Overskilling and Overeducation

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PRIFYSGOL ABERTAWESWANSEA UNIVERSITY

• Over-skilling and Over-education
• Peter J Sloane, Director, WELMERC, School of
  Business and Economics, Swansea University, IZA,
  Bonn and University of Melbourne
• Abstract
• There is now a substantial literature on the
  concept of over-education, but due to data
  availability a much smaller one of the concept of
  over-skilling. This paper compares and contrasts
  these two concepts. The policy relevance of
  over-education depends on the extent to which it
  represents a mismatch between workers levels or
  types of education and the requirements of the
  job. However, is there is substantial
  heterogeneity among individuals with particular
  levels of education, over-educated workers may
  simply be those with lower ability levels given
  their level of education, so that there is no
  market failure. Using data on over-skilling from
  both Australia and Britain the paper argues
  first that over-skilling and over-education
  measure different things and second that the
  over-skilling measure is more likely to capture
  true mismatch than the over-education measure.
• CEDEFOP Research Arena Workshop on Skill
  Mismatch Identifying Priorities for Future
  Research Thessaloniki, Greece, 30 May 2008.

WELMERC
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• INTRODUCTION
• In Australia the university participation rate
  rose from 24 in 1988 to 38 in 1999, while in
  Britain, the participation rate rose from 13 in
  1980 to 33 in 2000.
• This rapid increase has led to concerns about
  employer employee mismatches (i.e. graduates
  in non-graduate jobs). Over-education rates are
  about 30 in both countries.
• Over-educated workers are paid more than matched
  co-workers, but less than matched individuals
  with the same qualifications as themselves.
• Does this represent individual heterogeneity or
  market failure?
• The paper examines
• Whether overskilling is substantial in the
  two countries and has a similar pattern
• Whether there is a sizeable wage penalty in
  each country.

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• OVERSKILLING AND OVEREDUCATION
• In both HILDA and WERS 2004 individuals report
  the extent to which they utilise their skills and
  abilities in the workplace
• This is less subject to bias as a consequence of
  individual heterogeneity than the over-education
  variable.
• The two variables measure different things. Green
  and McIntosh (2002) found that less than half
  over-educated were also overskilled. They found
  20 of British workforce were overskilled and 4
  under-skilled.
• Four possibilities
• Education and skill matching (- professional
  degrees)
• Overeducation, but skill matching (- individual
  heterogeneity)
• Education matching, but overskilling (- grade
  inflation)
• Both over-education and overskilling (-
  constrained job search)

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• THE DATA
• HILDA is a panel of about 20,000 individuals,
  which has run from 2001
• WERS is cross-section matched employer employee
  data set, containing 2,295 establishments and up
  to 25 employees per establishment
• HILDA measures overskilling on a 7 point scale
  from 1 strongly disagree to 7 strongly agree on
  answers to the statement
• I use many of my skills and abilities in my
  current job

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• WERS measure is derived from the question
• How will do the skills your personally have
  match the skills you need to do your current
  job?
• There is a five point scale defined as much
  higher, a bit higher, about the same, a bit
  lower, much lower.
• In HILDA 11.5 are severely overskilled (1, 2 or
  3) and 30.6 moderately so (4 or 5).
• In WERS 21.1 are severely overskilled and
  31.9 moderately so.

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• OVER-EDUCATION AND OVERSKILLING A COMPARISON
• We assess the strength of the relationship
  between the two variables, using HILDA only and
  the empirical method to estimate overeducation
• Three measures
• Definition 1- One education level above the modal
  level of education
  within the occupation
• Definition 2 - One standard deviation above the
  mean level of education
• Definition 3 - Half a standard deviation above
  the mean level.
• Whatever the definition 50 of those classified
  as over-educated were also overskilled, and of
  these 20 were severely overskilled and 30
  moderately so.
• Table 3 The effect on wages
• Model 1 -Overskilling alone
• Model 2 -Overeducation alone
• Model 3 Combined

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Table 3 The effects of overskilling and
overeducation on wages - comparison of
alternative overeducation definitions
Note Dependent variable is weekly wages.
Standard errors in parentheses. // denote
significance at 1, 5 and 10 respectively.
Source Hilda survey waves 4 and 5.
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• PATTERNS OF OVERSKILLING IN AUSTRALIA AND BRITAIN
• Incidence is measured for full-time workers only,
  using weekly earnings and correcting for hours
  worked, for different levels of education and
  using comparable explanatory variables in the two
  data-sets.
• Table 4 suggests
• Overskilling is more prevalent in Britain than in
  Australia
• In Australia it declines with educational level,
  whilst in Britain it is invariant to educational
  level.

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Table 4 Overskilling by education
Note Full-time employees only. Source Hilda
2001-2006 and WERS 2004.
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• ESTIMATION
• We estimate a standard wage regression in which
  log of weekly wages is regressed on a vector of
  characteristics for individual i in workplace j
• Where includes a vector of
  individual characteristics such as gender,
  marital status, age,tenure and
  educational attainment
• includes a vector of employment
  characteristics such as size of establishment
  and industry
• is a dummy for severe overskilling
• is a dummy for moderate overskilling
• denotes estimated returns to the
  characteristics vector
• is standard error
  term.

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Table 8 OLS and interval regression estimates
for effects of overskilling on weekly wages -
Australia vs. Britain
Note Standard errors in parentheses. Reference
groups are as follows age 16-17 education
attainment below yr 10 employed with current
employer for less than a year employed on
continuing contract with a firm that employs at
least 50 people. // denote significance at
1, 5 and 10 respectively. NA denotes that R
square statistics are not available for interval
regression equations.
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Table 9 Effects of overskilling on weekly
earnings by education level
Note Standard errors in parentheses. OLS
regression results for Australia and interval
regression results for Britain, with weekly wage
as the dependent variable. A large number of
covariates has been included and is reported in
Appendix Tables A5a, A5b and A5c. // denote
significance at 1, 5 and 10 respectively. NA
denotes that R square statistics are not
available for interval regression equations.
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Table 10 Effects of overskilling on weekly
earnings by gender
Note // denote significance at 1, 5 and
10 respectively. NA denotes that R square
statistics are not available for interval
regression equations.
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• CONCLUSIONS
• Both countries have a problem of overskilling,
  but the effects are greater in Britain than in
  Australia.
• In Australia incidence falls with educational
  level, whilst it is invariant to education level
  in Britain. In both countries the wage penalty
  increases with education.
• In the long run any benefits to employing
  overskilled workers are likely to be eroded by
  lower job satisfaction and a higher propensity to
  quit.
• There are likely to be costs to the economy in
  terms of lost output.