Over-education and over-skilling in the labour market: theory and empirics

Transcription

1 Institut d Études Politiques de Paris École Doctorale Economics and Public Policy Over-education and over-skilling in the labour market: theory and empirics By: Joanne Tan Supervisor: Jean-Marc Robin Denis Fougère 20th May 2014

2 Contents 1 Introduction 2 2 Literature Review 3 3 The empirical incidence and impact of over-education and overskilling The data Who over-educates or over-skills? The effects of over-education and overskilling on wages The Model Setup The matching process The surplus sharing rule The Bellman equations Equilibrium Conditions for the steady state equilibrium Under what conditions would less competent graduates be mismatched? The returns to over-education in the equilibrium Caveats of the model and possible extensions Comparative Statics 41 7 Conclusion 43 8 Bibliography 44 9 Appendix 46 1

3 Over-education and over-skilling in the labour market: Theory and empirics Joanne Tan May 20, 2014 Abstract This paper covers both the empirics and theory of over-education and over-skilling. Using data on two generations of school leavers in France, I find that, among other factors, an individual s chance of being overeducated or over-skilled depends on his/her type of university qualification, whether he/she was over-educated or overskilled in the past and if so, whether he/she managed to change jobs. Given the same level of education, an over-educated/over-skilled individual earns less than his/her well-matched counterpart while, given the same job, an over-educated individual earns more than his/her colleague. Based on the empirical findings, I then construct a search and matching model that is adapted from the models of Dolado et al. (2009) and Moscarini (2001). 1 Introduction Between 1960 and 2012, the number of students in higher education in France 1 increased from around 309,700 to 2,386,900. In addition, the proportion of baccalauréat holders, a prerequisite for entry into higher education, in each cohort increased from 20.1 percent in 1970 to 65.3 percent in The expansion of access to higher education is not unique to France alone. For instance, amongst OECD countries 2, the average proportion of graduates in each cohort has risen from 20 percent in 1995 to 39 percent in The strand of literature on overeducation stems from the concern that the supply of graduates has outstripped the demand for graduate labour. This paper addresses the following question - What are the determinants and impacts of over-education and over-skilling in the French labour market and how can a theoretical model be constructed to explain these findings? To this aim, a binary logit regression is run to examine the determinants of over-education and over-skilling, followed by two modified Mincerian wage regressions to assess the impact of over-education and overskilling on individual wages. The key results are the following: I would like to thank Jean-Marc Robin, Denis Fougère and Maxime Tô for their invaluable advice. Any fault is my own. 1 Statistics from the Ministry for Higher Education and Research. 2 Figures from the OECD 2

4 University qualifications are not made equal and most types of degrees increase the likelihood of being over-educated/overskilled relative to not having a degree. In particular, engineering graduates and doctorate holders are significantly less likely than non-graduates to be over-educated/overskilled. The more an over-educated/overskilled person changes jobs, the less likely he/she is to be over-educated/overskilled at the next wave of interview. However, being previously over-educated/overskilled still significantly predisposes a person to being over-educated/overskilled in the future. Mismatched over-educated workers should face a wage penalty relative to their well-matched counterparts with the same education level but should earn slightly more than their well-matched less-educated work colleagues. While the above empirical exercise is rather standard in the literature, fewer attempts have been made to bring theory to the data. Such an attempt is made here. The theoretical model in this paper is adapted from two disparate search and matching models, the first from Dolado et al.(2009) and the second from Moscarini(2001), in a way that takes into account the key empirical findings. A brief simulation exercise shows that increasing the share of graduates in the population increases graduate unemployment rate and the number of mismatched workers, due to an insufficient increase in graduate job vacancies. The organization of the paper is as follows: Section 2 provides a review of the literature on over-education, Section 3 contains the empirical findings, Section 4 gives the setup of the theoretical model and the Bellman equations, Section 5 discusses the features of steady-state equilibrium, Section 6 shows some comparative statics and Section 7 concludes. 2 Literature Review The definition of over-education, since the seminal work by Freeman (1976) has been subject to debate. Conventionally there have been three ways to measure over-education, each with its own drawbacks: 1. The Objective/ Job Analysis measure - An international standard, such as the International Standard Classification of Occupations (ISCO) or the United States Dictionary of Occupation Titles (DOT), is used to ascertain the required level of formal education in a job. An individual is over-educated if he/she has a level of formal education that exceeds that required by his/her job. Drawback: This measure ignores the heterogeneity in difficulty between jobs that have the same occupational title. 2. The Subjective/ Worker Analysis measure - Individuals are asked directly in surveys to report the required level of education needed in their job. An individual is over-educated if his/her level of education exceeds his/her estimated required level of education for his/her job. Drawback: There may be misreporting by individuals due to inaccurate/outdated information. 3

5 3. The Standard Deviation/ Realized Match measure - The mean or mode of the education levels of people working at a each job type calculated. A person is over-educated if he/she has a level of education that is geq 1 standard deviation above the mean. Drawback: This is a highly arbitrary measure. The choice of the mean or mode as the threshold beyond which a person is considered over-educated is rather unconvincing. As highlighted by Battu et al. (2002) and Hartog (2002), the incidence of overeducation often varies according to the measure adopted. Arguably, an individual s skills are not solely acquired in formal education. Futhermore, the expansion of higher education may have raised the heterogeneity of skills amongst graduates with the same paper qualifications. For instance, Chevalier (2003) argues that the greater access to higher education in the UK has raised the heterogeneity of the skills of fresh graduates. Moreover, the quality of the university, the field of study, the work experience and social skills acquired alongside university education contribute significantly to the skilling of an individual. These aspects are not taken into account if individuals with the same paper qualifications are viewed as equally skilled. In addition, employers may assign more difficult tasks to employees while keeping them in the same occupation level if employees are formally over-educated (Battu et Al 2000). Simply put, not all formally over-educated individuals are under-utilized in their jobs. This is why the recent literature now distinguishes between overqualification and over-skilling, with the former referring to the possession of paper qualifications that are in excess of what the job requires, and the latter referring to the possession of skills that are in excess of what the job requires. In Quintini (2011), a worker is deemed overqualified if his/her qualifications, as measured by the 1997 International Standards Classification of Education (ISCED), exceeds the qualifications required by their occupation, defined by the 1988 International Standard Classification of Occupation (ISCO). On the other hand, a worker is over-skilled if he/she chooses the response I have the skills to cope with more demanding duties to the question Which of the following alternatives best describe your skills in your own work? The other responses are My duties correspond well with my present skills (well-matched) and I need further training to cope well with my duties (under-skilling).the report found that on average across OECD countries, 25.3 percent of workers were over-qualified, with Australia, Mexico, the Netherlands and Turkey having the highest rates of over-qualification. With regards to over-skilling, the report only had information on the EU 19 countries, Estonia, Norway, Slovenia and Switzerland. For these countries, it was found that 33.5 percent of all workers were over-skilled on average, with France, Ireland, Great Britain and Sweden having the highest incidence of self-reported over-skilling. Interestingly, within this group of countries, only 36 percent of all over-qualified workers reported being over-skilled. Using data from the UK, Green and Zhu (2010) coin the terms formal and real over-qualification to distinguish between the excess of paper qualifications and the excess of actual skills. Respondents were asked the following two questions: 4

6 1. If they were applying today, what qualifications, if any, would someone need to get the type of job you have now? 2. How much of your past experience, skill and abilities can you make use of in your present job? Respondents were classified as only formally over-qualified if their own qualifications exceeded their job s required qualifications, but replied quite a lot or almost all to the second question. On the other hand, they were classified as really over-qualified if their own qualifications exceeded their jobs required qualifications and if they replied very little or a little to the second question. Green and Zhu (2010) find that for men, over-qualification rose from 21.7 percent in 1992 to 33.2 percent in 2006, while for women, over-qualification increased from 23.8 to 32.1 percent. Formal over-qualification contributed to most of this overall rise in over-qualification while real over-qualification only made a small and insignificant contribution. Moreover, recent cohorts of graduates were more over-qualified than previous cohorts. Likewise, Chevalier (2003) specifies two categories of over-educated people: 1) the apparently overeducated and 2) the genuinely over-educated. He divides the pool of university graduates in the UK into clever and underachiever. The pool of jobs available to these graduates is divided into graduate, non-graduate job with intermediate skill level (called an upgraded job) and non-graduate job with low skill level. He assumes that clever graduates only enter graduate or upgraded jobs while underachieving graduates can only enter upgraded or non-graduate jobs. Clever graduates who are in upgraded jobs and under-achieving graduates in non-graduate low skill jobs are considered genuinely over-educated, whereas under-achieving graduates in upgraded jobs are only apparently over-educated. With regards to assessing the impact of over-education and/or overskilling, much focus has been placed on the effect on individual wages. The existing literature suggests that given the same job, the over-educated earn more than their less-educated but well-matched counterparts. However, given the same qualification level, the over-educated earn less than their well-matched counterparts and thus face a wage penalty. Evidence for the former is provided by Hartog (2000) who finds that the wage returns to over-education are positive but smaller (half to two-thirds) than that of required education. On the latter, Battu et Al (2000) find a significant wage penalty for the over-educated relative to well-matched graduates. As previously mentioned, recent works distinguish between surplus formal education and surplus skills. These papers have also measured the effects of surplus formal education and surplus skills on individual wages separately. It seems that the formally over-educated whose skills are well-utilized face a smaller penalty (relative to the well-matched with the same education level) than the over-educated whose skills are under-utilized. Green and Zhu (2010) for instance show that, compared to their equally-educated well-matched counterparts, the formally over-qualified face a statistically significant wage penalty. However, 5

7 the wage penalty is much greater for men and women who are really overqualified. Similarly, Chevalier (2003) finds that the apparently over-educated face a wage penalty of 5-11 percent (depending on the controls included) relative to well-matched graduates, while the genuinely over-educated face a wage penalty of percent. Yet, although there seems to be some consensus on the effect on individual wages, Leuven and Oosterbeek (2011) cast doubt on the validity of these findings, by arguing that, because over-education is subject to measurement error and is also likely to be endogenous, the reported effect of over-education of individual wages is probably biased. Another key issue that has been considered is whether individuals move out of over-education eventually. There are two reasons why over-education may just be a transient state for individuals: 1) over-educated individuals may be more likely to leave their current jobs, especially if they find better matches and 2) employers may increase the difficulty of tasks they assign to their overeducated workers. The hypothesis that employers adapt tasks to better utilize graduate skills is tested by Battu et Al (2000). The authors argue that if this is the case, then one reasonably expects that over time, the characteristics of jobs for overeducated graduates converge to those for the well-matched graduates. The characteristics considered by the authors are earnings, job satisfaction, managerial duties and promotion prospects across time and cohorts. However, their results show that there is no convergence in any of these. In fact, there is evidence of divergence; an over-educated graduate becomes increasingly worse off relative to a well-matched graduate over time, and the relative disadvantage of over-educated graduates increases from the 1985 to the 1990 cohort. The persistence of an individual s state of over-education is also discussed in Piracha et Al (2012). Looking at data on immigrants labour market experience in Australia, the authors find that those who were over-educated in the last job held in the home country are 45 percent more likely to be over-educated at five months after immigration to Australia. They argue that an individual who was over-educated in his/her home country sends a signal to prospective employer in Australia that he is of lower productivity that his well-matched counterpart with the same education level. The reason for this signal is twofold: Firstly, a graduate who was over-educated at his/her previous job may have acquired fewer on-the-job skills (or may have even been deskilled) compared to the graduate who was well-matched. Secondly, Australian employers may presume that a person s prior over-education status was due to his/her low ability. The over-educated at home hence face the same situation in their new country. Other studies that confirm the persistence of over-education for over-educated individuals include McGuinness (2003), who shows that the greater job turnover rate amongst the over-educated does not entail an eventual move out of their state of over-education, and Dolton and Silles (2008). The question of why over-education exists is one that is often asked in the literature. After all, according to conventional Human Capital Theory (HCT), 6

8 over-education should not exist, since employers always fully utilize human capital in their production. Several reasons have been offered, a few of which are cited here: 1. Demand-side factors: Business cycle troughs could lower the demand for educated labour and lead to a rise in the incidence of over-education. Using probit regression, Quintini(2011) finds that an individual is three percent more likely over-educated at his/her first job if the unemployment rate is double its 5 year average. This estimate is likely to be biased downward, since students are likely to decide to remain in education if labour market conditions are poor. Demand-side factors are also likely to explain why individuals educated in certain fields are significantly less likely to be over-educated. For example, Chevalier (2003) finds that graduates of Medicine, Engineering, Mathematics and Education in the UK are significantly less likely to be over-educated (both apparently and genuinely) than an Economics graduate, while Humanities graduates are more likely to be over-educated. 2. Education as a signaling device: According to Thurrow (1975) s Job Competition Model, an individual s relative position in the job candidate pool is crucial to his/her success in securing a job. Hence, an individual is compelled to pursue higher education if he/she expects fellow competitors to be highly-educated as well. Not unlike Spence (1973), higher education is a positive signal to the employer that places an individual ahead in the job queue. As such, it may be rational for an individual to pursue more education even if this extra education will not be of use in his/her job. In a similar vein, Lang and Manove (2011) show that because employers are less able to gauge the productivity of Blacks, African Americans attain a higher level of education than Whites with similar ability to serve as a signal. 3. Education as compensation for lack of ability or job experience: Using the Panel Study of Income Dynamics (PSID) data, Sicherman (1991) finds that over-educated workers are less likely to have job-experience than their co-workers who have just the required education level. Likewise, Chevalier (2003) uses the difference between expected and observed earnings as a proxy for unobserved individual ability and introduces it as one of the regressors in a probit regression with the over-education dummy as the dependent variable. He finds that having higher unobserved skills significantly reduces one s likelihood of being over-educated. 4. Search frictions: Albretch and Vroman (2002) come up with a search and matching model to explain how over-education may occur. In their model, two kinds of workers and jobs exist - high-skill and low-skill. Two kinds of equilibria can emerge - i) The equilibrium with ex-post segmentation, where high-skill workers only match with high-skill jobs and low-skill workers match with low-skill jobs, and ii) the equilibrium with cross-skill matching, where high-skill workers agree to match with low-skill jobs. Since on-the-job (OTJ) search is ruled out in their model, a high-skill worker once mismatched remains so for life. Dolado et al (2009) add OTJ search to the model of Albretch and Vroman (2002) so that mismatched high-skill workers are able to move on to high-skill jobs. 7

9 This paper attempts to add to the literature in two ways. Firstly, it conducts the standard empirical exercise to measure the incidence and determinants of over-education and over-skilling, as well as their impact on individual wages, using French data. Secondly, based on the empirical findings, an adaptation of the Dolado et al.(2002) model is made. 3 The empirical incidence and impact of overeducation and overskilling 3.1 The data The dataset comes from the French Enquête Génération, a survey of several cohorts of school leavers in France. To date, the survey has been conducted for the 1992, 1998, 2001, 2004 and 2007 cohorts of first-time school leavers. While survey questions vary somewhat between each cohort, they all cover the following domains: basic individual characteristics such as age and gender, family background, educational background, past and current job characteristics. Notably, the question on whether an individual is formally-educated in his/her current job is only asked from the 2004 cohort onward. Prior to this, individuals were only asked if they felt their skills were adequately used in their current job. Because of this difference, this paper explicitly distinguishes between overeducation, where an individual possesses formal educational qualifications above what he/she thinks is required for his/her job, and over-skilling, where an individual feels that his/her skills are under-utilized in his/her job. This paper only uses the survey data for the 1992 and 2004 cohorts firstly because they both follow the individual for at least up to five years after he/she has left school and secondly because there is more than a decade between the two cohorts, which allows for the study of the change in the incidence and impact of over-education and/or overskilling over time. The 1992 cohort The survey of the 1992 cohort consists of a single wave in 1997, 5 years after the cohort had left school. 26,359 school leavers were surveyed, representing 80 percent of the total number of school leavers 3. Overskilling is measured via the following question: At this job, would you say that your competencies were 1) adequately used, 2) under-used or 3) over-used? Individuals were considered over-skilled if they answered 2 and were not considered to be so otherwise. Also, they were deemed to be underskilled if they answered 3 and not so otherwise. I specifically create a dummy first overskilled equaling 1 if the individual was over-skilled in his/her first job after leaving school and another dummy overskilled equaling 1 if the person was over-skilled in his/her job at the time of interview in In addition, for later use, I create a dummy underskilled equaling 1 if the person was underskilled in his/her job in By default, a person without a job then would have a missing value for the dummy overskilled (and underskilled). 3 Weights are provided for each individual in the sample to ensure that the sample is representative of the population. 8

10 Table 1: Transitions in and out of overskilling for 1992 cohort 1992 cohort Not overskilled in 1997 Overskilled in 1997 Total Not overskilled in ,112 1,082 9, % 7.23% 61.47% Overskilled in ,589 3,175 5, % 21.23% 38.53% Total 10,701 4,257 14, % 28.46% 100% Looking at Table One, 14,958 people had a non-missing value for both first overskilled and overskilled percent were overskilled at their current job in percent of responents had been overskilled in their first job in 1992 but were no longer so in 1997 while 7.23 percent of respondents had not been overskilled in 1992 but became overskilled in percent of respondents were not overskilled in both years. It is perhaps also interesting to break down the numbers of overskilled by education level and specialization. From Table Two, it is notable that the rates of over-skilling are not necessarily higher amongst those with a higher level of education. For instance, only 53.4 percent of those without a degree were never overskilled while 21.2 percent of them were overskilled in both 1992 and The incidence of overskilling seems to be lowest amongst those with a Bac+3 in the sciences 4, with 73.6 percent of Bac+3 sciences graduates never being overskilled and only 14.5 percent of them overskilled in both years. Conversely, graduates from École de commerce (business school) seem to face the highest incidence of overskilling, with only 46.1 percent of them never being overskilled and 24.1 percent of them being overskilled in both years. However, it is important to keep in mind that this may be due to the small numbers of business school graduates. Moreover, it is premature to draw any concrete conclusions from this purely descriptive exercise. The 2004 cohort The 2004 cohort of school leavers were first surveyed in 2007 and then in In total, 33,655 people were surveyed in 2007 and out of them, 20,084 were re-surveyed in Just as for the 1992 cohort, sample weights were assigned to each individual for population representation. The 2004 cohort was the first to be asked the following question, To perform a job like yours properly, what level of educational qualification do you think is necessary? 1) None, 2) CAP or BEP, 3) BAC, 4) BAC+2, 5) BAC+3 or BAC+4, 6) BAC+5, 7) Your level (of education). An individual was considered over-educated if he/she cited a level of required education that was below that which he/she had attained. Otherwise, he/she was not considered to be so. More specifically, two dummy variables have been created: over educ07, which equals one if the individual had been over-educated in his/her job in 2007, and over educ, which equals one if the individual was over-educated in his/her job in Also, for later use in the wage regression, dummies under educ07 and under educ were created, equaling one if the person cited a level of required education that was 4 Here, the sciences includes those in the fields of maths, science and technology. 5 Some individuals were no longer contactable in 2009 and hence could not be surveyed 9

11 Table 2: Breakdown of overskilling transitions by education level and specialization for 1992 cohort Education 1992-Not Not Total (by level and overskilled Overskilled overskilled Overskilled row) speciality 1997-Not overskilled Not overskilled Overskilled Overskilled No degree 6,300 2, ,493 11,788 bac+3 arts bac+3 sciences bac+4 arts bac+4 sciences bac+5 arts bac+5 sciences 53.44% 17.79% 7.62% 21.15% 100% % 10.46% 5.02% 18.2% 100% % 7.55% 4.4% 14.47% 100% % 17.58% 6.57% 26.69% 100% % 16.67% 5.56% 27.22% 100% % 19.06% 6.61% 21.73% 100% % 12.12% 5.56% 21.21% 100% Ecole de commerce Incomplete 66.67% 33.3% 0% 0% 100% Ecole de commerce 46.07% % 7.33% 24.08% 100% Ecole d ingénieur % 14.88% 4.76% 19.05% 100% Total 8,112 2,589 1,082 3,175 14, % 17.31% 7.23% 21.23% 100% 10

12 Table 3: Transitions in and out of overskilling 2004 cohort Not overskilled in 2007 Overskilled in 2007 Total Not overskilled in ,049 1,991 10, % 15,03% 75.81% Overskilled in ,350 1,854 3, % 14% 24.19% Total 9,399 3,845 13, % 29.03% 100% Table 4: Transitions in and out of over-education 2004 cohort Not over-educated in 2007 Over-educated in 2007 Total Not over-educated in ,152 1,429 9, % % 72.70% Over-educated in ,027 2,571 3, % 19.51% 27.30% Total 9,179 4,000 13, % 30.35% 100% above that which he/she had attained and zero otherwise. Thus, individuals with both under educ and over educ equaling 0 are considered well-matched. In addition, individuals were also asked the skills question, which is coded in the same way as for the 1992 cohort. Unlike the 1992 cohort survey, individuals were only asked these questions for their current jobs held at each wave of interviews, which is why the first record of them being over-educated/over-skilled (or not) occurs in 2007, three years after graduation instead of immediately after graduation. A summary glance at the numbers of overskilled in Table Three gives the following: Out of 13,244 non-missing responses to the over-skilling question, 24.2 percent were over-skilled in percent had been overskilled in 2007 but were no longer so in 2009 while 10.2 percent had not been overskilled in 2007 but became so in percent of respondents were overskilled both in 2007 and 2009 while 60.8 percent were not overskilled in both years. Next, looking at the numbers of over-educated in Table Four, 27.3 percent of the 13,179 respondents were over-educated in percent had been over-educated in 2007 but were no longer so in 2009 while 7.8 percent had not been over-educated in 2007 but became so in percent of respondents were over-educated in both 2007 and 2009, while 61.9 percent were not overeducated in both years. In accordance with what has been noted in the literature, over-skilling and over-education do not seem to coincide much. As shown in Table Five, 53.8 percent of those who are over-educated are not overskilled, while 16.2 percent of those who are overskilled are not over-educated, which highlights the importance of distinguishing between over-skilling and over-education. In addition, comparing the 2004 cohort to the 1992 cohort, the incidence of overskilling five years after graduation has not changed much. Comparing Tables One and Three, 28.5 percent of the 1992 cohort were overskilled 5 years after 11

13 Table 5: Number of over-educated and/or overskilled from the 2004 cohort when interviewed in cohort Not overskilled Overskilled Total Not over-educated 9,130 1,764 10, % 16.19% 100% Over-educated 2,231 1,916 4, % 46.2% 100% Total 11,361 3,680 15, % 24.47% 100% graduation, compared to 24.2 percent for the 2004 cohort 5 years after graduation. Unfortunately, for aforementioned reasons, the incidence of over-education cannot be compared between the cohorts. Just as in the case of the 1992 cohort, a further point of interest would be the incidence of over-skilling and over-education by education level and specialization. This is presented in Tables Six and Seven. One should note here that the categorization of educational level and specialization for the 2004 cohort differs slightly from that of the 1992 cohort, due to the changes made in the survey response choices. From Table Six, it is clear that, just as for the 1992 cohort, the incidence of overskilling for the 2004 cohort is not necessarily greater among the higher educated. In fact, the incidence of overskilling is the lowest among doctorate holders, with 74.1 percent of them being overskilled neither in 2007 nor in 2009 and only 9.84 percent being overskilled in both 2007 and In comparison, 63.2 percent of those without a university degree were overskilled neither in 2007 nor in Those who graduated with a Masters 1 seem to have the highest incidence of overskilling, with only 44 percent of them being overskilled neither in 2007 nor 2009, and 25.7 percent of them being overskilled in both years. Turning the attention towards the breakdown of the incidence of overeducation in Table Seven, it seems that, just as in the case of overskilling, the incidence of over-education does not necessarily rise with the level of education. In fact, doctorate holders seem to face the lowest incidence of over-education, with 87.6 percent of them being over-educated neither in 2007 nor in 2009, and ony 6.22 percent of them being over-educated in both years. At the other extreme, those who graduated with a Licence Pro (vocational degree) appear to have the highest incidence of over-education, with only 26.9 percent of them being over-educated neither in 2007 nor 2009, and 41.8 percent of them being over-educated in both years. Once again, it is crucial to remember that these summmary statistics are but for descriptive purposes, and it is premature to comment on the causal link between one s level and type of education and one s likelihood of being over-educated/over-skilled. In the next subsection, a binary logit regression is run to assess the determinants of over-education/over-skilling. 12

14 Table 6: Breakdown of overskilling transitions by education level and specialization for 2004 cohort Education 2007-Not Not Total (by level and overskilled Overskilled overskilled Overskilled row) speciality 2009-Not overskilled Not overskilled Overskilled Overskilled No degree 5,896 1, ,142 9,330 License Pro Bac+3 arts Bac+3 sciences 63.19% 14.69% 9.87% 12.24% 100% % 18.62% 10.9% 16.49% 100% % 12.92% 8.54% 21.67% 100% % 14.67% 10.22% 13.78% 100% Masters % 17.41% 12.8% 25.73% 100% Bac+5 arts Bac+5 sciences Ecole de commerce Ecole d ingénieur % 15.69% 12.63% 20.21% 100% % 17.82% 10.88% 14.81% 100% % 14.38% 8.9% 19.18% 100% % 15.94% 10.87% 10.33% 100% Doctorat % 9.84% 6.22% 9.84% 100% Total 8,049 1,991 1,350 1,854 13, % 15.03% 10.19% 14% 100% 13

15 Table 7: Breakdown of over-education transitions by education level and specialization for 2004 cohort Education Not Total (by level and Not overeducated Overeducated overeducated Overeducated row) speciality Not overeducated Not overeducated Overeducated Overeducated No degree 6, ,469 9,267 License Pro Bac+3 arts Bac+3 sciences 67.21% 9.97% 6.97% 15.85% 100% % 18.62% 12.77% 41.76% 100% % 13.99% 8.77% 32.78% 100% % 8.44% 11.56% 26.67% 100% Masters % 12.14% 8.18% 31.27% 100% Bac+5 arts Bac+5 sciences Ecole de commerce Ecole d ingénieur % 14.38% 9.85% 35.82% 100% % 12.27% 8.56% 26.62% 100% % 14.38% 13.7% 21.23% 100% % 12.5% 11.96% 11.59% 100% Doctorat % 3.11% 3.11% 6.22% 100% Total 8,152 1,429 1,027 2,571 13, % 10.84% 7.79% 19.51% 100% 14

16 3.2 Who over-educates or over-skills? For over-education (OE) or over-skilling (OS) the regression model used is the following: { OE /OS 1 if OE /OS 0, = XB + ɛ OE/OS = 0 if OE /OS < 0 Assuming ɛ is logistically distributed gives us a binary logit model, which is OE/OS log( 1 OE/OS ) = XB + ɛ Following authors such as Battu and Sloane (1999) and Chevalier (2003), the suspected determinants of over-education included in X are gender, if the person has children, educational attainment and specialization, firm size, job experience, the number of unemployment spells, the rate of unemployment at the time of hire and job sector controls. The coefficients on the job sector controls are not presented in the regression results tables. When over educ is the explained variable, over educ07 is added as a regressor to see if being previously over-educated significantly increases the probability of being currently overeducated. The same is done for the case of overskilling. Determinants of over-education for the 2004 cohort As seen from Table Eight, having a Licence Pro, a Bac+3 in the Arts or Sciences, a Bac+4, a Bac+5 in the Arts or Sciences or a Business school degree (instead of not having a university degree at all) significantly increases the log odds of being over-educated three years after graduation. For instance, having a Bac+3 in the Arts (instead of not having a degree) increases the log odds of over educ07 by In fact, only an individual with a degree from Engineering school or with a doctorate is significantly less likely to be over-educated three years after graduation than an individual without a degree. For example, having a doctorate (instead of not having a degree) lowers the log odds of over educ07 by 2.7. Other regressors that significantly increase the log odds of being over-educated three years after graduation include being female (instead of male), being older, the rate of unemployment at the time of hire (taux chom07 ) and the total number of months unemployment accumulated by the individual since graduation (months unemployed07 ). Conversely, other regressors that significantly lower the log odds of being over-educated three years after graduation include working full-time (instead of part-time). Table 8: Determinants of over-education three years after graduation (the 2004 cohort) Dep = over educ07 female Variable Coefficient (Std. Err.) (0.066) couple

17 (0.065) have kid (0.117) age07 license pro bac p3 arts bac p3 sciences bac p4 bac p5 arts bac p5 sciences ecole commerce ecole ingenieur doctorat (0.014) (0.147) (0.189) (0.249) (0.123) (0.126) (0.147) (0.245) (0.150) (0.437) exper (0.022) exper2 07 months unemployed07 taux chom07 full time (0.000) (0.008) (0.183) (0.086) small firm (0.073) large firm (0.089) Intercept (1.498) N

18 Log-likelihood χ 2 (87) Five years after graduation, however, several of the education level and specialization dummies are no longer significant in determining if an individual is over-educated. As seen in Table Nine, while having a Licence Pro, a Bac+3 in the Arts or Sciences, a Bac+4 or a Bac+5 in the Arts or Sciences (instead of having no university degree) still significantly increases the log odds of being over-educated, having a doctorate or a Engineering School degree has no significant impact on the log odds of being over-educated five years after graduation. Table 9: Determinants of over-education five years after graduation (2004 cohort) Dep = over educ Variable over educ07 Coefficient (Std. Err.) (0.083) job changes07to (0.057) overed07xjobchanges (0.079) female (0.081) couple (0.076) have kid age09 license pro bac p3 arts bac p3 sciences bac p4 bac p5 arts (0.096) (0.016) (0.183) (0.195) (0.243) (0.135) (0.147) 17

19 bac p5 sciences (0.168) ecole commerce (0.332) ecole ingenieur (0.195) doctorat (0.391) exper09 exper2 09 months unemployed09 taux chomage full time (0.035) (0.000) (0.008) (0.061) (0.113) small firm (0.093) large firm (0.104) Intercept (1.120) N 9844 Log-likelihood χ 2 (89) Also of interest are the coefficients on over educ07, jobchanges07to09 and overed07xjobchanges, which is the interaction term. Notably, having been overeducated in 2007 increases the log odds of being over-educated in 2009 by 2.54 if the person did not change jobs at all. This result echoes Dolton and Silles (2008), who find that being over-educated in one s first job significantly increases the probability of being over-educated in one s current job. On the other hand, as the coefficent on overed07xjobchanges is significantly negative, each job change made by a person who was over-educated in 2007 reduces the impact of being previously over-educated on the log odds of being over-educated in 2009 by Lastly, one should remark job experience and it s quadratic (exper 09 and exper2 09 ) do not help to lower the log odds of being over-educated at all. A plausible explanation for this could be that it is impossible to distinguish between relevant and irrelevant job experience. As such, the results suggest that 18

20 once over-educated individuals can move out of their situation by making job changes, even if being formerly over-educated still has a strong positive impact on being over-educated later on. The determinants of overskilling for the 2004 cohort Having discussed the determinants of over-education, this paper now moves on to the determinants of over-skilling. As seen from Table Ten, the determinants of overskilling three years after graduation differ somewhat from those of over-education. While having a Bac+3 in the Arts and a Bac+4 (relative to not having a university degree) significantly increases the log odds of being overskilled three years after graduation, having a Bac+5 in the Sciences, an Engineering school degree or a doctorate significantly reduces it. Other regressors that significantly increase the log odds of being overskilled include age. Conversely, regressors that significantly decrease the log odds include working for a small firm 6 (rather than a medium-sized firm), working full-time (rather than part-time) and being female (rather than male). Interestingly, being female has an opposite impact on being overskilled compared to being over-educated three years after graduation. 7 Table 10: Determinants of overskilling three years after graduation (2004 cohort) Dep = overskilled07 female Variable Coefficient (Std. Err.) (0.067) couple (0.062) have kid (0.127) age (0.014) license pro (0.147) bac p3 arts (0.168) bac p3 sciences (0.251) bac p (0.122) 6 One can imagine that smaller firms may offer each worker opportunities to work on different tasks instead of focusing on just one task, thus giving the worker the impression that his/her skills are well-used. However, the question of why workers who work in large firms are not then more likely to be overskilled is raised. 7 One explanation for this could be that women may tend to underestimate their own skills compared to men, but,barring any evidence, the reader is invited to think of other possibilities. 19

21 bac p5 arts (0.126) bac p5 sciences (0.150) ecole commerce (0.249) ecole ingenieur doctorat (0.146) (0.339) exper (0.022) exper (0.000) months unemployed (0.008) taux chom (0.176) full time07 small firm (0.089) (0.074) large firm (0.085) Intercept (1.443) N Log-likelihood χ 2 (85) Table 11: Determinants of overskilling five years after graduation Dep = overskilled Variable overskilled07 job changes07to09 oversk07xjobchanges Coefficient (Std. Err.) (0.079) (0.051)

22 (0.074) female (0.073) couple (0.072) have kid age (0.096) (0.017) license pro (0.186) bac p3 arts (0.173) bac p3 sciences (0.251) bac p (0.130) bac p5 arts (0.138) bac p5 sciences (0.169) ecole commerce (0.260) ecole ingenieur doctorat exper09 exper (0.167) (0.322) (0.034) (0.000) months unemployed (0.008) taux chomage (0.058) full time (0.107) small firm (0.082) large firm (0.094) 21

23 Intercept (1.100) N 9850 Log-likelihood χ 2 (89) From Table Eleven, it can be observed that five years after graduation, most of the education level and type dummies are no longer significant in determining over-skilling, except for having a Bac+4 (rather than having no degree), which significantly increases the log odds of being overskilled five years after graduation, and having a doctorate or an engineering degree (rather than having no degree), which significantly decreases it. Apart from these, the rate of unemployment at the time of hire also significantly increases the log odds of being overskilled, as does having children and being older. Of particular interest are overskilled07, job changes07to09 and oversk07xjobchanges, which is the interaction of the first two. Given that the person did not change jobs at all between 2007 and 2009, having been overskilled in 2007 raises the log odds of being overskilled in 2009 by However, given that the person was overskilled in 2007, each job change significantly reduces the impact of overskilled07 by Thus, just as in the case of over-education, while those who have formerly been over-skilled are more likely to remain in that situation, changing jobs lowers the odds that they do. Determinants for overskilling for 1992 cohort To serve as a comparison, similar regressions are also run for the 1992 cohort. Table Twelve shows the determinants of overskilling right after graduation. As respondents were not asked if they had children or were cohabiting at the time right after graduation, these regressors could not be added. Also, work and unemployment experience could not be included since by definition respondents could not have accumulated any such experience right after graduation. 8 As shown in Table Twelve, many of the education dummies have a significant impact on being overskilled right after graduation. In fact, a majority of the types and levels of university degrees (rather than having no degree) significantly lower the log odds of being overskilled. Otherwise put, for the 1992 cohort, a person with no university degree is at greater risk of being overskilled than nearly the majority of university degree holders, ceteris paribus. This is in contrast to the case of the 2004 cohort, shown in Table Ten, where some degree types, such as having a Bac+4 or a Bac+3 in the Arts (instead of having no degree), significantly increase the log odds of being overskilled. 8 For the 2004 cohort, on the other hand, respondents were asked if they were cohabiting or had children both in 2007 and Also, since the 2004 cohort respondents were first asked if they were overskilled three years after graduation, work and unemployment experience could be included in the regression in Table Ten. 22

24 Table 12: Determinants of overskilling in first job after graduation (1992 cohort) Dep = first overskilled female age 92 Variable bac p3 arts bac p3 sciences Coefficient (Std. Err.) (0.041) (0.010) (0.118) (0.198) bac p4 sciences (0.160) bac p4 arts (0.107) bac5etplus arts bac5etplus sciences (0.095) (0.132) incomplete commerce (0.998) ecole commerce (0.149) ecole ingenieur fj taux chomage fj full time fj small firm fj large firm Intercept (0.113) (0.032) (0.043) (0.048) (0.057) (0.391) N Log-likelihood χ 2 (84)

25 Five years after graduation, education level and specialty no longer play such a significant role in determining overskilling. As shown in Table Thirteen, only having an Engineering school degree, or to a lesser extent a Bac+3 in the arts, significantly reduces the log odds of being overskilled. Just as for the 2004 cohort, being previously overskilled significantly increases the log odds of being overskilled later on. Given that the person made no job changes between 1992 and 1997, having been overskilled in 1992 increases the log odds of being overskilled in 1997 by Once again, given that a person was overskilled in 1992, each extra job change significantly reduces the impact of being previously overskilled on the log odds of being overskilled in 1997, by Overall, the determinants of overskilling for the 1992 cohort are not too different from those for the 2004 cohort. Table 13: Determinants of overskilling five years after graduation (1992 cohort) Dep = overskilled Variable first overskilled job changes92to97 oversk92xjobchanges Coefficient (Std. Err.) (0.123) (0.061) (0.058) female (0.061) couple (0.058) have kid (0.067) age 97 bac p3 arts (0.014) (0.155) bac p3 sciences (0.243) bac p4 sciences (0.216) bac p4 arts (0.147) bac5etplus arts (0.132) 24

26 bac5etplus sciences (0.164) o.incomplete commerce (0.000) ecole commerce (0.200) ecole ingenieur exper exper2 months unemployed97 taux chomage full time small firm (0.148) (0.111) (0.011) (0.027) (0.053) (0.076) (0.070) large firm (0.075) Intercept (0.664) N Log-likelihood χ 2 (91) The effects of over-education and overskilling on wages As mentioned in the literature review, previous work has shown that given the same job, the over-educated earn more than their less-educated but wellmatched counterparts. However, given the same qualification level, the overeducated earn less than their well-matched counterparts and thus face a wage penalty. This subsection examines if the Enquête Génération data corroborates these findings. Since only the 2004 cohort data includes information on overeducation and overskilling, attention will first be paid to the regression results for the this cohort. Also, for reasons related to self-selection that will be discussed later, wage regressions are run only for the men. Only at the end of this subsection will results for the 1992 cohort be presented, with the aim of examining the changes in wage returns to over-skilling between the two cohorts. 25

27 The dummy variable approach The first regression specification, previously adopted by the likes of Battu et Al (1999) and Dolton and Silles (2008), basically involves adding the over-educated (overskilled) dummy 9 to the conventional Mincerian wage equation. To distinguish the effect on wages of not being under-educated (underskilled) from that of being just well-matched, I add the under-educated (underskilled) dummy as well. The regression run is thus ln(w i ) = α OEi(OS i)oe(os) + α UEi(US i)ue(us) + γs i + X i β + ɛ i Where S i refers to the individual s highest attained level of schooling, where OE (or OS) refers to the over-educated (or overskilled) dummy and UE (or US) refers to the under-educated (or underskilled) dummy. In the context of this paper, S i is a series of education level dummies, with no qualification being the reference category. X i is a vector including 1 and controls such as work experience and its quadratic, whether the person is cohabiting, the number of months of unemployment experienced since graduation, firm size dummies, whether the person works full-time and job sector dummies. For brevity, the coefficients for the job sector dummies are not reported. In Table Fourteen, the regression results for the impact of over-education (column one) and overskilling (column two) in 2009 on the 2004 cohort men s wages are shown. As seen from column one, being over-educated rather than well-matched lowers hourly wages by 8.2 percent, significant at the one percent level. On the contrary, being under-educated instead of well-matched increases hourly wages by 7.8 percent. Next, refering to column two, being overskilled instead of well-matched lowers hourly wages by 6.5 percent, significant at the one percent level, while being underskilled instead of well-matched has no significant impact. On the whole, these results cohere with the literature in that, given the same education level, over-educated (overskilled) individuals face a wage penalty relative to their those who are well-matched. Also, given the same education level, under-educated individuals enjoy higher wages compared to those who are just well-matched. Instead of just introducing over/under-education and over/under-skilling dummies separately in the regression, a worthwhile tweak would be to incorporate both sets of dummies and to allow for their interaction. This modification facilitates the study of the following: 1. How being over-educated and overskilled may impact wages differently than merely being either over-educated or overskilled. 2. The difference in the wage penalty, ceteris paribus, between those who are over-educated and underskilled and those who are over-educated but well-matched in skills. A distinction is thus made between over-educated workers who may be using their surplus education to compensate for a lack of work-related competency and over-educated workers who do not do so. 9 Note that the parenthesis here means that either the over-educated dummy or the overskilled dummy is used in the wage regression. 26