OVERQUALIFICATION AND CAREER. Simonetta Longhi Malcolm Brynin ISER University of Essex

Transcription

1 OVERQUALIFICATION AND CAREER Simonetta Longhi Malcolm Brynin ISER University of Essex 1

2 OVERQUALIFICATION AND CAREER Abstract A considerable proportion of the labour force is overqualified or overeducated for the work that it does. This leads to a wage penalty relative to those who are correctly matched. Various reasons have been given. One is that overqualification reflects variation in individual skills which relates to demand in ways which are generally unmeasured. Another refers to career. Overqualification might be a temporary phenomenon, related to poor information and therefore initial career uncertainty. Over the career matching improves incrementally; panel data are therefore needed to test its extent and wage effects. This paper measures the longevity of overqualification, analyses the joint effects of overqualification on wages, and the effect of overqualification on job exits. The analysis uses the GSOEP ( ), supported by the eliving survey (2001-2). The result show that overqualificaion is not a short-run phenomenon, and has long-term negative career consequences, both on pay and on the probability of exits from employment. Introduction A significant proportion of employees are apparently over-educated or overqualified for the jobs they do (Borghans and de Grip, 2000; Hartog, 2000). Yet, despite the consistent finding of overqualification (OQ) in many countries and periods, explanations for the phenomenon are sparse. This is partly because, according to human capital theory, which is the most widely accepted account of the demand for education, OQ should not arise. Although it is possible to view OQ in terms of the likely implications for other theories such as job queueing (Thurow, 1979) or assignment theory (Sattinger, 1993), according to human capital theory people are simply not expected to invest in education which they cannot fully utilise. There have as a consequence been several refinements to research on OQ designed to resolve this problem. For instance, the heterogeneous skills within qualification levels theory (Green and McIntosh, 2002) builds on the idea that a deficit in formal education can be balanced by superior skills or work experience (Groot and Maassen van den Brink, 2000). In this case OQ indicates a complex matching process consisting of more than one component of human capital, where a deficit in one element might be compensated by a surplus on another. Nevertheless, how this works on the demand side is unclear. It seems likely that employers generally expect a high correlation between qualifications, skills, and motivation (with the first of these signalling the others). If OQ does sometimes signal higher skills than might be expected for the job, it is nevertheless difficult to believe that oversupply has become a structured part of the recruitment process. At least Groeneveld and Hartog (2004) find no direct evidence that employers systematically seek the overqualified in order to obtain these higher skills. However, on the supply side, OQ could be used by job-seekers as a signalling device for their skills, perhaps especially as an insurance against job-search failure (Büchel, 2001). Although at least within a single firm there is limited evidence for overqualification as an individual career investment (Groeneveld and Hartog, 2004), the expansion of education could add to the competitive pressures that people feel, and so encourage excess education. Employers might then sort by real skills, because qualifications become less reliable indicators of ability. However, this in turn implies that people do not necessarily invest in education in the way predicted by human capital theory. 2

3 A second approach to the problem of OQ minimises its significance. In this view, some overinvestment can arise through distortions in the market, especially stemming from inadequate information on job opportunities, or because of potential career constraints such as family responsibilities. The idea of career postponement or mobility (Sicherman and Galor, 1990) in a related manner suggest that there can be rationale reasons for entering employment in too low a position. The idea is that matching takes time, increasing as careers evolve. This seems reasonable, although the evidence is as yet unclear. For instance, while Dolton and Vignoles (2000) suggest that OQ lasts some time into graduate careers, Hartog (2000) and Dorn and Sousa-Poza (2006) find an increasing tendency for potential employees to take first jobs below their educational value, and subsequently to find a match. Thus we need to be sure that overqualifiation is indeed a temporary career phenomenon (which might also, of course, reflect variation in skills). Below we examine both issues, but especially that of time, using the GSOEP and the eliving dataset. In common with Büchel and Mertens (2004), who demonstrate a negative relationship between OQ and wage growth, we do not view the existence of OQ as a career investment. The effects of time The ways in which OQ might evolve over time are complex. First, in the aggregate, it seems that while trends in rates occur, these are not consistently up or down, especially across countries (Hartog 2000), though any analysis of trends is made difficult as a result of methodological problems. The statistical method of calculating OQ (defined as the typical education found in a particular type of job), for instance, is inappropriate as trends simply measure changes in the structure of occupations. The objective method (managerial definitions of the education needed for a job) are problematic, as it is likely that changes in such definitions respond poorly to real job changes, but anyway data based on this method are rare. The most common subjective method, based on asking workers what education is needed for their job, is the best way forward, but has problems of its own. In particular, the questions is often asked in slightly different ways, so that comparability over time is doubtful; in addition, it is possible that the way respondents interpret the question might also change over time. What is being measured, therefore, might not be real change in the educational requirements of jobs but people s subjective perceptions of the values of these jobs (though of course these might well be correlated with each other). Despite these problems, the analysis of trends is extremely important as it can say something about the changing relationship between the educational system and the labour market. Rising OQ might indicate, for instance, an increasing gap between what people want out of education and what they want out of jobs. This leads to the issue of career, which is the second important way in which time is important. OQ can alter in only a limited number of ways over a career. It can of course change when educational qualifications are gained incrementally, especially in countries where students start work in order to finance their studies, though deliberate retraining after a career start is also a possibility. This issue, however, is not inherently interesting but, rather, simply creates a measurement problem. If we instead take unchanging education as a given, changes in OQ derive from changes in employment only. Any OQ people are faced with in their work can of course be eliminated when people leave employment. If this happens, what we want to know is whether the state of being overqualified encourages employment exits, as this would suggest the possibility of a degree of unwanted mismatch. This is, however, difficult to measure, because those most likely to leave employment might 3

4 also be those who are most likely to be overqualified. A second source of change occurs when people change employers. The new job might well have a different educational requirement from the previous one. The change could of course result in either upward or downward mobility, or simply a horizontal move. With the exception of people who are deliberately downsizing jobs, for instance as a result of family commitments, we would expect career progression to be upward. Thus, over a career OQ should decline that is, people should become less overqualified than they were at the outset of their careers. Finally, changes of job occur without a change of employer, which are also likely to result in either horizontal or upward mobility. Insofar as mobility is upward this might be partly because matching in the modern world takes time, perhaps as much in the labour market as in the marriage market. This might be more likely if it is true that work, as is often argued, has become more flexible. For instance, in a twenty-year period from 1975 the proportion of the British population of working age in full-time, permanent work fell by around 20 percentage points from around 56% to 36%, and job-entry wages fell relative to the wages of those in continuous employment (Gregg and Wadsworth, 1995). This sort of change could raise uncertainty and encourage people to obtain some work experience before settling down to a career, and in this case OQ should decline most sharply early on in a career. Nevertheless, Gregg and Wadsworth cast doubt on many aspects of the apparent trend towards flexible employment, doubts confirmed elsewhere: for instance Burgess and Rees (1996); European Commission (2003: ); OECD (2003: 50-52). It is also not unreasonable to suppose that the end of jobs for life should create uncertainty at the end rather than the start of a career. In addition to this problem of the conceptualisation of the significance of OQ, it is possible that OQ defines not employment situations but, rather, differences between people. Just as in marriage there are people who are less qualified to find any match, or a match they would ideally like, because they lack certain personal qualities, it is possible that OQ marks people who also lack particular desirable qualities. This is obviously different from having inadequate education, which might indicate people who have little interest in education are therefore likely to obtain poor jobs. The overqualified, in contrast, might describe people who value education highly, whether for its own sake, or for the social cachet it confers, or perhaps simply because it postpones real decisions, but who perhaps have no clear idea what job they want, or even what level of job they want. In this case, they not only enter the labour market overqualified but have a relatively high probability of remaining overqualified. This probability is likely to be accentuated if the structure of education matches poorly to the labour market, encouraging people to obtain qualifications for their own sake. We are not able to test such distinctions directly below but we assume they might be important contributory factors to the phenomenon of overqualification. This discussion leads to a number of reasonably clear hypotheses. First, if we accept that the structure of education is growing with only a weak relationship to labour-market requirements, we might expect significant OQ at all education levels rather than just at the higher level, while skill variation might make rather little difference to this. Second, we would expect rising OQ over time. Third, we would expect OQ to be associated with reduced commitment to the labour market, and in particular with labour-market exits. In all cases, it should be noted that high OQ is less likely in West Germany, on which our analysis is based, as the relationship between education and employment is relatively tight there compared to most other countries. The tests of our hypotheses are in this sense fairly stringent. 4

5 Data We first show results based on the eliving survey. This was funded by the EU s IST Programme. The project was based on a household survey of 1750 homes in six countries Britain, Bulgaria, Germany, Israel, Italy, Norway in The analysis presented below covers only Germany and Britain. Interviews were by telephone and all were with one randomly selected adult aged 16 or over in each home. The survey used equivalent sampling strategies, the same questionnaire wording, as well as a single co-ordinating survey organisation. Initial response rates were low, around 40% in wave 1. However, over 65% of these were re-interviewed in a second wave. The analysis pools the two waves and is based on a weighted version of the data designed to compensate for non-response bias. The questions relating to construction of the overqualification variables are: a) What qualification does someone usually need to be able to do your job? b) What is the highest qualification that you have? OQ is best calculated from a direct comparison of qualifications held and required, ideally at all appropriate educational levels. The data also include information on computer skills. This measure is based on six questions asking whether the respondent knows how to download files from the web, to construct a web page, to a file, to cut and paste, to reboot, and to copy files to a floppy. Most of the analysis is based on the GSOEP, taking the West German sample only. These data are used to calculate two measures of OQ. We mostly use the standard variable, what training is necessary for your job? However, this has some limitations. In particular, it seems to be primarily intended to describe vocational education, which is indeed important in Germany, but matches to the measures of achieved education only to a limited extent. This means that the standard variable cannot be created, whether is defined by the gap between achieved and expected qualifications measured directly or differences in actual and expected years of education. Whatever measure is produced cannot, therefore, be compared to measures derived from other surveys. Some of the difficulty can be reduced through the addition of information, for instance on occupations, to locate mismatches more precisely, as undertaken especially by Buchel and Mertens (2004). We choose not to do this here as doing so requires judgements of the value of occupations, which, while certainly helpful, are difficult to make. Nevertheless, there is no reason to think the basic GSOEP variable is not internally consistent, and therefore it can be used to analyse change over time, which is the focus of this paper. The first way in which we measure over-qualification is by comparing the qualification held by each employee with the qualification required for the job that person is doing. Qualifications held by the respondent are classified into four groups: none, low, intermediate, and high. Individuals classified in the group with no qualifications have at most a general elementary or basic vocational certificate; individuals classified in the group with low qualification hold intermediate general or intermediate vocational certificate. Those individuals belonging to the group of intermediate qualification hold a general or vocational maturity certificate; while those belonging to the group of high qualification hold a lower or a higher tertiary degree. The qualification required for the job is similarly classified into four groups: none, low, intermediate, and high. Jobs and therefore employees classified in the no qualification group require no sort of training; jobs classified in the low qualification group only required some (firm-specific) training, such as a short 5

6 introduction to the job, on-the-job training, or special courses. Jobs classified in the group of intermediate qualification require vocational training, while jobs classified as high qualification require education at the level of technical college or university. Over-qualification is computed by the combination of the qualification held by each individual, and the qualification required for the job they are employed in. The over-qualification dummy is one in all those cases in which the qualification held is higher than the qualification required for the job. In all other cases matched or under-qualified individuals the over-qualification dummy is zero. The second way in which we measure of OQ is by using the empirical method. This we do through taking two-digit (i.e. minor) ISCO groups, calculating the modal years of education for workers in this group, and defining OQ as any individual excess over this average. Then we create a parallel measure using the Goldthorpe classes instead of the ISCO categories, as these classes are supposed to be relatively homogeneous in respect of social status and therefore, implicitly, expected education. This general type of procedure is generally criticised as making the analysis of trends impossible, because changes in OQ would simply reflect changes in the occupational structure. In the case of a panel, however, this is actually quite useful. Change in OQ over time reflects not only a person s career but how that person s educational achievement relates to this. This therefore is not solely career change, which could after all be measured directly, but career progression in terms of an educational metric. How long does it take for people to arrive in a job for which they are educationally matched? Does this matching change much by age? Results Variation in skills We mentioned in the introduction that there are two main ways in which the findings of persistent OQ can be squared with human capital theory. One is through variation in skills at specific educational levels, with the least skilled likely to be overqualified; the other operates if OQ is only a temporary career phenomenon. If neither of these hold, then human capital theory remains problematic. In addition, we argued that if OQ is apparent at different educational levels, then this is also likely to indicate a problem with the concept of human capital. This is partly because career progression is less important at the lower levels, so change over time is extremely unlikely to be important in this instance. Using the eliving data, we have estimated (Brynin et al., 2006) the impact of overqualification on wages by means of a modified Mincer regression: Ln w i = α + X i β + Q i + ε i (1) where the dependent variable is the logarithm of individual wages, X i is a vector of individual characteristics - gender; age and its square; dummies for whether the job is permanent and whether the individual has a fixed working schedule; size of the working place; three dummies for industry; while Q i is a set of OQ dummies defined by any combination of actual and expected qualifications. The number of categories of education can of course vary. We use four for this purpose, which produces ten dummies that relate required to actual qualifications. Four refer to well-matched individuals: Matched Degree has value 1 for all individuals with a degree working in a job for which a degree is required. Similarly, Matched HSL, Matched LSL and Matched Low have value 1 for all individuals with a higher secondary level 6

7 certificate (Abitur or A-levels), lower secondary level (such as Realschule or GCSEs), and a low education level respectively, who are matched for their job. Three dummies characterise overqualified individuals, and three characterise the underqualified. The three OQ dummies represent different levels of OQ: has Degree is 1 for overqualified graduates; has HSL certificate is 1 for overqualified individuals with a higher school leaving certificate; has LSL certificate is 1 for those with a low school leaving certificate. We show the underqualified as a single group whatever their level. In Table 1 we show for Britain and Germany the distribution of OQ based on a direct comparison of achieved and required qualifications, using eliving. TABLE 1 ABOUT HERE In neither country is more than one third of graduates overqualified. In all cases the proportion of people with higher school-leaving certificate who are overqualified is much higher than one third, while it is at least one third in three countries even at the lower school level. This suggests that OQ is general, and not specific to higher education. This is more the case in Britain than in Germany but it applies to both countries. It suggests that people with low qualifications, and who are therefore unlikely to experience much career mobility, but who are nevertheless overqualified for the jobs they enter, are relatively likely to remain overqualified for their entire careers (though we cannot know this from these data). Does our measure of skills add anything to the understanding of OQ? In Brynin et al. (2006) we have shown that workers who are over-qualified tend to have higher (computer) skills than workers in the same job who are correctly matched, but at the same time lower skills than those in the same jobs as themselves who are, in contrast, educationally matched. Table 2 shows that when we regress pay on a range of variables, including these measures of OQ, in most cases higher pay does accrue to an overqualified person when compared to someone matched at the next level down (e.g. an overqualified graduate compared to matched high school leaving certificate). However, the addition of computer skills reduces the effect of both education and OQ equally. There is simply variation in skills at all educational levels. There is no specific relationship between computer skills, OQ and wages. TABLE 2 ABOUT HERE The effects of time We now turn to GSOEP data to use its panel properties to test whether time reduces the extent and impact of OQ. First of all we show a simple result for the second measure of OQ discussed above, the measure based on the statistical method. In Figure 1 we show the average length of OQ for all over-qualified individual in each wave, using in the upper graph OQ based on ISCO and in the lower on Goldthorpe. The calculations are only of the overqualified, and of course we do not know for how long people were over-qualified before entering the GSOEP sample. While it was argued above that we cannot look at trends using this measure to produce an account 7

8 of changing OQ over time, we would at least expect a declining trend, unless the overqualification of new entrants to employment outweighs the effects of career progression. The apparent increase in the duration of individual OQ revealed by Figure 1 is only one third of a year over the 21 waves, but it is by the same token not negative. Given that we exclude those who are matched or underqualified, this suggests that the aggregate effects of career are not such as to cause a decline in the aggregate amount of OQ individuals. If people are reducing their OQ through career progression, this is insufficient to counteract the high OQ of new entrants into employment. FIGURE 1 ABOUT HERE It is in fact by no means clear that OQ does decline by very much over the career. In Table 3 we show average OQ, again measured by excess years, for a number of fiveyear age groups, and change in these measures for the same groups (except the first) over two time periods three years and nine years (where the samples for each are the same). The first column shows a very slight decline in OQ over age, amounting in total to about half a year. The next two columns show the actual change, and this is negligible over a three-year period. Over nine years we get a reduction of at the most one fifth of a year, and this seems to occur towards the end of a career. TABLE 3 ABOUT HERE As people move up in their career the average years of education for the job also rises, so their original OQ will decline. But this general career progression is not enough to reduce OQ substantially. If matching does take place over the career, for instance through promotion, then this is slow and slight. The rest of the analysis uses the more commonly used information on required job training (the first measure of over-qualification discussed above). It is of some importance, however, that the two measures correspond quite well. We get 8.6% of the labour force overqualified using the required training variable, and 9.6% using the above method based on ISCO (though taking an arbitrary definition of OQ as having over 2 years excess education to discount marginal differences). The Goldthorpe version, however, produces 15.5% - presumably because these categories are much larger and therefore contain greater variation in years of education. It is of even greater interest that the various measures seem to overlap to a large degree. This is shown in Table 4 (which is restricted to those aged between 25 and 60). TABLE 4 ABOUT HERE In Figures 2 and 3 we show the proportion of people who are overqualified over time, using our first OQ measure. 8

9 FIGURES 2-3 Figure 2 shows the number of individuals who are over-qualified for a certain number of years. The figure suggests that although the highest number of overqualified individuals has a vocational degree, on average, individuals with a college degree are over-qualified for a longer part of their career. Although the figures are small, it can be seen that OQ can last a number of years for a significant proportion of those who are overqualified at all. However, these are not necessarily continuous spells. Figure 3 shows the number of individuals who are overqualified for a certain number of consecutive years. Similarly to Figure 2, this suggests that there the biggest number of overqualified individuals have a vocational qualification. However, for most individuals with a college degree overqualification seems to last one year longer (3 years) than for those individuals with a vocational degree (2 years). Some individuals are overqualified for longer, however. Having shown, in support of our hypotheses, that OQ can last some time, even though extensive durations are small in scale, we now proceed to test the wage effects of being overqualified. Our main aim here is to see if the negative wage effects of OQ are substantially attenuated by tenure. We argued that OQ might be an indicator of a reduced work ethic or commitment to career; a strongly positive relationship between OQ and tenure would not be expected if this is the case. We estimate the following wage regression: Ln w it = α i + X it β + Q it + OQ it + Tenure it + OQ it Tenure it + T + ε it (1) where the dependent variable is the logarithm of individual wages. X it is a vector of individual and job characteristics which includes age; a dummy for whether married; a dummy for being employed part-time; a dummy for having only a marginal or irregular employment; and three dummies for firm size: less than 20; 20 or more but less than 200; and 200 or more but less than 2000 employees. Among the explanatory variables we also add dummies for low, intermediate, and high qualification (Q it ), as well as the dummy identifying whether the worker is overqualified for the job (OQ it ). The variable Tenure it refers to the years of tenure in the job, while T are dummies for the year in which the data has been collected. OQ it Tenure it is the interaction between the over-qualification dummy and years of tenure. While the variable OQ it will capture the wage penalty of an over-qualified individual compared to a matched worker with the same qualification level, the interaction term will capture the extra return to tenure that is associated with having a higher qualification than required for the job. The model is estimated by means of fixed effects; for this reason individual specific time-invariant characteristics, such as gender, have not been included in the analysis. Their effect is picked up by the fixed effects α i. Table 5 estimates the model in equation (1) for all workers, and separately by gender. Consistently with the previous literature, the results suggest that overqualified workers earn less than well-matched people with their same qualification level. The comparison between the last two columns of Table 5 suggests that the gap is bigger for women: women seem to suffer a higher wage penalty for being overqualified. This result might be related to career interruptions of women, which might 9

10 force them to take up jobs for which they are more overqualified than is common amongst men. TABLE 5 ABOUT HERE As expected, Table 5 also suggests that tenure has positive returns on wages. The coefficient of the interaction between tenure and the over-qualification dummy is also positive and statistically significant. Overqualified workers seem to receive extra returns to tenure compared to well-matched workers. This might suggest, contrary to our hypothesis, that workers with higher qualifications who are overqualified learn faster, and therefore gain more specific human capital or gain it more quickly than workers in the same job who are instead matched. We might therefore think that higher qualification pays in the case of mismatches. However, comparison with the negative wage effects of OQ shows that it would take many years for this to be eliminated through the effects of tenure. OQ is not a good career strategy. At best, the overqualified have a slow if steady career progression. We analyse this more explicitly through an examination of the factors affecting exits from OQ, but, more significantly, of the role OQ might play in encouraging exits from the labour market. Table 6 shows the total number of changes in status of over-qualified workers between wave 1 and wave 21. Between 1984 and 2004 there were 396 changes from matched to over-qualified. There were 852 changes in status for over-qualified: while 486 changes were from the status of overqualification to the status of matched, 366 changes were to the status of not employed. Below we investigate changes in status in more details. Only individuals aged up to 60 are included in the analysis. TABLE 6 ABOUT HERE We model the probability of exiting OQ by means of a discrete time duration model specified as a sequential binary model with errors distributed as a complementary log-log distribution. The hazard in period t is: Pr(d t = 1 d t-1 = 0) = 1 exp ( exp (α 0 + X it β + Z i σ + µ i ) (2) where X it includes time-varying characteristics such as age; years of education; tenure in the job; a dummy for whether the worker has changed job but not employer in the last period; a dummy for whether the worker has changed employer in the last period; a dummy for whether working part-time; a dummy for whether working on marginal or irregular employment; a dummy for whether married; and three dummies for the size of the firm: less than 20; 20 or more but less than 200; and 200 or more but less than 2000 employees. Z i includes time-invariant characteristics such as gender, while µ i are uncorrelated with the variables in X it and Z i. The results of the estimation of equation (2) are shown in Table 7. This suggests that changing employer increases the probability of exiting over-education, while changing job with the same employer seems to decrease the probability of exiting the status of OQ. When the change in the job is associated with an 10

11 improvement in either wages or skill-use, as in the second column of Table 7, the probability that the job change leads to exit from the status of OQ becomes positive and rather high. TABLE 7 ABOUT HERE Years of education have a negative coefficient, suggesting that overqualification lasts longer in the career of highly educated workers, than of workers with lower levels of education, while tenure within the firm does not seem to have a relevant impact on the probability of exiting over-qualification. Older workers, nevertheless, are more likely to exit the status of over-qualification, presumably often through retirement. As expected, part-timers and workers with only marginal or irregular work are less likely to exit over-qualification. The coefficients for the female and married dummies are not statistically significant, suggesting that personal individual characteristics which might be only marginally related to the job do not seem to have a relevant impact on the probability of exiting overqualification, although the coefficient for the dummy for women seems to be consistently negative. Additionally, working in a smaller firm might offer more opportunities for career advancement of workers, since the coefficient of the dummies referring to smaller firms are positive and statistically significant. Finally we model also the probability of exiting employment by means of a discrete time duration model in a way similar to equation (2). In this case, however, X it includes age and some characteristics of the job in the year preceding the change in the status. These job characteristics are: tenure in the job; a dummy for whether working part-time; a dummy for whether working on marginal or irregular employment; three dummies for the size of the firm; as well as a dummy for whether married. As before, Z i includes a dummy for gender. The results of the model are shown in Table 8. TABLE 8 ABOUT HERE This suggests that overqualified workers, together with women and workers with only marginal or irregular employment, are more likely to exit the status of employment than well-matched workers. This might suggest that over-qualified workers are more likely to lose their job or to exit the labour force temporarily. Workers with higher tenure, as well as workers employed only part-time are less likely to exit employment. Although higher wages are likely to deter workers from leaving employment, this impact seems rather small. Conclusions Time cures many things, possibly including job mismatches. However, we have argued above that overqualification is not simply the result of inefficiencies in the labour market, characterising people with limited skills relative to their educational level, or who enter their career at a low level, perhaps in order to gain some work experience before deciding on a career. An alternative view is that individual 11

12 mismatches reflect a more structural mismatch between educational outputs and labour-market requirements. We cannot prove this directly but, rather, use the eliving survey to show that overqualification is possibly highest where career development is least likely. It seems that the general expansion of the educational system has raised the whole working population above the educational level required for their work, even in Germany, where there is a relatively close institutional link between education and work. We further show that a measure of computer skills does not seem to mediate in the relationship between overqualification and wages; so variation in skills is probably also not a factor in the explanation of the wage effect of overqualification. We use the GSOEP to test the effects of time. Firstly, we show that a significant proportion of those who experience overqualification at any time in their career are likely to experience this for extended periods. It appears not to be something which only marks the start of a career. This seems to support the general findings of Büchel and Mertens (2004) and leaves open the possibility that the overqualified are particular types of people, who perhaps have ill-defined career objectives, rather than people in a particular stage of their career. We hope to develop this idea further in later versions of the paper. Here though, we do at least test the effects of time both on wages and on employment exits. We do the former through an examination of the joint effects of overqualification and job tenure on wages. Tenure reduces the negative effects of overqualification, but the long-term effect is rather small. Career progression does not eliminate its impact. We then show that the overqualified are more likely than others to leave employment. This brings career progression at least to a temporary halt. Time does not after all necessarily cure an individual s problems in matching their educational achievements to the requirements of the labour market. We cannot therefore assume that overqualification is merely a short-term phenomenon and of little social consequence. 12

13 References Borghans, L. and de Grip, A. (2000) The Debate in Economics About Skill Utilization. The Overeducated Worker? The Economics of Skill Utilization. Ed. by Borghans, L. and de Grip, A. Cheltenham, Edward Elgar: Brynin, M., Lichtwardt, B. and Longhi, S. (2006) Overqualification, Major or Minor Mismatch?, ISER Working Paper Büchel, F. (2001) Overqualification - Reasons, Measurement Issues, and Typological Affinity to Unemployment. Training in Europe. Second Report on Vocational Training Research in Europe Ed. by CEDEFOP:, Descy, P. and Tessaring, M. Luxembourg, Office for Official Publications of the European Communities. 2: Buchel, F. and Mertens, A. (2004) Overeducation, Undereducation, and the Theory of Career Mobility. Applied Economics 36(8): Burgess, S. and Rees, H. (1996) Job Tenure in Britain Economic Journal 106(435): Dolton, P. and Vignoles, A. (2000) The Incidence and Effects of Overeducation in the Uk Graduate Labour Market. Economics of Education Review 19(2): Dorn, D. and Sousa-Poza, A. (2006) Overqualification: Permanent or Transitory. Neuchatel, Switzerland. European Commission (2003) Employment in Europe 2003: Recent Trends and Prospects. Luxembourg. Green, F. and McIntosh, S. (2002) Is There a Genuine Underutilisation of Skills Amongst the Overqualified? London. Gregg, P. and Wadsworth, J. (1995) A Short History of Labor Turnover, Job Tenure, and Job Security, Oxford Review of Economic Policy 11(1): Groeneveld, S. and Hartog, J. (2004) Overeducation, Wages and Promotions within the Firm. Labour Economics 11: Groot, W. and Maassen van den Brink, H. (2000) Overeducation in the Labour Market: A Meta Analysis. Economics of Education Review 19(2): Hartog, J. (2000) Over-Education and Earnings: Where Are We, Where Should We Go? Economics of Education Review 19(2): OECD (2003) ICT and Economic Growth: Evidence from OECD Countries, Industries and Firms. Paris, Organisation for Economic Co-operation and Development. Sattinger, M. (1993) Assignment Models of the Distribution of Earnings. Journal of Economic Literature 31(2): Sicherman, N. and Galor, O. (1990) A Theory of Career Mobility. The Journal of Political Economy 98(1): Thurow, L. (1979) A Job-Competition Model. Unemployment and Inflation: Institutionalist and Structuralist Views. Ed. by Piore, M.J. White Plains, N.Y., Sharpe:

14 TABLES AND FIGURES Table 1: Proportion of matched, over- and under-qualified workers and their hourly wages (certification method) Britain Germany % % Underqualified: Overqualified: has degree has HSL certificate has LSL certificate Matched: has degree has HSL certificate has LSL certificate has low/no qualification Total % overqualified % graduates overqualified % HSL overqualified % LSL overqualified Observations 1,225 1,408 Source: Derived from Brynin et al (2006) 14

15 Table 2: Effects on log of hourly wages of differentials in levels of actual and expected qualifications (certification method) Britain Germany Overqualified: has degree 0.448*** 0.391*** 0.447*** 0.378*** (0.066) (0.064) (0.091) (0.092) has Higher School certificate 0.201*** 0.162** (0.054) (0.053) (0.063) (0.064) has Low School certificate (0.056) (0.054) (0.080) (0.081) Matched: has degree 0.566*** 0.510*** 0.384*** 0.321** (0.047) (0.049) (0.097) (0.099) has Higher School certificate 0.365*** 0.323*** 0.354*** 0.288*** (0.056) (0.055) (0.066) (0.067) has Low School certificate *** 0.218*** (0.070) (0.069) (0.061) (0.063) Computer Skills 0.027*** 0.028** (0.008) (0.010) Adjusted R Observations Robust standard errors in parenthesis; * Significant at 10%, ** Significant at 5%, *** Significant at 1% Other explanatory variables: gender; age and its square; dummy for whether the job is permanent; dummy for whether the individual has a fixed working schedule; size of the working place; three dummies for industry. Source: Derived from Brynin et al (2006) Table 3: Average OQ and Change in OQ by Age (with 3-year and 9-year change on same sample) Age Average OQ 3 year change 9 year change Mean N Mean Mean N

16 Table 4: Average years of education overqualified (ISCO and Goldthorpe measures) by OQ based on required training (with extreme values reduced) OQ status Excess years of education ISCO Goldthorpe Underqualified Matched Overqualified Table 5: Impact of education, OQ and tenure on wages Dependent Variable: ln monthly wages (1) All Employees (2) Men (2) Women Low Qualification Dummy * (0.010) (0.012) (0.018) Intermediate Qualification Dummy 0.033* 0.078*** (0.015) (0.015) (0.027) High Qualification Dummy 0.276*** 0.251*** 0.298*** (0.017) (0.018) (0.033) Over-qualification Dummy *** *** *** (0.010) (0.011) (0.017) Years of Tenure in the Job 0.005*** 0.004*** 0.006*** (0.000) (0.000) (0.001) Over-qualification Dummy * Years of Tenure 0.006*** 0.004*** 0.006*** (0.001) (0.001) (0.002) Age 0.018** 0.025*** (0.006) (0.006) (0.012) Adjusted_R Observations Robust standard errors in parenthesis; * Significant at 10%, ** Significant at 5%, *** Significant at 1% Other explanatory variables: age; a dummy for whether married; a dummy for being employed parttime and a dummy for having only a marginal employment; and three dummies for firm size: less than 20; 20 or more but less than 200; and 200 or more but less than 2000 employees. 16

17 Table 6: Changes across statuses Status t-1 Status t Not Employed Matched Over-qualified Total Not Employed Matched Over-qualified Total Table 7: Probability of exiting the status of over-qualification (1) (2) Changed Job (Same Employer) *** (0.134) Changed Job (Same Employer) Improvement 0.708*** (0.170) Changed Employer 0.288** 0.238** (0.088) (0.085) Years of Education *** *** (0.018) (0.018) Years of Tenure in the Job (0.007) (0.007) Dummy for Part-time *** *** (0.101) (0.101) Dummy for Marginal or Irregular Employment *** *** (0.106) (0.106) Dummy for Female (0.087) (0.087) Dummy for Married (0.087) (0.086) Age 0.024*** 0.026*** (0.005) (0.005) Firm with Less than 20 Employees 0.277** 0.335** (0.107) (0.107) Firm with More than 20 Less than 200 Employees 0.212* 0.262* (0.107) (0.107) Firm with More than 200 but Less than 2000 Employees (0.108) (0.108) Constant 3.263*** 3.215*** (0.277) (0.277) Observations Robust standard errors in parenthesis; * Significant at 10%, ** Significant at 5%, *** Significant at 1% 17

18 Table 8: Probability of exiting the status of employee (1) Over-qualification Dummy (t-1) 0.477*** (0.072) Years of Tenure in the Job (t-1) *** (0.003) Dummy for Part-time (t-1) ** (0.055) Dummy for Marginal or Irregular Employment (t-1) 0.225*** (0.060) Firm with Less than 20 Employees (t-1) 0.300*** (0.056) Firm with More than 20 Less than 200 Employees (t-1) (0.055) Firm with More than 200 but Less than 2000 Employees (t-1) (0.059) Monthly Wages / 1000 (t-1) *** (0.000) Dummy for Married (t-1) (0.044) Age * (0.002) Dummy for Female 0.283*** (0.048) Constant *** (0.103) Observations Robust standard errors in parenthesis; * Significant at 10%, ** Significant at 5%, *** Significant at 1% 18

19 Figure 1: Average excess years of education per wave amongst the overqualified (ISCO above, Goldthorpe below) dfedw Wave of the data (1-21) dfedg Wave of the data (1-21) 19

20 On-the-job Training Vocational Degree No of people College Degree Years of over-qualification Graphs by Highest completed schooling Figure 2: Number of persons over-qualified for a certain number of (not necessarily consecutive) years On-the-job Training Vocational Degree No of spells College Degree Consecutive years of over-qualification Graphs by Highest completed schooling Figure 3: Number of persons over-qualified for a certain number of consecutive years. 20