Stuck in a mismatch? The persistence of overeducation during twenty years of the post-communist transition in Poland

Economics of Education Review 32 (2013) 78–91

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Stuck in a mismatch? The persistence of overeducation during twenty years of the post-communist transition in Poland Anna Kiersztyn a,b,* a

Institute of Sociology, Department of Philosophy and Sociology, University of Warsaw, Karowa 18, 00-927 Warsaw, Poland Research Group of Comparative Analysis of Social Inequalities, Institute of Philosophy and Sociology, Polish Academy of Sciences, Nowy S´wiat 72, 00-330 Warsaw, Poland b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 24 June 2011 Received in revised form 12 September 2012 Accepted 17 September 2012

This paper examines long-term changes in the persistence of overeducation among individual workers, focusing on the relationship between the rate of those changes and the general economic situation. All analyses are based on data from the Polish Panel Survey (POLPAN) conducted throughout the post-communist transition period, 1988–2008. The results suggest that being in a job with too low educational requirements is stable through time and raises the probability of experiencing the same situation five years later. The incidence of overeducation increased in the studied period, most rapidly during recession. The youngest cohorts, workers aged 26–35 in 2008, faced a higher risk of persistent overqualification than other cohorts. These findings are consistent with Thurow’s job competition theory, as well as Sattinger’s job assignment model. ß 2012 Elsevier Ltd. All rights reserved.

JEL classification: I21 J21 J24 J62 Keywords: Overeducation Career mobility Cohort effects

1. Introduction The last decades were a period of a fast educational attainment growth in many industrialized societies. The consequences of this change have become subjects of controversy among scholars and policymakers. On the one hand, investments in schooling are thought to foster economic development. On the other hand, the point has been made that as labor markets fail to absorb the rapidly growing number of high school and college graduates, a large share of the latter are pushed into jobs for which they are overeducated. In this context, an important and policy relevant research question is whether overeducation is temporary,

* Correspondence address: Institute of Sociology, Department of Philosophy and Sociology, University of Warsaw, Karowa 18, 00-927 Warsaw, Poland. Tel.: +48 22 552 0710; fax: +48 22 827 8599. E-mail address: [email protected]. 0272-7757/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.econedurev.2012.09.009

acting as a stepping stone to better employment prospects, or whether it is a dead-end for workers. If in the long run, individuals are able to move on to more demanding jobs, this initial human capital waste may not be a serious problem. However, large numbers of highly educated workers trapped in jobs with low educational requirements would imply that individuals and societies overinvest in schooling (Borghans & de Grip, 2000). In the latter case, the negative individual and social consequences of overeducation also become reasons for concern. Working below one’s skill level was shown to have a negative impact on productivity and job satisfaction (Belfield, 2010; Burris, 1983; Peiro, Agut, & Grau, 2010; Tsang, Rumberger, & Levin, 1991). Longitudinal studies of the psychological consequences of underemployment suggest that overqualification among recent high school graduates tends to increase the likelihood of depression even after controlling for earlier levels of psychological well-being (Dooley, Prause, & Haw-Rowbottom, 2000; Friedland & Price, 2003; O’Brien & Feather, 1990).

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This study examines long-term changes in the persistence of overeducation in Poland, the relationship between the rate of those changes and the general economic situation, and checks for cohort effects in the distribution of persistent overschooling throughout the twenty-year post-communist transition period, 1988–2008. It is organized as follows: Section 2 presents the theoretical context of the debate concerning the duration of occupational mismatches,1 analyzes the results of earlier research on this topic, and describes the hypotheses of the present study, Section 3 describes the data and methodology used to test the hypotheses, Section 4 presents the results, and Section 5 concludes. 2. Background and hypotheses 2.1. Theoretical considerations The labor market literature offers several competing theoretical explanations for the inequality of rewards received by different groups of workers. Each theory leads to opposing predictions concerning the duration of overeducation. According to human capital theory, workers’ wages depend solely on their productivity, which in turn is determined by their formal education, experience and onthe-job training (Becker, 1962; Schultz, 1961). Overeducation is difficult to explain by this model, since theoretically, in a properly functioning labor market, it should not occur. However, in some cases a sudden and dramatic increase in the supply of better educated workers can cause a fall in their relative wage, which in turn would lead employers to hire more qualified (thus more productive) workers into positions previously occupied by individuals with less education (Borghans & de Grip, 2000). Hence, overeducation appears when the labor market is in disequilibrium, and ends as workers respond to lower returns to education by reducing their investment in schooling, and firms adjust their production processes to accommodate higher numbers of skilled workers. Other theories which suggest that overeducation is a temporary phenomenon are the matching model and career mobility hypothesis. According to the matching (or job shopping) model, individuals have imperfect information concerning the rewards and performance requirements associated with different jobs. They can only learn about those properties through experimentation: by accepting consecutive employment offers and quitting whenever they find their experience unsatisfactory. Thus, as time passes, workers who started out in suboptimal employment, are usually able to achieve a better job match (Johnson, 1978; Jovanovic, 1979; Viscusi, 1980). The more

1 The concept of ‘occupational mismatch’ is sometimes used in a wider sense and may also denote situations when workers are underqualified for their jobs, or are in occupations outside their field of specialization. Since these problems are beyond the scope of my study, in this paper the term ‘mismatch’ refers only to overeducation, and the undereducated are included among those with an adequate match. (For recent research on the mismatch between field of education and occupation see: Nordin, Persson, & Rooth, 2010; Robst, 2007, 2008).

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recent career mobility hypothesis of Sicherman and Galor (1990) assumes that people voluntarily accept jobs for which they are overqualified in order to gain the experience and training necessary for career development. Overeducation is usually a short-term mismatch at the beginning of a working life. From a policy perspective, it is also worth noting that both the matching and career mobility theories see skill mismatches as unproblematic: they are considered a normal feature of a well-functioning labor market. Contrary to these views, there are models which imply that overeducation can last for longer periods of time. For example, according to the job competition theory formulated by L. Thurow, workers queue for better paying primary-sector jobs, and are ranked by education level. From the employer point of view, under imperfect information, education serves as a proxy for future job performance and trainability. When the educational attainment of workers increases, the returns to schooling fall and the low-skilled are ‘crowded-out’ into low wage jobs or out of the labor market altogether. However, despite lower returns, investment in education is not reduced, since it enables workers to keep their position in the labor queue (Thurow, 1972). As a result of more schooling, a larger number of graduates end up in occupations below their level of skills. A similar mechanism is predicted by Spence’s job screening model, which assumes that individuals invest in education to signal higher productivity to potential employers (Spence, 1973). The job competition theory also assumes that workers in low level occupations are often unable to compete for more demanding positions, as most actual job skills are acquired through experience and on-the-job training. Under these conditions, overeducation is likely to become a permanent state for individuals. The view of undemanding jobs as traps may also become a self-fulfilling prophecy, as the overqualified lose their motivation to search for better matched work. An alternative explanation of overeducation is offered by the assignment model (Sattinger, 1993), which conceptualizes the earnings distribution as arising from the equilibrium solution to the problem of allocating heterogeneous workers to jobs of varying complexity. This allocation is achieved through individual choices of workers seeking to maximize their utility. Since in practice, the distribution of available jobs according to skill requirements does not fully match the distribution of workers according to their level of education, some individuals end up in jobs for which they are over- or underqualified. Such educational mismatches may become relatively persistent if the job structure is unresponsive to changes in the supply of workers with varying levels of schooling (Sloane, 2003; Tinbergen, 1984). Further mismatches arise due to imperfect information. For example, since workers do not have complete knowledge of their potential earnings or utility in each job, they engage in search behavior, by setting a reservation wage and accepting the first job offer with a wage that meets this criterion. Under such conditions, individual decisions to form or reject job matches do not necessarily lead to an optimal allocation, in which all workers are assigned to the

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specific jobs at which they are most productive or which fully utilize their skills (Sattinger, 1995). 2.2. Earlier research results Over the last years, there have been many publications attempting to assess the validity of the career mobility hypothesis. Initial studies, based on cross-sectional data (for example, Alba-Ramirez, 1993), found a negative relationship between overeducation and job tenure. Overqualified respondents experienced higher levels of job mobility, and also were more likely to be at the beginning of their careers. These findings were interpreted as supportive of the hypothesis, implying that overeducation was indeed a temporary situation and concerned mostly young people entering the labor market. However, some pointed out that such results could also be explained in terms of the dual labor market theory. For instance, Sloane, Battu, and Seaman (1999), in their analysis of the employment history of British overqualified workers based on retrospective survey data, found that the overeducated experienced more frequent job changes compared to other workers, but did not observe any signs of upward mobility as suggested by matching or career mobility hypotheses. On the contrary, the data showed that the overeducated were more likely to have experienced spells of unemployment and involuntary job termination. (See also McGuinness & Wooden, 2009, for similar results based on preliminary analyses of Australian longitudinal data.) The finding that overschooling is relatively more common among younger workers, can also be explained by cohort effects (Bu¨chel & Mertens, 2004). In light of these opposing interpretations, attempts were made to evaluate the career mobility hypothesis directly, using panel data. These studies typically use multivariate regressions explaining the probability of upward mobility, either to an occupation with higher skill requirements, higher status, or to higher wages, with overeducation at the starting point included among the independent variables. The results were also mixed. Some suggest that overqualified workers are more likely to improve their labor market position after one or two years, compared to other workers (Dekker, de Grip, & Heijke, 2002; Sicherman, 1991). However, many of these findings were criticized on grounds that the models did not include controls for the respondents’ initial occupational status. Therefore, the results may have been biased by the fact that career mobility usually occurs from jobs with low qualification requirements. When this is taken into account, it seems that there are no significant differences in mobility among overeducated workers compared to others in occupations with similar requirements, at least in the short-term (Bu¨chel & Mertens, 2004; Groot & Maassen van den Brink, 2003; Robst, 1995a; see also: Korpi & Ta˚hlin, 2009). This is true even though overschooling has also been found to increase the likelihood of searching for other work and expressing quit intentions (Groot & Maassen van den Brink, 2003; Robst, 1995a). Other analyses suggest that overeducation, at best, has no influence on respondents’ perceived access to on-the-job training, which could lead to a better job or a promotion (Alba-Ramirez, 1993; Bu¨chel & Mertens, 2004; Korpi & Ta˚hlin, 2009; Robst, 1995a).

Even if overqualified workers are indeed more likely to move into better or more demanding jobs compared to their adequately allocated counterparts, this does not necessarily mean that their mobility always leads to an adequate match. Several studies, each using a different methodological approach, suggest that the percentage of overqualified workers who remain overqualified after one year or more is considerably high. For example, in the U.S. throughout the 1990s, around 74% of the overeducated were similarly mismatched after one year (Rubb, 2003). In Great Britain, 46% of those who were mismatched in 1991 were found to be in the same situation five years later, and 34% ten years later (Lindley & McIntosh, 2009). In the Netherlands, survey data from the mid-1990s show overschooling persistence to be somewhat lower, but still substantial – 41% of those who perceived themselves overeducated for their job in 1994 held the same perception two years later (Groot & Maassen van den Brink, 2003).2 However, a recent study of subjective overqualification persistence, based on data from the Swiss Household Panel covering the years 1999–2003, found little evidence of prolonged job mismatches (Frei & Sousa-Poza, 2012). A more direct test of the career mobility hypothesis is offered by cohort studies of school leavers, which analyze overeducation among individuals who are at the beginning of their careers. Their results differ depending on the methodology used and cohort studied. For example, according to a study of 1980 U.K. college graduates, as much as 30% of college-educated labor market entrants remain overeducated for their work six years after graduation (Dolton & Vignoles, 2000), while surveys of younger 1995 and 1999 graduates found that the percentage of respondents employed in non-graduate occupations fell rapidly during four years following graduation: from almost 50% to below 20% (Purcell, Elias, Davies, & Wilton, 2005). However, analyses which explicitly relate current to earlier overeducation tend to support the claim that education–job mismatches are a trap for many young workers. A multi-national 2005 survey of European college graduates who got their degree in the academic year 1999/2000 found that, among those who were overeducated in their initial employment, between 30% and 58% remained overeducated five years following graduation. By contrast, between 3% and 6% of graduates who were matched in initial employment were found to be overeducated five years later (Verhaest & van der Velden, 2010; see also McGuinness & Sloane, 2011, for a more detailed analysis of U.K. data). Another example is offered by an earlier Canadian panel study of three cohorts of college graduates: the study found that as much as 74% of individuals who were overeducated two years following graduation were still overeducated three years later

2 It should also be noted that despite the fact that the Dekker et al. (2002) study found significant positive relationships between overeducation and the likelihood of upward mobility in the Netherlands, the relatively low overall percentage of workers who improve their job level (8%) and the relatively high percentage of overeducated workers (30.6%), together with the regression coefficient of 0.66 suggest that still a majority of overeducated workers remain in this situation after two years.

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(Frenette, 2004). It also appears that there may exist a significant group of young graduates who remain consistently overeducated for longer periods of time. According to a U.K. survey of 1985 graduates, around 30% had never had a job which required a degree during eleven years following graduation (Battu, Bellefield, & Sloane, 1999). Similar results were found in a more recent study of the career patterns among Flemish high school leavers: around one-third remained in overeducation most of the time during the first seven years after labor market entry, and an additional 11% entered into persistent overeducation after a prolonged period of joblessness (Verhaest & Schatteman, 2010). There are reasons to expect that, even in the long run, overeducation could become a permanent state. When individuals are deprived of opportunities to apply their skills in the jobs they have, they may be less able to sustain their cognitive abilities than adequately matched workers. At the same time, the skills acquired during their education may become obsolete, leading to a deterioration of their human capital. The preliminary results of a recent analysis of longitudinal test data (De Grip, Bosma, Willems, & van Boxtel, 2008) suggest that job–worker mismatch is associated with a decline in workers’ immediate and delayed recall abilities, as well as their verbal fluency. It also seems that cognitive decline increases with the extent of occupational mismatch. These results imply that in the case of overeducation there exists a state dependence: the longer individuals remain in jobs for which they are overqualified, the more difficult it becomes to achieve upward mobility. On the other hand, cognitive decline may be viewed as another process, besides career mobility, adjusting the match between workers’ abilities and the level of their jobs. This could explain the seemingly contradictory findings of studies which suggest that the overeducated often report increased skill requirements for their jobs, or a better job–education match (particularly when longer term changes are taken into account), even while they continue to perform the same job at the same firm (Groot & Maassen van den Brink, 2003; Robst, 1995a). For example, Groot and Maassen van den Brink (2003) found that although the length of tenure with the current employer decreased the probability of subjective overschooling, this could not be a result of changing jobs (internal mobility had no statistically significant effect on overeducation). They conclude that, as time passes, workers’ perceptions of the match between their education and jobs tend to become more positive. If this is the case, we should not be overly optimistic about studies suggesting a long-run improvement of workers’ subjective perceptions of the match between their skills and jobs. Although research results generally suggest that overeducation is often a trap for workers, these findings are still far from conclusive. An important limitation of the existing dynamic analyses is that they take into account mostly short-term changes (typically after one or two years) and often focus on any upward mobility rather than mobility out of overeducation. Further, few studies attempted to deal with the possible relationships between immobility and the general economic situation. I am aware of only two such studies based on panel data: Asplund and Lilja (2000) for Finland, and Rubb (2003), for the U.S., of

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which only the former takes into account long term economic changes. To my knowledge there are no studies which explicitly focus on the demographic and social distribution of persistent job mismatches (see, however, Mavromaras & McGuinness, 2012, who analyse overskilling state dependence by education level). The present paper extends the previous analyses in that it examines long-term changes, puts more emphasis on the economic context of labor market trends, and examines the characteristics of those who appear trapped in overeducation. Finally, there are practically no detailed analyses of overeducation in transition economies, particularly using panel data. One exception is Lamo and Messina’s (2010) study of overeducation in Estonia. The latter study, however, focuses on the wage effects of educational mismatch, and although it uses panel data, it does not analyze dynamic relationships. 2.3. Hypotheses In the present study, I adopt the following three hypotheses. Hypothesis 1. Overeducation in post-communist Poland can be characterized by relatively high and increasing persistence. There are two factors which may contribute to this trend. First, the rapid increase in the supply of college graduates over the last years. This ‘educational boom’ was motivated by the notion that higher education was an entry ticket to better jobs (UNDP, 2007). However, it has been pointed out (for example, by Kabaj, 2005) that the Polish labor market is unable to accommodate the growing number of college educated workers. Indeed, analyses of Polish survey data suggest that the prevalence of overeducation increased systematically in the period 1988–2003 (Słomczyn´ski & Krauze, 2003; the results for 2003 are available from the author). In this context, there are reasons to believe that overeducation is better understood in terms of the job-competition than the human capital model. Second, the relatively low spatial and occupational mobility of the Polish labor force (UNDP, 2004). According to public opinion research, only one out of four respondents expressed the willingness to change their place of residence in order to find a better job (or any job at all) (Public Opinion Research Center [CBOS], 2008). Such results suggest that occupational mismatches are likely to last for a prolonged period of time. Hypothesis 2. The increase in the persistence of overeducation is more pronounced during economic recession. I expect that the incidence of long-term overeducation in Poland was relatively higher in the years 1988–1993 and 1998–2003. The former were the first years of the postcommunist transition, marked by economic instability, the collapse of many state-owned companies, the appearance and fast rise of joblessness. The latter was a time of economic slowdown. By 2002, the unemployment rate in Poland reached a record value of 20%. In the relatively prosperous years 1994–1997 and 2004–2008, the situation

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on the labor markets improved (EUROSTAT, 1998–2009; GUS, 1990–2010; UNDP, 2004). This hypothesis is based on the skill-bumping assumption: during recession, when work is less available, higher educated individuals take the jobs previously performed by workers with less schooling, while the latter are crowded out into unemployment or inactivity (Dolado, Felgueroso, & Jimeno, 2000; Muysken & Ter Weel, 2000; Thurow, 1972; Verhaest & van der Velden, 2010). As labor mobility tends to fall at times of economic slowdown, overeducated workers may also find it more difficult to search for more adequate employment (Asplund & Lilja, 2000; Evans, 1999). Hypothesis 3. Persistent overeducation is more common among young workers who entered the labor market after the transition. There exists a large body of research showing that overeducation is relatively more prevalent among those who are at the beginning of their careers (for example, Alba-Ramirez, 1993; Dekker et al., 2002; Kiersztyn, 2007a; Patrinos, 1997). The career mobility and job matching hypotheses, as well as the status trajectory model (Słomczyn´ski, Krauze, & Peradzyn´ski, 1988) predict that the occupational positions of young workers improve as they grow older. There are, however, reasons to expect that the opportunities for upward mobility may no longer be there for the youngest cohorts. If the general persistence of overeducation has increased over the last decades due to the rapidly growing number of workers with university diplomas (Hypothesis 1), then today’s young workers in low skill, entry-level jobs are likely to be among the ones most affected by this process (Dolado et al., 2000). They may find it more difficult to move towards higher level occupations, since in order to do so, they would have to compete with older, equally educated but also more experienced workers (Groot, 1996).

3. Methodology 3.1. The data Data for the analysis are taken from the Polish panel study on social structure (POLPAN). The study consisted of five waves, conducted in five-year intervals on a random sample of the Polish adult population. The data were collected in the years 1988, 1993, 1998, 2003, and 2008.3 POLPAN is especially well suited for this analysis for several reasons. First of all, it is a unique and valuable source of information due to its length – it is to my knowledge the only Eastern European study in which faceto-face interviews covering a number of social, economic and political issues were conducted with the same respondents over twenty years. As such, it allows for the analysis of long-term labor market trends, not just changes

3 More information on POLPAN and data accessibility is available on the Leibniz Institute for the Social Sciences (GESIS) website. Results are presented in: Słomczyn´ski, K. M., Marquart-Pyatt, S. T. (Eds.), Continuity and change in social life: Structural and psychological adjustment in Poland. Warsaw: IFiS Publishers.

which could be a result of short-term shifts in the economic situation. Another important point about POLPAN is that it covers the whole period of the postcommunist transition, a time of profound social, institutional and economic change, making it possible to control for the effects of differing economic contexts on the persistence of overeducation. Second, starting from the 1998 wave, the panel sample has been supplemented by younger cohorts of respondents, enabling the analysis of life-cycle and cohort effects independently of each other. The unit of analysis is a respondent during the time of two consecutive waves of the panel study (henceforth, tn and tn+1, where n is the number of the first of the two waves). I selected from the POLPAN sample respondents aged 21 and over at tn, who were working for an income at both tn and tn+1 and who have not reached retirement age (which, in Poland, is 65 for men and 60 for women) at tn+1. Overeducation is considered persistent if a given respondent experiences such a situation for at least two consecutive waves of the panel. Five years is a longer interval than the one taken into account in many labor market studies. However, if there is a group of temporarily overeducated workers who achieve an adequate job match only after several years, then focusing on short-term mobility might lead us to mistakenly include those workers among the ones trapped in occupational mismatch. This point is made more important by the fact that one of the objects of this analysis is to examine the relationships between the persistence of overeducation and age. Since it is commonly assumed that the young are more likely to move on to better jobs, compared to older workers, focusing on short-term transitions might bias the results.4 3.2. The model I analyze overeducation persistence by means of random-effects logistic regression models, relating overeducation at tn+1 to its lagged value. These models can be written as: OEDin ¼ X 0in b þ g OEDin1 þ ci þ uin

(1)

where i denotes the individual, n denotes the number of the POLPAN wave, OEDin is a binary variable which takes the value 1 for those who are overeducated and 0 for those who are well matched, X 0in is a vector of explanatory variables, ci is an individual specific measure of unobserved heterogeneity, and uin is an error term. Several endogeneity issues need to be addressed. First, the problem of initial conditions, which occurs if the stochastic process under study is not observed from the

4 Analyzing long term transitions can, however, be problematic if individuals shift into and out of jobs between panel waves. Not all of those who were well matched at the time of two consecutive panel waves, have continuously been in this situation throughout the whole five-year interval. Some respondents may experience short-term mobility out of and back into low skilled employment. However, such changes are unlikely to affect the results of this study, as labor mobility in Poland is generally low. Additional analyses of the respondents’ job trajectories (based on retrospective employment history data) confirm that situations in which a respondent experienced movement into or out of overeducation twice or more between two POLPAN waves are very rare (only 3.5% of the analyzed cases).

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very beginning, and being overeducated at the start of the process is dependent on a set of unobserved individual characteristics, which also affect the probability of overeducation being observed in consecutive panel waves. Ignoring this problem results in inconsistent and biased parameter estimates. In particular, the strength of the relationship between the dependent variable and its lagged value would be overestimated (Heckman, 1981). A second problem arises from the fact that out of all the respondents aged 21–59 who were working at tn, some could not be included in the dynamic analyses, either because they were not working at tn+1, or due to panel attrition.5 There are reasons to expect that the probability of non-inclusion is not randomly distributed with respect to the process under study. For example, overeducated workers have been shown to be more likely, compared to the well-matched, to hold unstable employment (Sloane et al., 1999). Overeducation may also affect the chances of panel attrition, in particular, resulting from migration in search for better employment. A third important issue to be taken into account is unobserved individual heterogeneity, particularly with respect to the actual level of skills (Sloane, 2003). There are many studies which suggest that overeducation at both tn+1 and tn may be explained by factors like: individual abilities, personality traits, college quality, and on the job experience and training (Bauer, 2002; Bu¨chel & Pollmann-Schult, 2004; Chevalier, 2003; Chevalier & Lindley, 2009; Frenette, 2004; Robst, 1995b). Thus, failing to account for individual heterogeneity is likely to cause an upward bias of the regression coefficient for lagged overeducation. In order to deal with these problems, I apply the approach proposed by Wooldridge (2005). This method accounts for initial conditions and unobserved heterogeneity by specifying an auxiliary distribution of the unobserved individual effect conditional on the initial observation OEDi1 and the explanatory variables: ci ¼ j0 þ j1 OEDi1 þ X 0i j2 þ ai

(2)

where ai is a measure of unobserved heterogeneity which is independent of both X 0in and OEDi1, and X 0i are the means of the time varying variables for each individual, as in the Mundlak (1978) specification. If X 0in contains binary variables which change over time, such variables for each time period are also included in X 0i . The final model is the following: OEDin ¼ X 0in b þ g OEDin1 þ j0 þ j1 OEDi1 þ X 0i j2 þ ai þ uin

(3)

5 In POLPAN, the share of respondents who were excluded from the panel was particularly high in 1993 (69.9% of those below retirement age and working in 1988). This was, however, mostly due to the fact that the 1993 survey was conducted on a random subsample of the original 1988 sample, and therefore should not bias the results. The response rate in 1993 was 76%, and the employment drop-out rate was 9% (of those who were below retirement age and working in 1988). In the later waves the overall shares of respondents excluded from analyses due to sample or employment attrition were 36.6%, 45.6%, and 36.1% of those aged 21–60 and working in 1993, 1988, and 2003, respectively.

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In the results section, I start by reporting initial estimates of Eq. (1) on an unbalanced sample of respondents present in at least two successive waves of POLPAN. To assess whether the regression results hold after controlling for initial conditions and unobserved heterogeneity, I then estimate the model (3) on two subsets of observations constituting balanced panel samples of individuals present in at least three successive waves. 3.3. Measurement of overeducation There are three alternative approaches to the measurement of overeducation that have been used in the literature (for a review, see Halaby, 1994; Hartog, 2000; Verhaest & Omey, 2006). The first, sometimes referred to as the job analyst (or objective) method, is based on systematic expert evaluations of the level of qualifications needed to perform a certain job. If a worker’s education is above the level required for the performance of his or her job, he or she is considered overeducated. The second, subjective method, is based on a comparison of the respondents’ self-assessment of the educational requirements of their jobs with their actual education. In some cases, the respondents are also asked directly whether they consider the match between their qualifications and jobs adequate or not. In the case of the third approach (the socalled empirical or realized matches method), overeducation is defined as existing when the worker’s educational level is more than one standard deviation above the mean years of schooling for workers with the same occupation. Some authors use a modified empirical measure, replacing the mean-based criterion by the modal level of education in each occupational category. The POLPAN data does not include a subjective measure of overeducation, and applying any of the remaining two standard approaches for the purpose of this paper may cause serious methodological problems. The empirical, meanbased approach is questionable in the Polish context, as it requires information on the number of years in education. Since Poland has a two-tier educational system, a unidimensional measure such as years of schooling would not reflect the actual differences between people with different educational credentials. Further, due to the relatively small sample sizes in POLPAN, the mean or modal values of educational attainment may only be obtained for relatively large, heterogeneous occupational categories, which would lower the validity of the overeducation indicator. The validity of the job analyst indicator is weakened by the lack of correspondence between the ordinal completed schooling variable and the educational requirements scale. Any attempt to convert the scale into levels of education would require questionable assumptions concerning the relationship between these two variables (Halaby, 1994). In order to avoid the caveats described above, I propose a new approach to the measurement of overeducation. Occupational mismatch is defined as occurring among individuals who are in jobs with relatively low educational requirements scores given their level of schooling. Specifically, a respondent R is considered overeducated during a POLPAN wave n if the educational requirements

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score attributed to R’s main occupation is lower than one standard deviation below the mean for all respondents in R’s educational category in the first POLPAN wave. This approach is similar to the standard job analyst method in that it uses educational requirements scores assigned to different occupations through expert evaluations. However, by taking as reference the actual distribution of occupations with differing skill requirements among respondents with the same level of education, it avoids the arbitrariness involved in linking the requirements scores to educational credentials.6 In my calculations, I use the educational requirements scale for 3-digit-level Polish Social Classification of Occupations (SKZ) categories, developed in the early 1980s by Słomczyn´ski (1983) and updated in 2004 (Słomczyn´ski, 2007). The values of this scale range from 6 for unskilled service workers to 89 in the highest level managerial and professional occupations. The threshold values of the educational requirements scores were calculated separately for three educational categories: high school graduates, those with post-secondary vocational or incomplete university education, and university graduates. The lowest value of educational requirements score with which a respondent’s job is assumed to be adequately matched (the value obtained by subtracting the standard deviation from the mean requirement score) was 29.21 for respondents with secondary education, 43.30 for those with post-secondary vocational or incomplete university, and 60.75 for university graduates. I assumed there could be no overschooling among those with only elementary or basic vocational education. It is important to note that the overeducation threshold values for all five panel waves were calculated on the basis of data from the first, 1988 POLPAN wave. Calculating the threshold values separately for each wave yields overeducation indicators which are based on the distribution of workers with different levels of education in various occupations at a given point in time, and therefore precludes the study of how shifts in the educational and occupational structures affect the risk of job mismatch. If, for example, a rapid growth in the number of college graduates raises the share of highly educated individuals in jobs with low requirements, the overeducation threshold for such workers falls accordingly – so that relatively fewer workers are considered overeducated in the later periods. (In an extreme case, even a university educated bus driver or janitor would not be defined as overeducated, if a sufficiently large share of workers with higher education has similar jobs.) Using thresholds calculated separately for each wave could also lead to a situation in which many respondents appear to move out of (or into) overeducation, even while their actual

6 Although the overeducation measure proposed in this paper differs from the ones commonly used in the literature, in the case of Poland, the choice of measurement does not appear to strongly affect the overall incidence of job mismatches. Additional analyses of the most recent, 2008 POLPAN data found a similar share of overeducated respondents (around 20%) using the subjective and realized matches methods, and the approach proposed here. The indicator used in this paper was found to be strongly correlated with the realized matches measure, based on the modal level of education (Pearson r = 0.76, p = 0.000, N = 1024).

level of education and occupation remain unchanged (Mehta, Felipe, Quising, & Camingue, 2011).7 There are two additional important issues to consider regarding the overeducation indicator used in the present analysis. First, it has been pointed out that since educational requirements scales for occupations are not updated regularly, they fail to capture changes in the level of qualifications necessary for the performance of a given job over time (for example due to technological innovations). This may bias the movement out of overeducation downwards in studies using such scales (Borghans & de Grip, 2000; Mendes de Oliveira, Santos, & Kiker, 2000). However, a recent analysis of educational requirements for the updated SKZ revealed that those shifts were not large enough to cause any major changes in the distribution of the requirements scores since 1983 (Słomczyn´ski, 2007). A second difficulty results from the heterogeneity of jobs within broadly defined occupational categories. Adequately matched individuals could be mistakenly included among the overeducated, if the educational requirements scores attributed to their occupations do not reflect the actual schooling required for the performance of their specific jobs. In Poland, such misclassifications seem most likely among farmers and proprietors. Running a farm can, in some cases, require quite specialized knowledge (including marketing and promotion or applying for EU funds). However, the overall educational requirements score for this category is only 29 (on a scale of 6–89) – the same as, for example, for truck drivers or plumbers. Being a proprietor can also mean running a complex, innovative business. However, for owners of many types of companies the value of the requirements score is slightly below the overeducation threshold for workers with college degrees (58, while the threshold is 60.75). I deal with this problem by including an occupational control variable in the regression models presented below. 3.4. Independent variables To capture differences in the economic context of the consecutive five-year intervals, I use a dummy variable tn+1 year, which identifies cases where tn+1 refers to the 1993, 1998, 2003, and 2008 POLPAN waves. The variables age category at tn and cohort were created on the basis of the respondents’ year of birth. While the former may have different values for the same respondent (depending on the year of the POLPAN wave), the latter remains unchanged for each panel participant. The cohort variable identifies respondents born in the years: 1963–1967,

7 It should be noted that the threshold value of educational requirements scores for workers with a given level of education, is dependent on the fit between the educational and occupational structure in 1988. Since there are reasons to doubt that the allocation of people to jobs was fully meritocratic under Communism, the validity of measure of overeducation used in these analysis can be subject to critique. However, as mentioned above, the available data suggest that the mismatch between education and occupation grew in the years following the transition (mainly due to the unprecedented growth in the number of college graduates). Using 1988 as the base year allows us to capture changes in the incidence of persistent overeducation which are the result of these long-term trends (Kiersztyn, 2007b).

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Table 1 Incidence of overeducation and persistent overeducation in Poland, 1988–2008. Year (tn)

1988

1993

1998

2003

2008

Overeducated among all working respondents (%) Overeducated among those working 5 years earlier (%) Overeducated among not included or not working 5 years earlier (%) Persistently overeducated (%)

7.5 – – –

13.9 13.4 16.4 6.1

15.8 14.7 19.9 10.4

20.1 16.9 28.5 10.7

19.4 14.7 27.8 9.2

Notes: In the first row, respondents who did not participate or were not working in other POLPAN waves are taken into account (sample sizes in the consecutive waves are: 4426, 1501, 1144, 825, 794). Incidence of persistent overeducation: The percentage of overeducated at tn and tn+1 among all working respondents (sample sizes: 1294, 903, 597, 510).

1968–1972, 1973–1977, 1978–1982 (the youngest cohorts, aged 21–25 in the first, second, third, and fourth wave of the panel, respectively). The remaining respondents (born in the years 1928–1962) are the reference category. The age category variable identifies the youngest (aged 21–25, 26–30, and 31–35 at tn) and oldest (aged 40– 44, 45–49, and 50–59 at tn) groups of respondents. 36–39year-olds are the reference category. It should be noted that although respondents aged 21–25 were not included in the 1993 wave of POLPAN, on the basis of the employment history and educational variables from the 1998 survey it was possible to assess whether respondents aged 26–30 in 1998 were overeducated five years earlier. Apart from the general economic situation, another important factor affecting the risk of overeducation is the unemployment rate in the respondents’ region of residence. Due to limited spatial mobility, the chances of finding an appropriately matched job are mainly determined by the employment opportunities available on the local (regional) labor market (Bu¨chel & Van Ham, 2003). The regional unemployment rate at tn+1 is measured as number of registered unemployed per 100 production age population in the respondents’ province (voivodeship) of residence. This variable is based on the former, pre-1999 administrative division of Poland into 49 voivodeships.8 Among the remaining control variables, I included gender, with men as the reference category. To account for the possibility that observed trends in the incidence of occupational mismatches may largely be a result of changes in the shares of respondents who, by definition, are eligible to be included among the overeducated, I control for individual educational attainment. Education at tn groups the respondents into three categories: high school (together with post-secondary vocational) graduates, university graduates, and those with elementary (completed or incomplete), or basic vocational education as the reference category. (1993 data on education for the youngest age group are based on information from the 1998 wave of the survey.) Since the risk of overeducation is likely to decrease among workers with job-specific experience (Becker, 1962; Sicherman, 1991), I also include the respondents’ years of tenure with their current employer in the regression models.

8 Since the territorial reform of 1999, Poland is divided into 16 voivodeships. However, the POLPAN data base provides information on the respondents’ voivodeship of residence at the time of each wave only according to the former administrative division. In addition, the current voivodeships are too large to allow to capture the local economic conditions which may affect the labor market chances of individuals.

Finally, to control for potential biases resulting from within-occupation heterogeneity, an additional variable, occupation at tn, is also included in the regression models. It is based on one-digit SKZ codes, and identifies respondents who were independent farmers (code 7) or proprietors (code 8) at tn. The remaining occupations are the reference category. 4. Empirical results Data on the overall incidence of overeducation, presented in the first row of Table 1, follow an upward trend during the first fifteen years of the transition. The growth rate is counter-cyclical: during the recession periods of 1988–1993 and 1998–2003, the overall percentage of overeducated workers increased by 6.4 and 4.3 percentage points, respectively. Between 1993 and 1998, the incidence of overeducation increased by only 1.9 percentage points, and in the most prosperous years 2003–2008, it even fell by 0.7 percentage point. This finding is consistent with the skill-bumping hypothesis, according to which highly educated workers tend to crowd-out the low qualified during recession. It appears, however, that after 1993, this process concerned mostly new labor market entrants. The incidence of overeducation among POLPAN respondents who were also working during the preceding wave (second row of Table 1) grew rather modestly during the recession years 1998–2003 (by only 2.2 percentage points), while overschooling among new panel participants (aged 21–25) and respondents who were unemployed or inactive five years earlier increased by 8.6 percentage points. At the same time, I find no direct support for the hypothesis that persistent overeducation in Poland increased over the last two decades: the percentage of respondents who were overqualified for their work during two consecutive panel waves (last row of Table 1), after an initial 4 percentage point jump between 1993 and 1998, remained relatively stable. An interesting way to look at the overall persistence in overeducation is to see whether, controlling for important worker characteristics and the economic context, having a job below one’s qualifications is associated with analogous mismatches five years later. I use random-effects logistic regression models, estimated on the full POLPAN sample, and on a sample restricted to those with at least high school education. The results, presented in Table 2, suggest that this relationship is quite strong: those overeducated at tn were almost seven times more likely to still be in that situation at tn+1, compared to other workers, when the occupational control variable is included in the equation

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Table 2 Determinants of overeducation at tn+1 (random effects logistic regression), 1988–2008. Independent variable

Overeducation at tn Gender (1 = female) tn+1 year 1993 1998 2003 Cohort – born in 1963–1967 1968–1972 1973–1977 1978–1982 Education at tn High school University Tenure at tn+1 Regional unemployment at tn+1 Occupation at tn Farmers Proprietors Log likelihood Observations

Model 1a

Model 1b

Model 2a

Model 2b

Odds ratio

Standard error

Odds ratio

Standard error

Odds ratio

Standard error

Odds ratio

Standard error

10.82** 0.63**

1.466 0.079

6.75** 0.64**

1.063 0.080

10.66** 0.59**

1.569 0.088

6.60** 0.61**

1.145 0.088

0.95 0.97 0.93

0.221 0.209 0.222

1.01 0.92 0.92

0.236 0.201 0.219

0.87 0.94 0.90

0.234 0.232 0.244

0.89 0.88 0.86

0.233 0.213 0.227

1.02 0.60o 0.90 0.95

0.222 0.161 0.327 0.377

1.03 0.66 0.95 1.19

0.230 0.176 0.350 0.463

0.74 0.51* 0.83 0.99

0.205 0.159 0.357 0.437

0.71 0.56o 0.81 1.17

0.195 0.167 0.345 0.497

5.56** 3.72** 0.96** 1.04**

1.023 0.750 0.007 0.015

8.44** 5.89** 0.95** 1.04**

1.749 1.328 0.007 0.015

0.62** 0.95** 1.05**

0.101 0.009 0.017

0.67** 0.94** 1.05**

0.104 0.009 0.017

3.53** 2.11**

0.766 0.406

4.72** 1.96**

1.493 0.429

948.17369 3287

928.47576 3287

736.54938 1751

722.226 1751

Notes: Reference categories for tn+1 year and cohort are: 2008 and respondents born before 1963. In models 2a and 2b, the sample is restricted to respondents with high school or university education. * Significant at p < 0.05. ** Significant at p < 0.01. o Significant at p < 0.10 (2-tailed).

(model 1b). The latter finding cannot be explained by the fact that those who have not completed high school are automatically assumed not overeducated, as it remains practically unchanged when respondents with elementary and vocational education are excluded from analysis (models 2a and 2b).9 To assess the extent to which the relationship between the likelihood of overeducation at tn and tn+1 may be attributed to actual state-dependence, I estimated additional models on two balanced samples of respondents present in at least three waves of the panel, controlling for initial conditions and unobserved heterogeneity through Mundlak corrections (Table 3). The first sample includes 758 respondents who participated in the 1988, 1993, and 1998 waves, and the second sample (429 respondents) covers the 1998, 2003, and 2008 waves. Since samples restricted to those who participated in the initial panel waves exclude members of the youngest cohorts, in all the models presented in Table 3, the cohort variable is replaced by age category at tn. The variables included in Xi are restricted to the length of tenure and the regional employment rate, as the other control variables are either

9 The simultaneous inclusion of respondents’ age category and cohort membership in the regression equations caused collinearity problems. For this reason, age at tn was excluded from the models presented in Table 2. Although additional analyses (available upon request) found some weak positive correlations between belonging to certain age categories and the chances of overeducation, these results were neither robust nor easily interpretable. Including the age variable in the model had no effects on the odds-ratio for overeducation at tn.

time-invariant, or in practice do not vary much over the estimation period (education, occupation). First of all, it is important to note that in the initial equations (models 3a and 4a), the results for both subsamples do not differ much from those observed in the full, unbalanced sample of POLPAN respondents (model 1b in Table 2). When the indicator of overeducation at t1, the average length of tenure, and the average regional unemployment rate are included among the control variables (models 3b and 4b), the relationship between overeducation at tn and tn+1 is weaker, but still statistically significant. It appears that even when initial conditions and unobserved heterogeneity are accounted for, those overeducated at tn were about four times more likely to remain in this situation at tn+1, which is consistent with Hypothesis 1.10 This

10 I reran the regression analyses using an alternative indicator of initial overeducation, one more closely related to the start of the process under study. On the basis of retrospective employment and educational history data from the 1988 POLPAN survey, I created a binary variable informing whether the respondent was overeducated in his or her first job following graduation (overeducation at t0). Substituting overeducation at t1 by overeducation at t0 in the 1988–1998 equation increases the regression coefficient for overeducation at tn (the odds ratio was 5.94 and significant at p < 0.01). This finding suggests that the strength of the relationship between overeducation and its lagged value in models 3b and 4b may have been underestimated due to the fact that they covered only two time periods. I also estimated additional models on subsamples restricted to respondents with at least high school education. Although the latter results should be treated with caution, due to small sample sizes, they confirm the findings of the other models. Full results available upon request.

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Table 3 Determinants of overeducation at tn+1 (random effects logistic regression), with controls for initial conditions and unobserved heterogeneity, in 1988–1998 and 1998–2008. Independent variable

1988–1998

1998–2008

Model 3a

Overeducation at tn Overeducation at t1 Gender (1 = female) tn+1 year Age category at tn 21–25 26–30 31–35 40–44 45–49 50–59 Education at tn High school University Occupation at tn Farmers Proprietors Tenure at tn+1 Average tenure Regional unemployment at tn+1 Average regional unemployment Log likelihood Observations

Model 3b

Odds ratio

Standard error

7.45**

1.906

0.50** 1.48o

Odds ratio

Model 4a

Model 4b

Standard error

Odds ratio

Standard error

2.102 2.111 0.112 1.212

7.13**

2.344

0.099 0.340

3.76* 2.80 0.43** 2.47o

0.83 1.35

1.28 0.92 1.43 1.80* 2.24* 2.04

0.623 0.310 0.399 0.534 0.798 0.938

1.31 0.92 1.44 1.93* 2.54* 2.41

0.712 0.340 0.443 0.633 1.059 1.290

9.46** 4.67**

2.921 1.608

11.04** 4.85**

3.06** 3.42** 0.95**

0.976 1.101 0.011

1.05*

0.021

3.09** 3.78** 0.95o 0.99 0.95 1.12

399.79125 1510

Odds ratio

Standard error

0.228 0.511

4.31* 2.23 0.86 0.09*

2.786 1.732 0.248 0.094

1.08 0.47 1.63 1.05 1.25 1.18

0.818 0.260 0.767 0.428 0.546 0.593

0.92 0.42 1.44 1.01 1.22 1.06

0.757 0.245 0.710 0.433 0.560 0.573

4.263 1.889

8.91** 8.78**

4.474 4.599

10.26** 9.89**

5.799 5.733

1.122 1.469 0.023 0.028 0.077 0.097

5.76** 1.20 0.96*

2.683 0.437 0.015

0.99

0.040

6.04** 1.06 0.97 0.98 1.46* 0.65**

3.217 0.433 0.032 0.037 0.217 0.105

397.16266 1504

210.46887 750

201.227 746

Notes: In models 4a and 4b, overeducation at t1 denotes overeducation in 1998. tn+1 year is a binary variable which takes the value 1 for 1993 and 0 for 1998 in models 3a and 3b. In models 4a and 4b, the values are: 1 for 2003, 0 for 2008. The reference category for age category at tn is 36–39-year-olds. * Significant at p < 0.05. ** Significant at p < 0.01. o Significant at p < 0.10 (2-tailed).

Table 4 The rates of transition into overeducation at tn+1 depending on overeducation at tn (A, B), persistently overeducated among all overeducated at tn+1 (C), and the rate of transition into overeducation among those overeducated at tn relative to the well-matched at tn (A/B) in each five-year period, 1988–2008. Period of transition

1988–1993

1993–1998

1998–2003

2003–2008

Percent overeducated at tn+1 among those overeducated at tn (A) Percent overeducated at tn+1 among those well-matched at tn (B) Percent of overeducated at tn among those overeducated at tn+1 (C) Relative rate of transition into overeducation at tn+1 (A/B) Observations

62.7 8.1 45.4 7.7 1294

68.6 5.1 70.9 13.5 903

61.5 7.5 63.3 8.2 597

50.5 6.7 62.4 7.5 510

relationship was somewhat stronger in the later period, 1998–2008. Another result worth noting concerns the relationship between age and overeducation. In models 3a and 3b, overeducation propensity is higher among older workers. This finding is consistent with a recent study of overeducaton in Estonia (Lamo & Messina, 2010), and suggests that in rapidly changing transition economies, overeducation may be in part driven by the structural mismatches between the qualifications acquired by workers under Communism and those required on the labor market. It appears, however, that in Poland, this mechanism persisted only throughout the first decade of the transition. In models covering the 1998–2008 period, the relationship between age and overeducation disappears. The results concerning changes in the persistence of overeducation over subsequent POLPAN waves are shown

in Tables 4 and 5. Data in Table 4 are based on simple transition matrices. Table 5 presents the logistic regression coefficients and odds ratios for overeducation estimated separately for each period, allowing to see whether the association between working below one’s level of schooling at tn and tn+1 is becoming stronger over time, even when controlling for factors such as gender, age group, education, tenure, occupational heterogeneity, and regional unemployment. Although throughout the twenty-year period, a majority of those in jobs below their qualifications at tn were in the same situation five years later, there is no clear evidence of a systematic growth in the persistence of overeducation in Poland. Regardless of whether we take into account the A/B ratios, or the odds ratios after controlling for other significant variables, the relationship between overschooling at tn and tn+1 appears stable since 1998. This finding is inconsistent with

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Table 5 Relationship between overeducation at tn+1 and overeducation at tn in each five-year period, 1988–2008 (logistic regression coefficients). Period of transition

1988–1993

1993–1998

1998–2003

2003–2008

Coefficient Standard error Odds ratio Observations Log likelihood

1.782 (0.286) 5.943 1294 352.013

2.545 (0.340) 12.739 903 216.526

1.725 (0.370) 5.613 597 174.531

2.032 (0.365) 7.628 510 147.883

Notes: Dependent variable: overeducation at tn+1. All models control for: gender, age at tn (the three youngest and three oldest age categories), education at tn (high school and university), occupation at tn (farmers and proprietors), years of tenure with the current employer at tn+1, and the unemployment rate in the voivodeship of residence at tn+1. All odds ratios are significant at the 0.01 level (2-tailed).

Hypothesis 1. The second hypothesis, which predicts that the growth in the persistence of occupational mismatches in Poland was more pronounced during economic recession, is not supported by the data. The overall incidence of overqualification increased faster at times of high unemployment (Table 1), but overeducation persistence increased only in the relatively prosperous years 1993–1998 and to a lesser extent, when other variables are controlled for, the even more prosperous 2003–2008 period. As can be seen from the second row of Table 4, the growth in overall overschooling in 1988–1993 and 1998–2003 can be partially explained by an increase in the inflow of better educated panel respondents, previously adequately matched, into jobs with low qualification requirements. The lower mobility out of overeducation during recession, assumed in Hypothesis 2, is not visible in the data. On the contrary, it seems that once individuals had found themselves in an occupational mismatch at the end of a recession period, they faced a relatively high risk of staying there during the subsequent prosperous years. In a sense, economic growth, by reducing the risk of occupational downgrading among adequately matched workers, increases or at least maintains the divide between the overqualified and other workers. This is demonstrated in the third row of Table 4: the share of persistently overeducated in the total number of mismatched individuals did not fall at times of prosperity (in 1993–1998 it even grew by 25.5 percentage points).11 Regarding the most recent period, 2003–2008, another factor must be taken into account, namely, panel attrition resulting from migration in search for better employment. After Poland joined the EU in 2004 and foreign migration became more easy for people seeking to improve their labor market prospects, many young or dissatisfied workers decided to take advantage of this opportunity. This tendency is clearly visible in the POLPAN data: respondents who were overeducated in 2003 were overrepresented among those who dropped out from the study five years later (23.4% compared to 18.2%). The youngest cohorts of respondents also had relatively higher attrition probabilities: the percentage of workers aged between 21 and 30 in 2003 among non-participants in 2008 was 39.3%, compared to 22.3% among those who remained in the sample. Under the

11 A similar pattern of results concerning overeducation was obtained when respondents with less than secondary education were excluded from the sample.

human capital model, the non-inclusion of foreign migrants might lead to an underestimation of upward occupational mobility, as emigration is likely to cause a ‘brain drain’ of the labor force. However, the job competition or job assignment model would lead us to expect that emigration lowers overeducation persistence among those who stay, due to the falling number of candidates for high skill jobs. The fall in the share of respondents staying in overeducation by 11 percentage points between 1998–2003 and 2003–2008 (shown in the first row of Table 4) suggests that the latter is the case in Poland. The aim of the last part of the analysis is to answer the question whether the probability of persistent overeducation is dependent on cohort membership (Hypothesis 3). Persistent overeducation is defined as existing among workers overqualified at both tn and tn+1. Data on the incidence of overeducation among members of the each cohort of 21–25-year-olds entering the POLPAN sample in successive waves, presented in the first column in Table 6, confirm that the incidence of persistent overeducation is higher among recent labor market entrants: respondents born in the years 1973–1982 were almost four times more likely to experience persistent overeducation when they were in their twenties, compared to respondents born in 1963–1967. The high incidence of persistent overeducation in the youngest cohort, which entered the POLPAN sample in 2003, is particularly striking given the fact that this cohort was also relatively more likely to benefit from the increased opportunities for foreign migration – which should lower their propensity for long-term job mismatch. In this context, the results of a more detailed analysis of the cohort effects in persistent overeducation, while controlling for the general economic situation (by means of random effects logistic regression models, which include transition period dummies and the regional rate of unemployment) seem interesting. The regression coefficients, presented in Table 6, suggest that the cohort effects are much stronger than would appear from a simple comparison of the percentage persistently overeducated, especially in the case of the youngest cohort. The latter result suggests that if it were not for the generally favorable labor market conditions in 2003–2008, the risk of persistent overeducation among labor market entrants might have been much higher. Consequently, as the current economic crisis makes searching for work outside of Poland a less attractive option, the percentage of qualified young workers trapped in jobs with low educational requirements is likely to increase.

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Table 6 Cohort effects in persistent overeducation: percentage overeducated when aged 21–25; random effects logistic regression coefficients, 1988–2008.

Year of birth 1963–1967 1968–1972 1973–1977 1978–1982

Pct persistently overeducated when aged 21–25

Model 5

Model 6

Coeff.

Standard error

Odds ratio

Coeff.

Standard error

Odds ratio

4.21% 10.47% 16.33% 15.94%

0.336 1.052o 1.888* 2.961**

(0.593) (0.592) (0.734) (0.781)

1.40 2.86 6.60 19.32

0.086 0.841o 1.664* 2.607**

(0.515) (0.505) (0.686) (0.683)

1.09 2.32 5.28 13.56

Log likelihood Observations

686.40638 3302

512.17206 1754

Notes: Dependent variable: overeducation at tn and tn+1. All models control for: gender, period of transition (n), education at tn (university), occupation at tn (farmers and proprietors), and the unemployment rate in the voivodeship of residence at tn+1. Reference categories for tn+1 year and cohort are: 2008 and respondents born before 1963. In model 6, the sample is restricted to respondents with high school or college/university education. * Significant at p < 0.05. ** Significant at p < 0.01. o Significant at p < 0.10 (2-tailed).

5. Conclusions The goal of this paper was to assess whether overschooling can be regarded as a stepping stone into more adequate jobs, or a trap for workers. I analyzed changes in the persistence of overeducation in Poland over twenty years of the post-communist transition, as well as the factors affecting the likelihood of long-term occupational mismatch. The findings suggest that overeducation in Poland is characterized by high persistence. More than 50% of those working in jobs below their level of education remained in that situation five years later, and around one in ten respondents were persistently overeducated. In general, overeducated workers were about four times more likely to be overqualified in the subsequent panel wave, compared to other working respondents, even when other important factors are controlled for. These figures seem quite high, especially given that five years can be assumed sufficiently long to allow many workers to find better matched jobs. These findings are contrary to the assumptions of the career mobility hypothesis, which treats all mismatched jobs as stepping stones to more adequate occupations. It also casts doubt on the validity of human capital theory. In particular, the finding that even after two decades persistent overschooling shows no signs of falling is difficult to reconcile with the notion that this phenomenon occurs only as a result of temporary disequilibrium. Even as the better educated cohorts who entered the labor market over the last twelve years are becoming more atrisk of persistent overeducation, the college boom continues, which is also easier to explain in terms of the job competition or job assignment than the human capital theory. I found no evidence of a systematic growth in the persistence of overeducation. Contrary to expectations, the association between overschooling in two consecutive panel waves was found to be stronger during economic prosperity. These counterintuitive findings may mean that even though the persistence of employment inadequacies is not on the rise, there exists a divide between those in low

skill jobs, who do not enjoy the full benefit of economic growth, and other workers, who seem well protected from slipping into overeducation as the economy improves. The findings reported above confirm the expected cohort effects in persistent overschooling. Workers who entered the labor market at the turn of the Millennium were more likely to find themselves trapped in jobs requiring less education than they possessed. This result is also consistent with Thurow’s job competition theory and Sattinger’s job assignment model, and challenges the assumptions of human capital theory and the career mobility hypothesis. Additional, more detailed research, taking into account the ability of workers, as well as college quality, needs to be done in order to assess the extent to which overeducation persistence, particularly among younger cohorts of workers, can be explained in terms of human capital deficits. Nonetheless, the persistence of overeducation is, in itself, of practical significance, as it suggests that society may not be making the right investments in education. This would be the case if, for example, many of those trapped in mismatched occupations were graduates of colleges offering low quality education, or specialized in fields for which labor market demand is low (Verhaest & van der Velden, 2010). Finally, it should be noted that the results presented in this paper have important policy implications due to the fact that further investment in education is now often treated as a simple way to reduce unemployment, enhance competitiveness, and foster economic growth. If occupational mismatches are mostly temporary, propositions of this kind seem reasonable. However, if low level jobs are becoming traps for educated young workers, as my analysis suggests, such policies should be reconsidered.

Acknowledgements The data used in this analysis come from a study supported by grants from the Polish Ministry of Science and Higher Education [NN 116 33 30 33 and NN 112 33 68 35], the Norwegian Research Council [156809/730], and

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