Higher education and the distribution of income

1. Higher education and the distribution of income Craig Holmes Higher Education II seminar 19thOctober 2012

2. Seminar outline • Aims: • Look at how economists have tried to measure to private returns to education, including HE • Discuss some of the methodological issues • Consider the factors which drive changes in the distribution of earnings of graduates over time

3. Higher education and earnings • In week 1, we discussed links between education and training and higher wages: • Human capital theory: education  higher productivity  higher wages through labour market competition • Signalling: education  signal of productivity  high wages through separation from less able workers

4. Higher education and earnings • Average wages of graduates vs. non-graduates: Source: LFS 2008

5. Higher education and earnings • Male wages, graduates vs. non-graduates: Source: LFS 2008

6. Higher education and earnings • Female wages, graduates vs. non-graduates: Source: LFS 2008

7. Higher education and earnings http://www.oecd.org/edu/highereducationandadultlearning/48630790.pdf

8. Higher education and earnings • Income benefits to education not solely about higher wages • More educated people: • Are less likely to be unemployed • Are more likely to find a job whilst unemployed • Are more likely to receive further training and education • Are more likely to progress to better jobs

9. Graduate earnings http://www.oecd-ilibrary.org/education/what-are-the-returns-on-higher-education-for-individuals-and-countries_5k961l69d8tg-en

10. Higher education and earnings • Average earnings do not take into account all the differences between two groups • Key question: what is the marginal effect of investing in education on earnings? • Economists use wage regressions to calculate this (Mincer, 1974)

11. Introduction to regression Y • Data sample: • Y – dependent variable • X – explanatory or independent variable X

12. Introduction to regression Y • Estimate using OLS: • Yi= a + bXi + errori b 1 a X

13. Introduction to regression Y • OLS finds a and b to minimise sum of errors2 a+bXj errorj a Xj X

14. Introduction to regression Y • Assumption is that for a given X, Y is distributed normally with mean value a + bX Ex(Y|Xi)=a+bXi a Xi X

15. Introduction to regression • When there are many potential explanatory variables we use multivariate regression (still using OLS) • Y = a + bX + cZ + dW + ... + errors • Each coefficient captures the partial correlation between the explanatory variables and Y, holding everything else constant • Linear form is convenient, but real life may be more complex • Missing variables • Interactions

16. Returns to higher education • Wage premia and rates of return conflated • Internal rate of return: • NPV of E = (w0 – c0)+ δ(w1 – c1)+ δ2(w2 – c2)+... • w and c are wage increases and costs of acquiring education level E • δ is discount factor – NPV = 0  δ = 1/(1+IRR) • Wage premia: • Log wage = a + b1.E + b2.exp+ b3.E.exp • Under certain assumptions, b1= IRR

17. Returns to higher education • BIS (2011): • Labour Force Survey 1996-2009 • Sample: • Undergraduate or equivalent vs. 2 ‘A’-levels • Masters or doctorate vs. undergraduate • Controlled for age, family, marital status, gender, ethnic background, region and year • Breakdown by subject and degree class • Max vocational qualification at level 3

18. Returns to higher education • BIS (2011):

19. Returns to higher education • BIS (2011): • Average return: 27.4% (29.7% and 23.5% for women and men respectively) • Highest returns: medicine (82.8%), maths and computer science (41.1%) and law (41.2%) • Lowest returns: creative arts (6.3%), mass communications (8.4%) and history and philosophy (10.4%) – only significant for women. • First class degree (32.7%) vs. Lower second (21.3%)

20. Higher education and earnings • Questions: • Does completing a degree make a worker more productive or are those who get a degree, on average, more productive? • Does a degree act as a labour market signal to employers?

21. Measuring returns to education Wage • Estimate: • Log wagei = a + b.Ei + d.Xi + errors Δw = b ΔE E1 E2 Education

22. Measuring returns to education High ability Wage • Estimate: • Log wagei = a + b.Ei + d.Xi + errorsi Estimated b Low ability b E1 E2 Education

23. Measuring returns to education • Selectivity bias (or ‘ability’ bias): • Observed data reflects differences in unobservable characteristics • Unobservable characteristics are correlated with variable of interest • Higher ability workers choose more education • Some of estimated wage return to education is due to differences in ability • This may, or may not, reflecting signalling

24. Measuring returns to education Estimated Wage High ability • Suppose education and ability are correlated • Estimated b > true b b Low ability b' b E1 E2 Education

25. Measuring returns to education • Thompson (2012) – BIS sample comparison

26. Measuring returns to education • Signalling vs. ability bias • Signalling could cause ability bias to be present in OLS estimations of earnings data • However, ability bias might exist even if signalling is not taking place – employers may be better at observing productivity differences than econometricians!

27. Measuring returns to education • Huge literature on dealing with ability bias • Twin studies • Instrumental variables • Natural experiments • See Card (1999) for attempts to deal with ability bias (mostly in schooling, not HE) • Two HE approaches: • Use test scores as proxies for pre-college ability • Look at non-completing students

28. Returns to higher education • Blundell, Dearden and Sianesi (2000): • National Child Development Study (1958 cohort) • Degree holders compared to ‘A’-levels • Wages at age 33 • Uses school tests (‘A’-levels scores, maths and reading aged 7) to proxy for ability

29. Returns to higher education • Blundell, Dearden and Sianesi (2000): • ‘Raw’ returns to undergraduate degree 21% (men) and 39% (women) • Reduced slightly by controlling for ability • Reduced further by controlling for job characteristics • Full model returns: 12% (men) and 34% (women)

30. Measuring returns to education • Other issues: • Highest qualification or all qualifications? • Include occupations?

31. Measuring returns to education • Exercise: • What would happen if you estimated: wages = a + b.DEGREE + c.APPRENTICE • Would b = 10% and c = 5%? • How could you solve this? • What practical problems would you encounter?

32. Measuring returns to education • Occupation and/or industry variables are sometimes included in wage equations • May potentially proxy for unobserved ability differences • However, may also create additional selectivity biases (due to unobserved specific ability) • People self-select into jobs they earn highest at

33. Labour market for graduates • Most earnings studies offer a snapshot of the historical link between (higher) education and wages • Policymakers also need to be forward looking: • What are the trends? • What are the effects of policy options? • This will largely depend on the demand for skills

34. Labour market for graduates • Wage premia reflect supply and demand in the labour market • Assume a competitive labour market • Firms demand workers of particular skill until MR = MC • MC = wage rate • In our week 1 example, MR of each skilled worker was 200. Competition led to wage = 200. • In real life, MR decreases as more workers are employed • Wage set to clear market  Total demand (sum of all firm demands) = total supply

35. Labour market for graduates Firm Labour market Wage Supply Wage w Demand D=MR L L

36. Labour market for graduates Wage Supply New supply W** W* New demand Demand L

37. Labour market for graduates • We saw in week 1 that the supply of graduates in UK has increased dramatically since 1989 • What has happened to demand?

38. Labour market for graduates • Technology is often acknowledged as being a key driver of the demand for skill: Technical advances since the 1980s have been the main driver in helping workers become more productive. This has been strongly biased towards those with the skills to adapt and use new technology. As a result, more highly skilled workers are in increasing demand by employers (‘Skills For Sustainable Growth’ pg. 9, BIS 2010) • Skill-biased technical change • Task-biased technical change (Autor, Levy and Murnane, 2003; Goos and Manning, 2007) • Growth in non-routine jobs – some of which are low skill

39. Changing returns to higher education • Walker and Zhu (2008): • LFS 1994-2006: • No drop in average male wage premium • Small rise in average female wage premium • Some evidence of drop-off at bottom of distribution

40. Changing returns to higher education • Bratti, Naylor and Smith (2008): • Similar methodology to Blundell et al (2000) • Uses British Cohort Study (1970 cohort) • Male premia has remained same as in NCDS study (15%) • Female premia has fallen sharply (18%)

41. Changing returns to higher education • Ireland et al(2009): • Changing graduate premia reflect • Change in return to education • Change in return to unobserved ability • Changing ability composition of graduates • Hence, expansion of HE can have effect on signalling role of degrees

42. Labour market for graduates • There is concern about skill utilisation • Will return to this theme is weeks 4-7. • This may have distributional, not average, effects • Green and Zhou (2010): Graduate job Degree a sorting mechanism (retrospective graduatisation) Employees use graduate skills in non graduate job Non-graduate job

43. Labour market for graduates • Green and Zhou (2010):

44. Changing returns to higher education • “The Global Auction” – Brown, Lauder and Ashton (2011): • Four key themes • Mass expansion of higher education • Quality-cost revolution • Digital Taylorism • The War for Talent

45. Changing returns to higher education • Suggests graduate jobs are segmenting • No such thing as “the graduate premium”? Source: Holmes and Mayhew (2011)

46. Exercises • The last section of this lecture focused heavily on the UK • How do you think the issues discussed apply to other countries? To consider: • The size of the HE sector • The skills focus of the HE sector • Transitions into work • Labour market trends that may drive earnings