1. Amanda Gosling and Yu Zhu GES Summer School, Kent, 30th June 2010
Over-education
Amanda Gosling
and
Yu Zhu
GES Summer School, Kent, 30th June 2010

2. Structure Introduction and Motivation
Background (literature, some trends and data
A bit of theory
Estimating the extent of over-education and discussion of some evidence
Tea Break
Discussion

3. Why look at over-education?
Over-education is a very active research field:
Google Scholar keyword search returns papers in Business, Administration, Finance, and Economics; and another papers in Social Sciences, Arts and Humanities
Policy
Controversy over 50% target of cohort in further or higher education
Debate over the level and mechanisms for funding and subsidy
Current (25% help!) funding cuts
Skills and Concepts
Controversy over over-education is a good way to understand what labour economics are concerned about, the models and concepts used and the key areas of disagreement
Right way to model the labour market
How to estimate the return to education?
How to think about policy interventions

4. Workshop should not be considered a summary of everything there is to know about over-education but an illustration of current thinking about labour markets through this particular question

5. Definitions Micro Macro Examples
Over-education refers to the situation when a job-holder has an achieved qualification above that which would currently be required for someone to get the job (rather than to do the job).
As such, it represents graduate labour market disequilibrium: workers possess excess educational qualifications relative to those their jobs require.
Macro
Labour market has “too many” graduates
Credentialism
Again represent disequilibrium
Examples
Slacker
Philosophy PhD
SAHM
Mother with small children unable to find suitable jobs with flexible/part time hours
Russian Labour market
Early labour market experiences
Long run graduates “trapped” in low status jobs

6. Background Emphasis on education expansion Academic Factual
Third way (emphasis on equality of ops. rather than of outcomes)
Becoming more controversial
Academic
Freeman (the over-educated American)
Research on demand and supply of skills
Research on large (and growing) returns to education
Micro led research on over-education (and its criticisms)
Factual
Changes in “return” to education
Participation in education
Long series on education by occupation and gender

7. Academic background (1) Cobweb-model
Graduate wage
supply
Wage associated with zero rents
demand
Graduate employment

8. Card and Lemieux (QJE 2001)
Look at changes over time in relative wage of graduates
Hypothesis that part of the changes can be explain by changes in relative supply
Idea that demand and supply are always racing to keep up with each other
Strong support using US micro data
BUT
Identification problems
Identification relies on non-substitutability of workers of different ages
Estimate is “raw”

9. What do we know about the return to education?
Early work (e.g. Dennison 1962, 1967) on growth accounting
But is education a consumption or investment good?
Mincer, Becker Human Capital Wage regressions
Rate of return (like on any other asset)
High but fluctuating
Sheepskin effects
Later (Ashenfelter, Angrist, Kruegar, Walker) on solving the “ability bias” problem
Use of instrumental variables to relate differences in wages to differences in education that are not a result of differences in ability (e.g. Twin studies, Vietnam draft, Compulsory School Leaving laws)
Measured returns appear to RISE
Measurement error
Heterogeneity in Returns (marginal entrant different from the average, suggestion of credit constraints)

10. What do we know about the return to education?
Changes in the return to education over time (Card, Gosling et al. Schmitt).
Sharp rise over 80s and early 90s
Evidence (Green and Zhu) that return to education for younger cohorts has flattened or even fallen
Growing focus on the distribution of returns using techniques like quantile regressions
Starts off with Buschinsky (1996)
Key difficulty is that the distribution of returns is NOT the same as the distribution of differences
Treatment effect literature
Consensus that all changes to the structure of wages cannot be explained by differences in the demand and supply of education and skills

11. Over–education literature
Consequences (wages, job satisfaction
Causes (race, gender, discrimination, ability)
Measurement
Changes over time
Long versus short run
Implications for human capital model
Is this a meaningful avenue for research?

12. Now some data on background trends
Own calculations using FES and BHPS data

13. Supply of Education (Men)

14. Supply of Education (Women)

15. Men

16. Women

17. men

18. women

20. Quantile estimates of Ed diff (Men)

21. Quantile estimates of Ed diff (Women)

22. Summary
Dramatic increase in relative supply of educated workers over last 30 years
Some weak evidence that the return has declined
For younger cohorts
Distribution of returns
Picture becomes less clear cut when we look at employment
More graduates doing non-graduate jobs but
Less of these jobs
Numbers are small
Not clear that over-education is a growing problem
Might think it should be more of an issue for women but this is not apparent in the data

23. Now for some economics!

24. Some key economic concepts to understand and think about
Production functions
Education as screening/signalling device
Labour market models
Incentives to acquire education. If education is a choice, how can a worker have too much?
Investment under uncertainty

25. Production functions
Can we write a production function with labour quality in which the concept of over-education “makes sense”?
Need the marginal product of extra education to be zero
Firm production with labour of different types
Technology of human capital production
Part of explanation of why this topic is so controversial

26. Education as screening/signalling device
Dog-bone economy (Sattinger)
Variations in costs of education are correlated with variation in unobserved ability.
Sheepskin effects
Plausibility of models depend on other available strategies to separate workers

27. Labour market
Assume there ARE some firms for which MP of education is zero
Will we then get over-education?
Argue that only in presence of labour market imperfections

28. W
Supply of graduates
Supply of non graduates L Q

29. W
Supply of graduates
Supply of non graduates L Q
Non graduate firm ONLY employs non graduates

30. So in classic model of labour market will get specialisation rather than over-education
If we do see “over-education” then it must be to do with technology of human capital rather than production
If graduate were in non graduate jobs they would have to get a graduate wage
So specialisation and no wage diffs

31. What about market with frictions?
Much applied theory of the labour market (Burdett, Shimer, Mortensen, Coles, Manning) works on the idea that workers are not able to see or to move to all potential jobs
Search or mobility costs
Non wage differences in jobs (Bhaskar and To)
See idea with simple discriminating monopsony model (but note this analysis is partial)

32. Supply non graduates W
Supply graduates
Constant marginal product for simplicity N

33. W
Marginal cost of hiring non graduate
Marginal cost of hiring graduate
Supply graduates
Supply non graduates
demand N

34. W
Marginal cost of hiring non graduate
Marginal cost of hiring graduate
Supply graduates
Supply non graduates
demand
Small wage premium here and large diff in employment N

35. W
Marginal cost of hiring non graduate
Marginal cost of hiring graduate
Supply graduates
Supply non graduates
demand
Note that could have drawn graph to get a NEGATIVE or zero premium N

36. So Simple monopsony model does predict incidence of over-education
Model is ambivalent about relative wage for graduates in non graduate firms
If labour supply is very inelastic then may get zero or negative differences
Costs too much for the firm to try and get more graduates by paying them more
Key thing is the relative wage depends on outside option NOT on relative productivity
What about industries with more than one firm?

37. This analysis is partial, as if each firm follows this strategy the supply curves will look very different
General equilibrium search models,(Diamond (1971) , Burdett and Mortensen (1998) can be easily used to show how the relative wage and employment of graduates will evolve in the non graduate sector in equilibrium
Manning shows that key findings are similar to those in the partial equilibrium model BUT
Get wage dispersion amongst workers of each type
Association of education and wages across firms (higher wage firm attract more educated workers) but this does not relate to productivity
May get higher graduate unemployment

38. What about incentives to acquire human capital?
Assume
There exist firms for which MP of education is zero
Labour market frictions exist
private returns are lower than social returns
returns are risky

39. Earnings B A O G + E F Age 65 - C D Stays at school until E
Leaves school at F O G + E F
Age 65 - C D

40. So if individuals are undertaking investments with low return
Are the costs also low
Open to the floor!
Temporary versus permanent effects
Is the low return predictable ex ante?
If so then mean return may be large but the outcome small or zero
Finding from investment literature is that this typically results in under-investment as agents are risk averse
Case for MORE subsidy not less
Govt. should act as insurer (graduate tax?)

41. Estimating the Extent of Over-Education

42. Background Focus on graduate over-education
Better measured than other types of over-education
important policy implications
HE expansion over the past two decades
Government policy to increase HE participation rate to 50%
Destinations of Leavers from Higher Education (DLHE) survey: snapshot of graduates 6 months after graduation
Latest figure on the 2008 cohort of graduates:
61.4% entering employment, 14.1% entering further study or training, 8.1% entering working & studying, 7.9% unemployed and 8.5% other

43. What types of work did graduates go into?
Of those who were working ft or pt or combine work with further study, 32.3% (last 5 rows) might be classified as over-educated
But this is only 6 months after graduation
Occupations
Share
Arts, design, culture, media and sports professionals
6.20%
Business and financial professionals and associate professionals
7.50%
Commercial, industrial and public sector managers
9.30%
Education professionals
7.10%
Engineering professionals
3.20%
Health professionals and associate professionals
14.60%
Information technology professionals
Legal professionals
0.60%
Marketing, sales and advertising professionals
4.10%
Scientific professionals
1.20%
Social and welfare professionals
4.70%
Other professionals, associate professionals and technical occupations
5.20%
Numerical clerks and cashiers
2.00%
Other clerical and secretarial occupations
8.90%
Retail, catering, waiting and bar staff
10.60%
Other occupations
11.60%
Unknown occupations
0.20%

44. Earnings of new graduates by occupations
salary of full-time, first degree leavers who entered full-time employment in the UK
Graduates in professional jobs earn more than their counterparts in non-graduate occupations
Associate professionals in between
Types of jobs
Average salary for a new graduate (£)
Health professionals (eg doctors, dentists and pharmacists) 
25,362
Functional managers (eg financial managers, marketing and sales managers)
23,976
Engineering professionals
23,651
Business and statistical professionals (eg accountants, management consultants, economists)
23,208
Information and communication technology professionals
22,941
Architects, town planners, surveyors
21,567
Teaching professionals (eg secondary and primary school teachers) 
19,989
Science professionals
19,972
Legal professionals (eg solictors and lawyers)
19,765
Sales and related associate professionals
19,134
Design associate professionals (eg designers, including web designers) 
17,829
Artistic and literary occupations (eg artists, writers, actors, musicians, producers and directors)
17,334
Social welfare associate professionals (eg youth and community workers, housing officers) 
17,317
Legal assocaite professionals (eg legal executives and paralegals)
16,931
Sports and fitness occupations
16,443
General administrative occupations 
15,374
Customer service occupations 
14,543
All occupations
19,677

45. Trend in over-education: DLHE 2004-8
Graduate job classifications were developed by Professors Peter Elias and Kate Purcell for their study Seven Years On: Graduates in the Changing Labour Market.
Types of job 
Examples
2004
2005
2006
2007
2008
Traditional graduate occupations
Solicitors, research scientists, architects, medical practitioners
11.1%
11.2%
11.5%
11.7%
12.4%
Modern graduate occupations
Software programmers, journalists, primary school teachers
12.3%
12.6%
13.1%
13.8%
13.7%
New graduate occupations
Marketing, management accountants, therapists and many forms of engineer
14.9%
15.5%
16.0%
17.2%
16.6%
Niche graduate occupations
Nursing, retail managers, graphic designers
22.7%
23.3%
23.7%
23.8%
23.0%
Non-graduate occupations
Any jobs that do not fall into the above categories
39.1%
37.5% 
35.6%
33.5%
34.3%
Total in graduate occupations 
60.9%   
62.5%
64.4%
66.5%
65.7%

46. Measurement of over-education
Typologies of over-education:
Objective measures of over-education
Required education determined on the basis of job title according to the SOC system (job title inflation will lead to under-estimation of over-education!)
Comparing individual’s education with the mean education level of his/her occupation
Subjective measures of over-education
Self-assessed (by the respondent) minimum requirements of the job (to be contrasted with individual’s acquired education)
Directly asking the respondent whether they are overeducated
Distinction between overqualification and skill underutilization
Respondent’s satisfaction with the match between qualification and job

47. Empirical evidence Use the Green & Zhu 2010 OEP paper as a case study
Based on up-to-date data from the UK Quarterly Labour Force Survey and recent UK Skills Surveys (1992, 1997, 2001 and 2006)
Theme: use of “overqualification” to help understand trends in the returns to graduate education after the surge in HE participation
Trends in the dispersion of returns to graduate education: quantile regressions
Definition and decomposition of overqualification
Trends in overqualification
Trends in the costs of overqualification
Linking the trends

48. Motivation
Figure shows proportion of year olds who record having a first degree in UK QLFS 1994–2006, by birth cohorts (by year when aged 19) (Walker & Zhu, SJoE 2008 Fig 1)
huge increases in HE participation over a short period of time
More than 50% increase for men
Doubling for women

49. Measure of stock of graduates
Figure updated with two additional years of data (up to Dec 2009)
It shows share of graduates in the labour force (proportion of year olds with NVQ 4+)
Rapid rise throughout period.
For women an apparent acceleration after 2002

50. Returns to graduate education
Key research question: If the increased participation persists, when if ever will there be a decline in the returns to graduate education?
General stability or rise over 1980s and 1990s:
Machin, 2003
Elias and Purcell, 2004; Mason, 2002
Some hints of falling returns from:
Purcell et al., 2005
Sloane, 2005
Walker and Zhu, 2008
Machin covered to the end of the 1990s.
Mason: three sectors in : graduate skills being utilised in traditionally non-graduate jobs
Elias and Purcell: new and niche jobs, using some of the skills normally associated with graduates
Chevalier and Lindley: no increase in penalty for overeducation for 1995 graduates in 2002, compared with earlier cohort.
Covering up to
A few hints from these studies, nothing definitive.

51. QR/OLS est. of the college premium for women
Returns are for Level 4 relative to Level 2.
Dispersion between high and low percentiles, attributable to either: education complementary with unobserved skill; heterogeneous school quality or over-education (as found by Pereira and Martins for both sexes together).
Dispersion has become more pronounced over time: 0.17 to 0.27 log points; significant.
Another way of seeing this: the returns have increased, slightly, at higher quantiles, but have decreased somewhat at lower quantiles.

52. QR/OLS est. of the college premium for men
Increasing dispersion over time with higher returns for high residual quartiles and slightly lower returns for low quartiles at end of period; gap increased significantly from to 0.11.
Put another way, there are significant increases at the top end, but not at bottom end: new finding.
Our story is that this is evidence of persistently increasing demand at the high end; but that returns at the lower end have fallen or remained steady because more people at this end are failing to obtain graduate-level jobs (with associated pay). Those who are overeducated are not in effective competition with successful graduates; hence the falling returns at the lower end can co-exist with stable or rising returns to more successful graduates.

53. Changing composit. of the lowest quintile of the residual wage dist.
In lowest quintile of the residual pay distribution we have:
No rise in proportion of graduate jobs being held
Rise in the proportion of graduates.

54. Previous evidence on overeducation
Overqualification, according to earlier studies
Has a pay penalty
Lowers job satisfaction
For some is persistent/permanent
Is more likely for less able persons
Chevalier (2003): splits into “genuine” and “apparent” overeducation, according to whether satisfied with match.
Little/nothing known about (our research questions):
change over time,
whether cohorts shake it off as they get older,
changing penalties over time;
plus little or no attempt to relate to changing returns to education.

55. Definitions and decomposition
Overqualification (OQ) dummy:
OQ = 1 if RQi < Qi, OQ = 0 if RQi >= Qi
Overskilling (OS) dummy, defined from:
“How much of your past experience, skill and abilities can you make use of in your present job?”, (“very little”/ “a little”/ “quite a lot”/ “almost all”). Top two points.
“Real Overqualification”: OS and OQ
“Formal Overqualification”: OQ but not OS.
Skills Fully Utilised
Skills Underutilised
In Graduate Jobs
Matched
Qualification Matched and Skills Underutilised
In Non-Graduate Jobs
Formal
Overqualification
Real
Two definitions of overskilling used in paper for robustness.

56. Data Employment in Britain (1992) 1997 Skills Survey
UK-wide, but here restricted to employees, in England, Scotland and Wales; some regions over-sampled
5224 employees, weighted analyses, representative

57. Validation of classification of overqualification
(% of graduate employees)
Learning Time Over 2 Years
Learning Time Under 1 Month
Requirement to Learn New Things
Influence Skills Requirement
Complex/
Advanced Computing Skills Requirement
In SOC Major Groups
1-3
Real
Overqualification
8.0
38.0
19.5
5.0
11.3
30.9
Formal
26.3
13.1
36.7
25.7
25.0
58.0
Qualification Matched and Skills Underutilised
30.5
7.1
43.9
26.2
34.2
79.4
Qualification Matched and Skills Utilised
6.7
49.9
50.1
30.8
89.7
Point is: more skilled jobs for less overqualified, better matched, graduates.

58. Prevalence of Graduate Overqualification by Education Characteristics (Row percentages summing to 100%)
Real
Over-qualification
Formal
Qualification-Matched and Skills Underutilised
Qualification-Matched and Skills Utilised
Maths Level **
Below A-Level
9.4
24.9
4.7
61.1
A-Level or above
5.3
13.7
6.4
74.6
University Type
Oxbridge
0.0
10.6
0.4
89.0
Pre-1992 University
8.9
14.3
6.0
70.9
Other UK
8.8
21.4
65.2
Non-UK
14.8
3.7
60.1
Degree Grade*
Below Upper Second
12.8
20.0
3.5
63.7
Upper Second or First
9.3
15.6
4.6
70.4
Some connection to ability/achievement; not at all perfect correlation.

59. Education/job matching for graduates
1992
1997
2001
2006
MEN:
Matched
78.3
77.0
73.0
66.8
Overskilled
15.4 -
12.8
Overqualified, of which
21.7
23.0
27.0
33.2
Real Overqualification
7.5
7.2
9.9
Formal Overqualification
14.0
19.8
23.4
WOMEN:
76.2
74.8
76.6
68.0
12.2
12.0
12.7
23.8
25.2
32.1
5.9
8.4
16.1
17.5
23.7
Rise in overqualified. No rise in overskilling. Main increase in Formal Overqualification.
Similar story for both genders.

60. Cond. association of overqualification with log hourly wage, Men
1992
1997
2001
2006
1. Estimates including graduate overqualification (see below for other variables included)
Overqualified
-0.276
-0.330
-0.246
-0.404
(0.050)
(0.061)
(0.048)
Observations
1172
888
1657
2126
R-squared
0.43
0.40
0.45
2. Estimates including types of mismatch (see below for other variables included)
Real overqualification
-0.401 -
-0.488
-0.619
(0.085)
(0.081)
(0.099)
Formal overqualification
-0.240
-0.175
-0.322
(0.055)
(0.042)
Qualification Matched but Skills Underutilised
-0.229
-0.189
-0.046
(0.069)
(0.092)
(0.056)
1163
2125
0.44
0.41
0.46
Sharp increases in the overqualification penalty; as highlighted in red.

61. Cond. association of overqualification with log hourly wage, Women
1992
1997
2001
2006
1. Estimates including graduate overqualification (see below for other variables included)
Overqualified
-0.316
-0.257
-0.392
-0.454
(0.048)
(0.063)
(0.040)
(0.033)
Observations
1144
886
1643
2308
R-squared
0.49
0.58
0.57
0.55
2. Estimates including types of mismatch (see below for other variables included)
Real overqualification
-0.441 -
-0.430
-0.642
(0.060)
(0.056)
Formal overqualification
-0.250
-0.386
-0.407
(0.062)
(0.047)
Qualification Matched but Skills Underutilised
0.109
-0.076
-0.196
(0.058)
(0.088)
1134
1640
2306
0.56
Same story, with slight variation in the timing.

62. Cond. association of overqualification with job satisfaction
1992
2001
2006
Men
Real Overqualification
-0.429
(0.461)
(0.317)
(0.238)
Formal Overqualification
-0.196
(0.347)
-0.340
(0.184)
-0.199
(0.175)
Qualification Matched & Skills Underutilised
-0.784
(0.387)
0.195
(0.406)
-0.505
(0.430)
Mean job satisfaction of graduates
4.325
4.114
4.310
Women
(0.459)
(0.420)
(0.358)
0.160
(0.278)
0.389
(0.195)
-0.294
(0.177)
-0.750
(0.557)
-0.545
(0.315)
-0.341
(0.281)
4.515
4.231
4.472
Only real over-qualification has a significant effect on job dissatisfaction, as highlighted in blue.
Formal over-qualification has no significant effect.
For men, the satisfaction penalty has significantly increased since 1992; for women, it has always been quite high.
Same story using a compound job satisfaction measure.

63. Returns to graduate education at 10th/90th percentiles for men
Fanning out of 90th and 10th percentiles, as with QLFS data; increase at top end; decrease at lower end more pronounced than with the QLFS data.
But, no fanning out of the matched graduates.

64. Returns to graduate education at 10th/90th percentiles for women
Same story for women.

65. Summary of new findings
Increasing dispersion in returns
Rising “formal”, stable or slowly rising “real” overqualification till 2006
Rising pay and satisfaction costs of overqualification
Stable dispersion of returns for those matched to jobs

66. Discussion

67. Why does over-education happen?
The simple story (Human Capital Theory):
Human Capital Theory: education determines worker’s stock of HC which in turn determines marginal product; wages equate worker’s marginal product; firms adapt production technology in response to changes in the supply of skilled labour.
over-education as a result of too many graduates over-flooding the labour market arising from HE expansion, or
as a temporary blip while new graduates get their foot on the career ladder (life-cycle perspective) , or
omitted variable problem: over-educated workers may be compensating for a lack of work-related capital (assuming education and less formal measures of HC are substitutes)

68. Alternative explanations
Supply-side mechanisms:
worker skill heterogeneity: perhaps a university education only benefits the more able students, or
education is an inherently risky investment?
Demand-side mechanisms:
labour market frictions preventing possible efficient matches between educated workers and employers that need skills;
some type of graduates might be more constrained in the way they can look for jobs, e.g. women with partners, e.g. Frank (1978)
Does the evidence give support to the view that education is merely a signaling mechanism (rather than enhancing your productivity)?

69. Some things to think about
Is the concept of “over-education” a useful one for economists?
What does the evidence on over-education suggest about the rationale for reducing the number of state funded university places?
Is there anything that policy makers can (or should) do to improve outcomes for those graduates (both past and future) who are or will perform less well in the labour market?
Are there too many people going to university who would be better off going straight into the labour market or into another form of training.