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) Cobwebmodel
Graduate
wage
supply
Wage
associated
with zero
rents
demand
Graduate
employment
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
with university degree
stayed on at school past 18
with any degree
Proportion of 25-30 year olds
.35
.3
.25
.2
.15
1978
1982
1986
1990
Supply of Education (Men)
1994
year
1998
2002
2006
with university degree
stayed on at school past 18
with any degree
Proportion of 25-30 year olds
.4
.35
.3
.25
.2
.15
1978
1982
1986
1990
Supply of Education (Women)
1994
year
1998
2002
2006
Degree versus no qual
Post 18 versus compulsory
Differences in wages controlling for age
.7
.65
.6
.55
.5
.45
.4
1978
1982
1986
1990
1994
year
Men
1998
2002
2006
Degree versus no qual
Post 18 versus compulsory
Differences in wages controlling for age
.85
.8
.75
.7
.65
.6
.55
1978
1982
1986
1990
1994
year
Women
1998
2002
2006
Differences in employment rates controlling for age
Degree versus no qual
Post 18 versus compulsory
1.4
1.3
1.2
1.1
1
1978
1982
1986
1990
1994
year
men
1998
2002
2006
Differences in employment rates controlling for age
Degree versus no qual
Post 18 versus compulsory
1.8
1.6
1.4
1.2
1
1978
1982
1986
1990
women
1994
year
1998
2002
2006
bhps
fes
Men graduate jobs
Men non graduate jobs
.4
.08
.35
.06
.3
.04
.25
.02
1978
1984
1990
1996
2002
2008
1978
Women graduate jobs
1984
1990
1996
2002
2008
2002
2008
Women non graduate jobs
.1
.4
.08
.06
.35
.04
.3
.02
1978
1984
1990
1996
2002
2008
1978
1984
1990
1996
year
Proportions of Workers with Higher Ed
10th percentile FES
90th percentile FES
10th percentile BHPS
90th percentile BHPS
.8
.6
.4
.2
1978
1982
1986
1990
1994
year
1998
Quantile estimates of Ed diff (Men)
2002
2006
10th percentile FES
90th percentile FES
10th percentile BHPS
90th percentile BHPS
1
.8
.6
.4
.2
1978
1982
1986
1990
1994
year
1998
2002
Quantile estimates of Ed diff (Women)
2006
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 overeducation “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
W
Supply of graduates
Supply of non
graduates
Q
L
W
Supply of graduates
Supply of non
graduates
Q
L
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)
W
Supply
graduates
Supply non
graduates
Constant
marginal
product for
simplicity
N
W
Marginal cost of
hiring graduate
Supply
graduates
Marginal cost of
hiring non
graduate
Supply non
graduates
demand
N
W
Marginal cost of
hiring graduate
Supply
graduates
Marginal cost of
hiring non
graduate
Supply non
graduates
demand
Small wage premium
here and large diff in
employment
N
W
Marginal cost of
hiring graduate
Supply
graduates
Marginal cost of
hiring non
graduate
Supply non
graduates
demand
Note that could have
drawn graph to get a
NEGATIVE or zero
premium
N
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
Earnings
Stays at school until E
B
A
Leaves school at F
O
G
+
E
F
Age
C
D
65
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 OverEducation
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
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
43
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 fulltime, first
degree
leavers who
entered fulltime
employment
in the UK
Graduates in
professional
jobs earn
more than
their
counterparts
in nongraduate
occupations
Associate
professionals
in between
44
Trend in over-education: DLHE 2004-8
Types of job
Examples
2004
2005
2006
2007
2008
Solicitors, research scientists,
architects, medical practitioners
11.1%
11.2%
11.5%
11.7%
12.4%
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%
Any jobs that do not fall into the above
categories
39.1%
37.5%
35.6%
33.5%
34.3%
60.9%
62.5%
64.4%
66.5%
65.7%
Traditional graduate
occupations
Modern graduate
occupations
Non-graduate
occupations
Total in graduate
occupations
• Graduate job
classifications
were
developed by
Professors
Peter Elias and
Kate Purcell for
their study
Seven Years
On: Graduates
in the
Changing
Labour
Market.
45
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
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
48
Measure of stock of graduates
.4
•
.3
.35
•
•
.25
•
Figure updated with
two additional years of
data (up to Dec 2009)
It shows share of
graduates in the labour
force (proportion of 2560 year olds with NVQ
4+)
Rapid rise throughout
period.
For women an
apparent acceleration
after 2002
1994199519961997199819992000200120022003200420052006200720082009
Year
Men
Women
49
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
50
.2
.3
.4
.5
.6
QR/OLS est. of the college premium for women
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year (3-year moving window)
10th Percentilee
90th Percentilee
50th Percentilee
OLS
• 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.
51
.25
.3
.35
.4
.45
QR/OLS est. of the college premium for men
1995
1996
1997
1998
1999
2000 2001
yr3mw
10th Percentilee
90th Percentilee
2002
2003
50th Percentilee
OLS
2004
2005
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.
52
0.0
0.1
0.2
0.3
0.4
0.5
Changing composit. of the lowest quintile of the residual wage dist.
1994199519961997199819992000200120022003200420052006200720082009
Year
% in Graduate Jobs, Men
% with Degrees+, Men
•
% in Graduate Jobs, Women
% with Degrees+, Women
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.
53
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.
54
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.
In Graduate Jobs
In Non-Graduate Jobs
Skills Fully Utilised
Skills Underutilised
Matched
Qualification Matched
and Skills Underutilised
Formal
Overqualification
Real
Overqualification
55
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
56
Validation of classification of overqualification
(% of graduate
employees)
Real
Overqualification
Formal
Overqualification
Qualification
Matched and Skills
Underutilised
Qualification
Matched and Skills
Utilised
In SOC
Major
Groups
1-3
Learning
Time Over 2
Years
Learning
Time Under
1 Month
Requirement
to Learn
New Things
Influence
Skills
Requirement
Complex/
Advanced
Computing
Skills
Requirement
8.0
38.0
19.5
5.0
11.3
30.9
26.3
13.1
36.7
25.7
25.0
58.0
30.5
7.1
43.9
26.2
34.2
79.4
43.9
6.7
49.9
50.1
30.8
89.7
57
Prevalence of Graduate Overqualification by Education
Characteristics (Row percentages summing to 100%)
Real
Overqualification
Formal
Overqualification
QualificationMatched and
Skills
Underutilised
QualificationMatched and
Skills Utilised
Below A-Level
9.4
24.9
4.7
61.1
A-Level or above
5.3
13.7
6.4
74.6
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
Below Upper Second
12.8
20.0
3.5
63.7
Upper Second or First
9.3
15.6
4.6
70.4
Maths Level **
University Type
Degree Grade*
58
Education/job matching for graduates
1992
1997
2001
2006
MEN:
Matched
Overskilled
Overqualified, of which
Real Overqualification
Formal Overqualification
78.3
15.4
21.7
7.5
14.0
77.0
23.0
-
73.0
12.8
27.0
7.2
19.8
66.8
15.4
33.2
9.9
23.4
WOMEN:
Matched
76.2
74.8
76.6
68.0
Overskilled
12.2
-
12.0
12.7
Overqualified, of which
23.8
25.2
23.4
32.1
Real Overqualification
7.2
-
5.9
8.4
Formal Overqualification
16.1
-
17.5
23.7
59
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)
-0.240
-
-0.175
-0.322
(0.055)
-
(0.055)
(0.042)
-0.229
-
-0.189
-0.046
(0.069)
-
(0.092)
(0.056)
Observations
1163
-
1657
2125
R-squared
0.44
-
0.41
0.46
Formal overqualification
Qualification Matched but
Skills Underutilised
60
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
Formal overqualification
Qualification Matched but
Skills Underutilised
-0.441
-
-0.430
-0.642
(0.048)
-
(0.060)
(0.056)
-0.250
-
-0.386
-0.407
(0.062)
-
(0.047)
(0.033)
0.109
-
-0.076
-0.196
(0.058)
-
(0.088)
(0.058)
Observations
1134
1640
2306
R-squared
0.49
0.57
0.56
61
Cond. association of overqualification with job satisfaction
1992
2001
2006
-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
-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
Men
Real Overqualification
Women
Real Overqualification
•
•
•
•
Only real overqualification has a
significant effect on job
dissatisfaction, as
highlighted in blue.
Formal overqualification 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.
62
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.
63
Returns to graduate education at 10th/90th percentiles for women
• Same
story for
women.
64
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
65
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 overflooding 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 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)?
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.