Higher education returns and effects of ability composition Motivation

1. Higher education returns and effects of ability composition • Motivation • Increased higher education participation is likely to have various impacts on returns to degrees • One channel – through implied changes in ability composition of different educational groups – has received relatively little attention University of Sheffield

2. 2. Context HE API in UK shows rapid expansion after mid-1980s, with growth especially dramatic for women. See Figure 1. University of Sheffield

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5. 3. Why did the HE API rise? • Demand-side factors: • derived demand (SBTC) • GCSE pass rates • Supply-side factors: • Increase in places • - finance following student • - end of binary divide • Loans system University of Sheffield

6. 4. Heterogeneity in ability Consider the simple case of two ability types and increased participation stemming from a reduction in costs associated with education: r S1=MC1 DH DL sL sH s University of Sheffield

7. A fall in costs can produce a change in the ability composition: (and also a change in the marginal return: see W-Z QR results) r S1=MC1 S2=MC2 DH DL s sL=sH University of Sheffield

8. At the individual level, the issue of the relationship between ability and educational investments when individuals are heterogeneous is well-known and is associated with the problem of ability bias in estimates of returns to education. At the macro (cohort) level, cohort changes (eg in participation) can impact on estimates of returns through changes in the extent of ability bias across cohorts. University of Sheffield

9. Assume: Within a cohort: Across cohorts: . . . University of Sheffield

10. Across cohorts: University of Sheffield

11. What happens if HE API grows? There is no change in . But this is a special result under the uniform distribution . Blackburn and Neumark show that under a triangular distribution, falls . University of Sheffield

12. The implication is that graduate expansion over cohorts produces a compositional change of a type that leads to a reduction in ability bias (or a lower value to the signal of a degree), ceteris paribus, and hence a lower estimate of the college wage premium. The US literature on this was not developed further as the Blackburn-Neumark analysis was attempting to explain an increase in the college wage premium at a time of higher college participation. Rosenbaum (2003) finds evidence supporting the view that compositional changes can explain longer term patterns in the college wage premium in the US. University of Sheffield

13. 5. Evidence on the UK college wage premium over time • Harkness-Machin (1999) • was rising in the 80s and constant in the 90s • Likely explanation: SBTC in 80s raised rs and ra; offset in 90s by graduate expansion • Walker-Zhu (2008) • (LFS) Focus on birth cohorts of 66-68 vs 75-77: API more than doubled. • Result: constant for men (15%) and • rising for women (40 -> 47%) • Conclusion: ra must have been rising to offset what must have been falling rs (and compositional changes) University of Sheffield

14. What can we learn from the birth cohort studies in Britain? • HE API HE API (%) • +4 cohorts1 Men Women • NCDS 13% (1977) 14% 12-18 34-38 • 1958 • Birth cohort • BCS70 • 1970 • Birth cohort • 1Eg, entering HE in 1993, graduating in 1996, 4yrs experience • by 2000 when £ observed of 1970 birth cohort. • 2Conceals extent of growth in female participation in HE. 18% (1989)2 30% 15 18 University of Sheffield

15. Given the much greater expansion in the HE API of women relative to men, we might expect the consequently greater compositional change for women to lead to a relative fall in the college wage premium of women. On (i), if L-mkts are integrated (not segmented by gender), then gender composition changes should not affect rs differentially by gender. There is some evidence that SBTC has favoured women over men. On (ii), again there is evidence of shift in demand to skills associated with female employment. So evidence of relative fall in college premium for women would indicate importance of role for (iii). University of Sheffield

16. Data: BCS70. The dependent variable is the natural logarithm of gross hourly wages, age 30. Wage premia are relative to individuals with 2 or more A-levels. The wage equation also includes a wide set of explanatory variables: see paper. λ are the generalized residuals computed from the ordered probit model for the highest educational qualification achieved. The CFA model is identified by parents' education that is included only in the education equation. The F-test refers to the exclusion of parents' education from the controls in the wage equation (2) in the CFA model only identified by functional form. University of Sheffield

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18. 6. Degree class signals We now consider the premium associated with the award of a distinction to the most able graduates. Compared to the case concerning the premium for a degree, we expect the premium for a distinction to reflect a relatively strong signalling element. (Note contrast between UK and US: see Arcidiacono et al., 2008.) The question we address is: how is da/ds likely to change following an increase in the HE API? University of Sheffield

19. Theory As HE API increases, da/ds rises and this causes the estimated premium for a distinction to rise, cet. par.. (nb: assume initially that d constant). Result robust to distributional assumptions. Is this consistent with empirical evidence? University of Sheffield

20. USR data Ireland, Naylor, Smith, Telhaj (2009) • 1985 – 1993 graduating cohorts • (+ HESA data for1998 leavers) • (+ GCS data for 1985 and 1990 cohorts) • Administrative data on full graduate populations • Personal characteristics • Academic background • Family background • University/course information • First Destination Survey (EL-SD) • Problem with individual earnings (balloon surface) • Average occupational earnings (averaged over all years) University of Sheffield

21. USR data, summary statistics for those in employment based on the 1993 cohort (continued) Variable Mean Mean Degree Class Males Females First (I) 0.10 0.07 Upper Second (II.1) 0.45 0.55 Lower Second (II.2) 0.33 0.32 Third (III) 0.07 0.03 Sample size (n) 19476 19978 University of Sheffield

22. Average occupational earnings by subject field and degree class for the 1993 cohort   MALES FEMALES   Mean n Mean n 450.28 19476 333.10 19978 Degree Class I 480.14 1909 351.31 1309 II.1 465.25 8791 338.44 10982 II.2 432.62 6471 322.58 6381 III 408.41 1344 319.06 642 University of Sheffield

23. Selected Results of occupational earnings equation for the 1993 cohort     MALES FEMALES Variable Coeff  Coeff  Degree class I 0.038*** 0.037*** II.1 (default) II.2 -0.054*** -0.042*** III -0.094*** -0.053*** Other -0.080*** -0.079*** Note: Premium for a good degree is 6.0%. Similar to estimate of 6.4% for BCS70 students graduating at about same time. From 1990 GCS data, premium for a good degree is 5.0% University of Sheffield

24. Degree class coefficient estimates for the 1985-1993 and 1998 cohorts • 1985 1986 1987 1988 1989 1990 1991 1992 1993 1998 • Males • I 0.003 0.006 -0.007 -0.006 0.001 0.027 0.027 0.042 0.038 0.046 • II.1 (default) • Females • I 0.012 0.012 0.018 0.028 0.026 0.033 0.025 0.053 0.037 0.067 • II.1 (default) • Why is this an interesting time period? • Because of what was happening to HE API over this period. • Correlation between API and Premia (1985 – 1993 cohorts): • First, Males, = 0.81; (ii) First, Females, = 0.79; • (iii) Overall Span (1st to 3rd), Males, = 0.86; (iv) Overall Span, Females, = 0.64. • Over this time period, there is no strong evidence of substantial increases in rsor ra: W-Z show degree returns constant for both men and women at least prior to 1995 graduates.    University of Sheffield

25. Conclusion Observed changes across cohorts in returns to degrees by gender and in returns by class of degree awarded are consistent with the hypothesis that graduate expansion is an important driver, mediated through the implied changes in ability composition across education groups . Future work aims to examine how these patterns: (i) have continued to evolve for later cohorts and (ii) behave over time (with tenure/experience) University of Sheffield