Over-education

Amanda Gosling and

Yu Zhu

GES Summer School, Kent, 30th June 2010

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

Why look at over-education?• Over-education is a very active research field:

– Google Scholar keyword search returns 2000+ papers in Business, Administration, Finance, and Economics; and another 12000+ 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

• 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

Definitions• Micro

– 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

Background

• Emphasis on education expansion– 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

Academic background (1) Cobweb-model

Graduate employment

Graduate wage

demand

supply

Wage associated with zero rents

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”

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)

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

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?

Now some data on background trends

Own calculations using FES and BHPS data

Supply of Education (Men)

Pro

por

tion

of 2

5-30

yea

r o

lds

year

with university degree with any degree stayed on at school past 18

1978 1982 1986 1990 1994 1998 2002 2006

.15

.2

.25

.3

.35

Pro

por

tion

of 2

5-30

yea

r o

lds

year

with university degree with any degree stayed on at school past 18

1978 1982 1986 1990 1994 1998 2002 2006

.15

.2

.25

.3

.35

.4

Supply of Education (Women)

Diff

eren

ces

in w

ages

co

ntro

llin

g fo

r a

ge

year

Degree versus no qual Post 18 versus compulsory

1978 1982 1986 1990 1994 1998 2002 2006

.4

.45

.5

.55

.6

.65

.7

Men

Diff

eren

ces

in w

ages

co

ntro

llin

g fo

r a

ge

year

Degree versus no qual Post 18 versus compulsory

1978 1982 1986 1990 1994 1998 2002 2006

.55

.6

.65

.7

.75

.8

.85

Women

Diff

eren

ces

in e

mpl

oym

ent

rat

es

con

tro

lling

for

age

year

Degree versus no qual Post 18 versus compulsory

1978 1982 1986 1990 1994 1998 2002 2006

1

1.1

1.2

1.3

1.4

men

Diff

eren

ces

in e

mpl

oym

ent

rat

es

con

tro

lling

for

age

year

Degree versus no qual Post 18 versus compulsory

1978 1982 1986 1990 1994 1998 2002 2006

1

1.2

1.4

1.6

1.8

women

Proportions of Workers with Higher Edyear

bhps fes

Men graduate jobs

1978 1984 1990 1996 2002 2008

.25

.3

.35

.4

Men non graduate jobs

1978 1984 1990 1996 2002 2008

.02

.04

.06

.08

Women graduate jobs

1978 1984 1990 1996 2002 2008

.3

.35

.4

Women non graduate jobs

1978 1984 1990 1996 2002 2008

.02

.04

.06

.08

.1

year

10th percentile FES 10th percentile BHPS 90th percentile FES 90th percentile BHPS

1978 1982 1986 1990 1994 1998 2002 2006

.2

.4

.6

.8

Quantile estimates of Ed diff (Men)

year

10th percentile FES 10th percentile BHPS 90th percentile FES 90th percentile BHPS

1978 1982 1986 1990 1994 1998 2002 2006

.2

.4

.6

.8

1

Quantile estimates of Ed diff (Women)

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

Now for some economics!

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

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

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

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

L

W

Supply of non graduates

Supply of graduates

Q

L

W

Supply of non graduates

Supply of graduates

Q

Non graduate firm ONLY employs non graduates

• 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

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)

Constant marginal product for simplicity

N

Supply non graduatesSupply

graduatesW

N

Supply non graduates

Supply graduates

W

demand

Marginal cost of hiring graduate

Marginal cost of hiring non graduate

N

Supply non graduates

Supply graduates

W

demand

Marginal cost of hiring graduate

Marginal cost of hiring non graduate

Small wage premium here and large diff in employment

N

Supply non graduates

Supply graduates

W

demand

Marginal cost of hiring graduate

Marginal cost of hiring non graduate

Note that could have drawn graph to get a NEGATIVE or zero premium

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?

• 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

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

OG

FE

-

+

Earnings

Age 65

B

A

DC

Stays at school until E

Leaves school at F

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?)

Estimating the Extent of Over-Education

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

42

43

What types of work did graduates go into?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 3.20%

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%

• 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

44

Earnings of new graduates by occupations

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

• 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

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%

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

46

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 1994-2007 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

47

48

Motivation• Figure shows proportion of

25-59 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 25-60 year olds with NVQ 4+)

• Rapid rise throughout period.

• For women an apparent acceleration after 2002

.25

.3.3

5.4

1994199519961997199819992000200120022003200420052006200720082009Year

Men Women

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

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.

.2.3

.4.5

.6

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006Year (3-year moving window)

10th Percentilee 50th Percentilee90th Percentilee OLS

52

QR/OLS est. of the college premium for men

1. Increasing dispersion over time with higher returns for high residual quartiles and slightly lower returns for low quartiles at end of period; 90-10 gap increased significantly from -0.06 to 0.11.

2. Put another way, there are significant increases at the top end, but not at bottom end: new finding.

.25

.3.3

5.4

.45

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005yr3mw

10th Percentilee 50th Percentilee90th Percentilee OLS

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.

0.0

0.1

0.2

0.3

0.4

0.5

1994199519961997199819992000200120022003200420052006200720082009Year

% in Graduate Jobs, Men % in Graduate Jobs, Women% with Degrees+, Men % with Degrees+, Women

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 FormalOverqualification

RealOverqualification

56

Data• Employment in Britain (1992)• 1997 Skills Survey• 2001 Skills Survey• 2006 Skills Survey

– UK-wide, but here restricted to employees, 25-60 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

FormalOverqualification

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 43.9 6.7 49.9 50.1 30.8 89.7

58

Prevalence of Graduate Overqualification by Education Characteristics (Row percentages summing to 100%)

Real Over-

qualification

Formal Over-

qualification

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 4.7 65.2

Non-UK 14.8 21.4 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

59

Education/job matching for graduates1992 1997 2001 2006

MEN:

Matched 78.3 77.0 73.0 66.8Overskilled 15.4 - 12.8 15.4Overqualified, of which 21.7 23.0 27.0 33.2 Real Overqualification 7.5 - 7.2 9.9 Formal Overqualification 14.0 - 19.8 23.4WOMEN:

Matched76.2 74.8 76.6 68.0

Overskilled12.2 - 12.0 12.7

Overqualified, of which23.8 25.2 23.4 32.1

Real Overqualification 7.2 - 5.9 8.4

Formal Overqualification 16.1 - 17.5 23.7

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) (0.050) (0.048)

Observations 1172 888 1657 2126

R-squared 0.43 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) - (0.055) (0.042)

Qualification Matched but Skills Underutilised

-0.229 - -0.189 -0.046

(0.069) - (0.092) (0.056)

Observations 1163 - 1657 2125

R-squared 0.44 - 0.41 0.46

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

(0.048) - (0.060) (0.056)

Formal overqualification -0.250 - -0.386 -0.407

(0.062) - (0.047) (0.033)

Qualification Matched but Skills Underutilised

0.109 - -0.076 -0.196

(0.058) - (0.088) (0.058)

Observations 1134 1640 2306

R-squared 0.49 0.57 0.56

62

Cond. association of overqualification with job satisfaction1992 2001 2006

MenReal Overqualification -0.429

(0.461)-0.875 (0.317)

-1.315 (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

WomenReal Overqualification -1.670

(0.459)-1.229 (0.420)

-1.270 (0.358)

Formal Overqualification

0.160(0.278)

0.389(0.195)

-0.294(0.177)

Qualification Matched & Skills Underutilised

-0.750(0.557)

-0.545(0.315)

-0.341(0.281)

Mean job satisfaction of graduates

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.

.2.3

.4.5

.6

1992 1997 2001 2006Year

Matched 10 Matched 90

Average 10 Average 90

64

Returns to graduate education at 10th/90th percentiles for women

• Same story for women.

.2

.3

.4

.5

.6

.7

.8

1992 1997 2001 2006 Year

Matched 10 Matched 90 Average 10 Average 90

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

Discussion

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)

67

Alternative explanationsSupply-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)?

68

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.