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 PRIFYSGOL ABERTAWE SWANSEA UNIVERSITY WELMERC

1. 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  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 overskilling is substantial in the two countries and has a similar pattern Whether there is a sizeable wage penalty in each country. Whether there is a sizeable wage penalty in each country.

2. 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)Education and skill matching (- professional degrees) Overeducation, but skill matching (- individual heterogeneity)Overeducation, but skill matching (- individual heterogeneity) Education matching, but overskilling (- grade inflation)Education matching, but overskilling (- grade inflation) Both over-education and overskilling (- constrained job search)Both over-education and overskilling (- constrained job search)

3. 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”

 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.

4. 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  Clearly the two measures are different. They both have a significant negative effect on earnings.

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.

5. 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 i.Overskilling is more prevalent in Britain than in Australia ii.In Australia it declines with educational level, whilst in Britain it is invariant to educational level.

Table 4: Overskilling by education Note: Full-time employees only. Source: Hilda and WERS 2004.

6. 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 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 includes a vector of employment characteristics such as size of establishment and industry is a dummy for severe overskilling is a dummy for severe overskilling is a dummy for moderate overskilling is a dummy for moderate overskilling denotes estimated returns to the characteristics vector denotes estimated returns to the characteristics vector is standard error term. is standard error term.  Table 8 reveals wage costs for severely overskilled of 8.5% in Australia and 12% in Britain, with penalties for the moderately overskilled of 2.3% in Australia and 2.9% in Britain. Returns to obtaining a degree relative to no qualifications are, however, higher in Britain (56% compared to 42%).  Table 9 reports the effects of over-skilling on earnings by educational level. These seem to increase with educational level in both countries.  Table 10 shows that the effects tend to be stronger for men than for women.

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 ; 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.

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.

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.

7. 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.