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Variance composition of match categories in intercept-only models. Against the background that, empirically, the incidence rates of overeducation and overskilling are above underqualification and underskilling, we restrict our attention to the determinants of educational and skill-based overqualification. First, we include a variety of socio-demographic variables and variables reflecting worker human capital models 1.
As a separate model for males reveals, males who did not learn German as their first language on average have a 6. Similarily, an employment gap is positively related to the risk of overeducation in the pooled and the male sample. Therefore, to identify evidence of the occupational mobility hypothesis within these human capital models, we also include age cohorts.
In contrast to the assumption that mismatching is just a temporary phenomenon and part of a career mobility strategy of labor market entrants, our results reveal that younger workers do not have a higher probability of being overeducated With specific human capital task experience and health reflecting productivity, we test the human capital compensation heterogeneous ability assumption, i.
The results for both measures seem to support this assumption. Moreover, for a mean level of years of education the risk of overeducation is substantially lower for workers with ISCED 3B qualifications as compared to university graduates. At the same time, the probability decreases with additional years of education. As found in other studies Hartog , these results support the occupational mobility argument. While results for task experience are consistent with the human capital compensation hypothesis experienced workers have lower risks of overskilling , controlling for other human capital measures, health differences do not matter for overskilling though z-values are at the edge of statistical significance, especially in the female sample.
Models with job characteristics additionally control for supervisor status, firm size and employment status. Models 2—4 demonstrate that, more so than human capital variables, job characteristics play a role in differences in mismatching categories. First, the more the job is characterized by high-skill, non-routine analytic tasks and increases in skill requirements over the last 2 years, the lower the risk of both, overeducation and overskilling.
This could mean that jobs held by overeducated workers have higher skill requirements than can be observed by educational levels, a postulation that favors the job heterogeneity assumption. The risk of overeducation or overskilling is also reduced by routine manual tasks, which is consistent with the assumption that these middle-skilled routine jobs better protect against overqualification because most of these occupations are trained in the German VET system and have clearly defined skill requirements. In the assignment models, we suggested that overeducation might result from a decrease in middling jobs such that formerly adequately matched workers were bumped down in the labor market and thus became overeducated.
Generally, this is an assumption about trends which is hard to test with cross-sectional data. However, consistent with this assumption cognitive routine tasks and non-routine manual tasks increase the risk of overeducation but not overskilling model 4. Finally, with some plausibility from labor market rigidities assumptions, job characteristics such as part-time or shift work are positively related to overeducation and overskilling the latter, however, is not significant for females. Accordingly, we conclude that some overeducated workers might have chosen a less demanding job because it was the only job available given their time resources.
When both sets of variables are included in one model model 3 , there are no substantial changes in any of the human capital or job-related measures.
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From this, we conclude that both causes of heterogeneity have their independent share in the variance of overeducation, thereby strengthening support of the job heterogeneity hypothesis. Model 4 additionally includes the job skill requirements of the occupation. Again, the magnitudes of all human capital and job variables are only slightly reduced, if at all. Over and above the partial effects from worker and job characteristics, the skill content of occupations, i. Actually, the risk is substantially higher the more the occupation relies on non-routine manual tasks.
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Overall, first, the multivariate results stress that, to some extent, overeducation and overskilling are distinct phenomena in Germany. Some decisive covariates have opposite effects on educational- and skill-based overqualification, indicating that those who are overeducated are not necessarily those who are overskilled, and vice versa. Second, the models indicate that over and above worker heterogeneity, job heterogeneity has an independent share in explaining overqualification.
In addition to the regression coefficients, this is also reflected in the fit statistics. This especially applies for overeducation, where the error variance is strongly reduced when job heterogeneity is considered. From the human capital compensation hypothesis, mismatching is largely due to unobserved worker heterogeneity, i. Alternatively, observed mismatching would be rather a short-term phenomenon and thus would disappear with occupational mobility.
In contrast, in assignment models, it is assumed that skill requirements and thus wages are strongly determined by the characteristics of the job, not by the human capital of workers alone. The empty model estimates a mean log hourly wage of 2. In model 1, to test whether the basic ORU pattern is also valid for Germany, we introduce the decomposed ORU measures of educational mismatching for each vocational degree and several worker related variables.
In fact, we observe that the ORU pattern holds for Germany even when controlling for several human capital and socio-demographic variables. The overeducation coefficients are significantly positive, while the undereducation coefficients are negative Thus, overeducated undereducated workers of each qualification level earn more less than those with required levels of education in the same type of jobs, but less more than correctly matched workers with the same amount of education.
Moreover, the absolute value of returns for each qualification group is higher for overeducation than it is for undereducation. Thus, nearly three quarters of the differences at the occupation level is due to the different composition of workers within occupations sorting effect. All models include age cohort.
Models with job characteristics additionally control for economic sector, supervisor status, firm size and employment status. Second, we run a set of models with regressors for educational model 2 and skill-based mismatching model 3. As in the ORU model, model 2 includes standard human capital and socio-demographic variables. As we cannot observe wage growth with our data, our strategy to assess the validity of the occupational mobility hypothesis is to see whether there are mismatching penalties when controlling for age cohorts and labor market experiences.
Results show that, on average, compared with workers with the same education, overeducated workers earn approximately Thus, it is concluded that there are substantial wage penalties from mismatching in Germany when several human capital variables are considered. As in Green and McIntosh , in model 3, we introduce over- and underskilling to see whether the coefficient for overeducation is reduced. However, the reason that the overeducated earn less does not seem to be that they are not using their skills and abilities to the same extent than matched workers with the same education.
On the contrary, we find that these skill mismatching variables leave the educational mismatching variables almost unaffected. Over- and undereducated workers earn approximately To see whether the over- and undereducated earn less more because they are less more able than matched workers, in model 4, we additionally include specific human capital task experience and health, two measures for usually unobserved ability differences between workers. Though both variables significantly vary with wages in the expected way, against the heterogeneous ability hypothesis, the partial wage effects from over- and undereducation do not change significantly.
Moreover, model 4 reveals that introducing additional worker-level variables, i. Model 5 includes job-related variables of the skill content of jobs, employment status, and firm size the latter two are not printed. If jobs held by the over- or undereducated would be more skill demanding than could be observed by educational level, while holding constant skill utilization, wage penalties from mismatching should be strongly reduced when we add better measures for the skills content of jobs. Model 5, which includes job-related variables, is a far superior fit, and it strongly reduces error variance at both the occupational level and the worker level.
Model 6 extends model 5 by incorporating certain occupation level variables. Thus, having a job with a higher level of interactive tasks might be devalued by being positioned in an occupation intensive in cognitive routine tasks. Second, it is noted that even with the level-2 covariates, the basic patterns in wage penalties, from educational and skill-based mismatching, are largely unaffected. This, again, fosters the assumption that, to a certain extent, mismatching is a real phenomenon in the German labor market.
This paper provides actual and precise information on the incidence rates and wage penalties of educational match and skill utilization in Germany. First, we demonstrate that, at present, as in other industrialized countries, mismatching is a relevant phenomenon in the German labor market. As in other countries, in Germany, the incidence of overqualification Among overqualified workers, the largest group is overeducated workers Equally, the incidence of under-qualification is highest from a credential perspective approximately 8.
With respect to all mismatching categories, having education and skills below the required level, is the most uncommon mismatching phenomenon in Germany 0. Thus, workers in Germany can be over- or underqualified in terms of formal qualifications, even though their skills or abilities are perceived to be appropriate for the jobs for which they have been hired. Furthermore, there are workers who are perfectly matched with respect to their education, yet they feel under- or overchallenged by the skills requirements of their jobs. Random intercept models with worker, job, and occupation characteristics reveal that beyond worker characteristics, i.
Our results suggest that, to some extent, mismatching is a real phenomenon in the German labor market. Overall, this paper stresses the merits of the job requirement approach. With respect to the study of skill demand in general, research on mismatching greatly benefits from subjective measures on the skill content of jobs.
Based on the results of job tasks, it would be worthwhile to study the link between polarization and mismatching using over time data.
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Ideally, however, to test for the occupational mobility hypothesis and to separate cohort from age effects, our analyses should be validated with panel data. A further extension would be to distinguish between subjects studied, testing whether overeducated workers are equally as able as matched workers, though they have degrees in subjects that are in less demand in the labor market e.
The analyses could be further extended by including specific occupation-level variables, e. Im Hinblick auf a zeigt sich, dass der Zusammenhang zwischen beiden Passungsvariablen eher gering ist, d. Unfortunately, the in-depth matching information we are using is not available on a panel basis.
In general, we see that an important contribution to the literature would be to study individual change e. Schneider In general, the use of objective measures, such as occupational classifications as realized matches by mean educational level and expert ratings of occupational skill requirements, suffers from conflating supply and demand, or not capturing within-occupational heterogeneity, being less actual and possibly also less valid, e.
McGuinness and Hartog provide good overviews of advantages and drawbacks of different measures. Across studies in their meta-analysis, Groot and Maassen van den Brink find an average of For a review of existing studies, also see Hartog and Quintini Reviewing available cross-national evidence, Hartog , p. The authors report even negative values for overeducation and positive values for undereducation.
However, we do not consider these implausible values in this study. There are also some certificates for technicians that might be awarded through vocational education. On the other hand, there are a number of surveying positions held by formally underqualified employees. It is also the case that some academically trained employees do not realize that vocational education was required for their jobs, albeit their academic qualification was the requirement. In these cases, the required vocational education was adjusted accordingly.
Virtual years of education were computed by adding up the time it usually takes to achieve a certain degree.
In sum, you would get a score of 16 years for virtual education. Some respondents have accomplished, or at least started, more than one vocational education or training program. For some of these cases, the time of the first training episode had to be recomputed. In contrast to former waves and thus to the procedures in Spitz-Oener , it is possible to weight the number of activities by whether they are performed frequently with a weight of 1 , sometimes with a weight of 0.
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We are aware of the possible problems due to reporting errors of self-employed or free-lancing workers on their income. We do measure gross earnings and find that self-employed and free-lancers show the highest mean income with the highest standard deviation. The imputation of missing cases in gross earnings is probably responsible for the high deviation. Self-employed and free-lancers are still included in the sample, though they will likely have distinct task-profiles.
We use imputed values for cases with missing information on the original self-reported gross monthly earnings variable Alda and Rohrbach-Schmidt In the economic literature, multilevel models are also known as random coefficient models or mixed models. Bivariate statistics for mismatching by socio-demographic variables and several human capital variables as well as by job characteristics can be requested from the authors.
The ICC for undereducation but not for the other mismatching categories is much lower when those without a vocational qualification are considered. Instead, for female employees aged 35—44 have a 3. Possibly this signals processes of re-entering the labor market after family related breaks. We thank Michael Handel, Francis Green and two anonymous reviewers for helpful discussion and comments.
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author s and the source are credited. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Educational Mis match and skill utilization in Germany: Assessing the role of worker and job characteristics.
Open Access. First Online: 17 February Table 1 Descriptive statistics. Mean Std. To test for the assumption that above worker characteristics job heterogeneity has a role in explaining a mismatch, we run a set of multivariate models of mismatching and wages where we explicitly control for variance at the occupation level. First, with mis matching as the dependent variable, we regress the probability of being mis matched on worker and job variables simultaneously within a random intercept logistic regression model with workers nested in occupations.
In the same vein, in the second part, we calculate wage penalties from mismatching in a random intercept linear model for workers within occupations. Table 2 Educational and skills mis match. Matching category Frequency Percent Formal match Undereducated Table 3 Two-way cross-tabulation of educational and skill mis match.
Open image in new window. Table 4 Variance composition of match categories in intercept-only models.
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