -.0183491.0077262 female | -.1273411.0386864 -3.29 0.001 -.2034375 -.0512446 | startyr | 9 | -.1177361.0548012 -2.15 0.032 -.2255304 -.0099418 10 | -.0955398.0564629 -1.69 0.092 -.2066028.0155231 11 | -.1239787.0699904 -1.77 0.077 -.2616503.0136929 | _cons | 9.965254.0664273 150.02 0.000 9.834591 10.09592 ------------------------------------------------------------------------------- OLS of log(annual pay) on 2:1 Treatment Science (2:1 vs 2:2) 48=< Overall Average Mark <70

66. UEA 7th March 201666 Number of obs = 863 F( 3, 859) = 18.54 Prob > F = 0.0000 Total (centered) SS = 137.9609851 Centered R2 = 0.0583 Total (uncentered) SS = 86276.76392 Uncentered R2 = 0.9985 Residual SS = 129.9197298 Root MSE =.388 ------------------------------------------------------------------------------- | Robust lannualpay | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- upper second |.2631072.1358355 1.94 0.053 -.0031255.5293399 overall_ave60 | -.0060847.0073113 -0.83 0.405 -.0204146.0082452 female | -.1844776.026794 -6.89 0.000 -.2369929 -.1319624 _cons | 9.872391.0968484 101.94 0.000 9.682571 10.06221 ------------------------------------------------------------------------------- IV (2SLS): log(annual pay) on 2:1 Treatment ALL (2:1 vs 2:2) 48=< Overall Average Mark <70 But this includes cases of 3rds and 1sts in the borderline zones. Excluding these cases...

67. UEA 7th March 201667 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 ------------------------------------------------------------------------------- IV (2SLS): log(annual pay) on 2:1 Treatment ALL (2:1 vs 2:2) 48=< Overall Average Mark <70 (Same as previous estimation: but excluding cases of 3rds/1sts in borderlines) So the cases of 1sts in the top borderline was driving an apparent effect of 2:1s (given a discontinuity around 70).

68. UEA 7th March 201668 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 ------------------------------------------------------------------------------ IV (2SLS): log(annual pay) on 2:1 Treatment Science (2:1 vs 2:2) 48=< Overall Average Mark <70 For First Stage regression see next slide Second Stage regression

69. UEA 7th March 201669 First-stage regression of Upper Second (Science): OLS estimation -------------- Number of obs = 345 F( 3, 341) = 493.35 Prob > F = 0.0000 Total (centered) SS = 58.69565217 Centered R2 = 0.7412 Total (uncentered) SS = 270 Uncentered R2 = 0.9437 Residual SS = 15.1921136 Root MSE =.2111 ------------------------------------------------------------------------------ | Robust good | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- overall_ave |.0295916.0051715 5.72 0.000.0194195.0397636 female |.0501063.0242366 2.07 0.039.0024342.0977784 dum60 |.5147076.0791584 6.50 0.000.3590075.6704078 _cons | -1.446323.2642606 -5.47 0.000 -1.966109 -.9265373 ------------------------------------------------------------------------------ Included instruments: overall_ave female dum60 ------------------------------------------------------------------------------ Partial R-squared of excluded instruments: 0.2638 Test of excluded instruments: F( 1, 341) = 42.28 Prob > F = 0.0000

70. UEA 7th March 201670 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 -------------------------------------------------------------------------------- IV (2SLS): log(annual pay) on 1st Class Degree Treatment ALL (1st vs 2:1) 58=< Overall Average Mark

71. UEA 7th March 201671 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 -------------------------------------------------------------------------------- IV (2SLS): log(annual pay) on 1st Treatment Science (1st vs 2:1) 58=< Overall Average Mark Consistent with RD for 60 < Overall Average Mark < 70

72. 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 UEA 7th March 201672

73. 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 UEA 7th March 201673