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Demand and Supply of Skilled Labour and Overeducation in Europe: A Country-level Analysis

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Abstract

This paper analyses the incidence of overeducation of university graduates. To this end, we estimate a random effects model for a panel of European countries. Our results do not confirm that the increase of the supply of qualified labour per se can be seen as a relevant factor fuelling overeducation. The relative wage of university graduates is inversely related to overeducation. This finding suggests a role for the demand for qualified labour. Cyclical conditions also matter, as overeducation operates as a short-term adjustment mechanism. This result sheds new light on the possible effects of the recession currently hitting the industrialized countries and on policy measures needed to foster economic recovery.

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Notes

  1. In the literature on overeducation based on individual-level data, a number of explanations of overeducation have been proposed. One of these maintains that it can derive from substitution between human capital acquired through formal learning at school and skills learnt through experience in the labour market (Sicherman, 1991) or from a poor quality of schooling (Ordine and Rose, 2009). In an analogous way, overeducation can be associated with lower individual ability (McGuinness, 2006). Otherwise, it can be caused by imperfect matching in the labour market due to a wrong distribution of educated workers by field of study, to spatial mismatch or to other imperfections. For a survey, see also Leuven and Oosterbeek (2011) and Quintini (2011b).

  2. Correspondences between certificate of education and years of schooling are reported in Table 1.

  3. Austria, Belgium, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, Greece, Hungary, Ireland, Iceland, Italy, Lithuania, Luxemburg, Latvia, Netherlands, Norway, Poland, Portugal, Romania, Sweden, Slovenia, Slovakia, the United Kingdom.

  4. For thorough discussions of the different methods, see Borghans and de Grip, 2000; Hartog, 2000; McGuinness, 2006; Leuven and Oosterbeek, 2011.

  5. In Quintini (2011a), the share of overeducated employees are derived from data collected by the European Survey of Working Condition (2005), by applying the statistical method based on the modal qualification in each occupation.

  6. The Spearman test proves that the probability of independence between the ranks of the two distributions is equal to 0.6%.

  7. We use the percentage of individuals aged between 25 and 64 with tertiary educational attainment in European countries provided by Eurostat.

  8. In our definition, emphm corresponds to the ratio of the number of employees with tertiary degree to the number of employees with upper secondary degree (15–74 years).

  9. Indeed, if the percentage of upper secondary graduates (in the population and in each occupation) is higher, also the percentage of overeducated tertiary graduates can be expected to be higher, just as a consequence of the spurious effect stemming from the definition of overeducation that we have adopted. We can exclude multicollinearity between poplau and popdip, as the correlation between them is only −0.2515 and not statistically significant.

  10. ISCO 88 is coded on one digit as follows: (1) Legislators, Senior Officials and Managers; (2) Professionals; (3) Technicians and Associate Professionals.

  11. The output gap is derived from the OECD Economic Outlook 2010 (issue 2) and is defined as the deviation of actual GDP from potential GDP as a percent of potential GDP.

  12. More formal tests on the quality and validity of the instrument are reported in Appendix A.

  13. Actually, firms would be prone to hire overeducated workers when returns to overeducation are low (Di Pietro and Urwin, 2006), besides the contractual arrangement. The possibility of hiring temporary workers could strengthen this behaviour.

  14. Similarly, Dew-Becker and Gordon (2008) find that labour market reforms in Europe favoured a rise of employment of less experienced, less skilled components of the labour force and argue that this contributed to the productivity slowdown.

  15. The unweighted average rate of long-term unemployment at the end of the period was equal to 3.24% compared with 4.13% in 1998.

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Authors and Affiliations

  1. Department of Economics and Law, Sapienza University of Rome, Via del Castro Laurenziano, 9, Rome, 00161, Italy

    Giuseppe Croce & Emanuela Ghignoni

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  1. Giuseppe Croce
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Appendix

Appendix

Tests on the quality and the validity of the instrument

As pointed out in preceding sections, the estimation of a model with instrumental variable needs at least one instrument that affects the wage ratio and not the incidence of overeducation. In this section, we test the quality and the validity of the instrumental variable we use in this paper (union coverage).

Instrumental quality is ensured if there is a strong correlation between the instrument and the wage ratio. A statistic commonly used in order to test this condition (Bound et al., 1995) is the R2 of the first stage regression with the included instrument ‘partialled-out’, or Shea partial R2 (for an application to the analysis of overeducation, see Di Pietro and Cutillo, 2006). In our first stage regressions, the partial R2 on the excluded instrument range from 0.47 to 0.63, suggesting that the instrument make a relevant contribution in explaining the wage ratio (see Table A1).

Table A1 Tests on the quality and the validity of the instrument
Full size table

Instrumental validity is ensured if the instrument can be legitimately excluded from the overeducation equation. This assumption is often checked through the Sargan test. Nevertheless, this test is valid only in case of over-identification (ie, the number of valid instruments exceeds the number of endogenous variables), which is not our case. Following the suggestion of Cutillo and Ceccarelli (2010), we checked the validity of the instrument through the approach of Dolton and Vignoles (2002). According to these authors, a valid instrument must be uncorrelated with the error term of the outcome equation, and thus it should not affect the incidence of overeducation conditional on the included explanatory variables. When the residuals from the overeducation equations were regressed on the instrument, we obtained R2 ranging from 0.0002 to 0.0041 (see again Table A1). This indicates that the instrument does not explain any significant variation in the residual variability and hence is valid.

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Croce, G., Ghignoni, E. Demand and Supply of Skilled Labour and Overeducation in Europe: A Country-level Analysis. Comp Econ Stud 54, 413–439 (2012). https://doi.org/10.1057/ces.2012.12

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