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Labor Mismatches: Effects on Wages and on Job Satisfaction in 17 OECD Countries

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Abstract

This study analyzes the effects of labor mismatches on wages and on job satisfaction in seventeen OECD countries by distinguishing between educational mismatch and skills mismatch. Using data from PIAAC, the results suggest that whereas educational mismatch shows greater effects on wages, the effects of labor mismatch on job satisfaction are generally better explained by skills mismatches. Both phenomena appear to be relevant for understanding the economic effects of labor mismatch and suggest that educational mismatch is not an accurate proxy for skills mismatch, mainly when the non-monetary effects of labor mismatch are addressed.

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Notes

  1. Although the interest of this study focuses on developed countries, the relevance of educational investments is at a crucial juncture in developing countries. Interested readers on some of the key problems faced by developing countries as regards education and labor policies can see the works by Muysken and Nour (2006), Boccanfuso et al. (2015) or Kruss et al (2015), focused on the quality of education and skills formation, or the works by Amin (2009), Fakih and Ghazalian (2015), or Brixiova and Égert (2017), with a greater focus on labor market constraints, labor regulations and institutions.

  2. Previous OECD programs to assess the skills of the adult population were IALS (1994–1998) and ALLS (2003–2006). The PIAAC survey started in 2013 and continues nowadays, thus providing the most recent data in the assessment of the skills of the adult population in OECD countries.

  3. The Jackknife procedure allows to take account of weights included in the PIAAC database for each individual in the sample and its 80 replications when standard errors are estimated. See OECD (2013) for a detailed description of this procedure.

  4. Hoogerheide et al. (2012) show that “using the father’s education as an instrument in an income regression is a viable option for solving the endogeneity problem with regard to education”.

  5. All technical information and access to the PIAAC database can be found in http://www.oecd.org/skills/piaac/publicdataandanalysis/. Data from Germany come from Rammstedt et al. (2015).

  6. The empirical analysis is run for the pool of countries and then grouping these countries in more homogeneous blocs: Nordic (Denmark, Finland and Norway), Mediterranean (Italy and Spain), Continental (Belgium, France, Germany and Netherlands), Eastern (Czech Republic, Estonia, Poland, Slovak Republic), English-speaking (Ireland and United Kingdom) and Asian (Japan and Korea) blocs.

  7. The use of a dichotomous variable is a very common procedure followed in the literature on job satisfaction (see, for example, Allen and van der Velden (2001) or Mavromaras et al. (2013)). However, one could alternatively take account of all categories of job satisfaction and run an ordered probit. This alternative has also been followed and the obtained estimates are presented in the “Appendix” section as a robustness check.

  8. A detailed discussion of the advantages and disadvantages of each method can be found in Hartog (2000), who concludes that the use of one or another estimation method does not affect the empirical results, so the choice of the method will depend on available information.

  9. With some differences in magnitude, the results for the pool of countries hold for all country blocs being considered. The distributions of educational and skill mismatches for the different blocs of countries are presented in “Appendix” section (Table 7).

  10. Estimates were also run with literacy scores, obtaining quantitatively and qualitatively similar results (estimations are available upon request). In any case, all 10 plausible values are included to run the estimates.

  11. Estimates for educational and skills mismatch have also been run separately for the different blocs of countries. Nevertheless, since the pattern found for the different blocs is similar to that of the pool of countries, for the sake of space, we only present the estimates for the relevant variables when both types of mismatch are jointly considered. All complete estimates are available upon request.

  12. As shown by Halvorsen and Palmquist (1980), the interpretation of estimated coefficients for dummy variables in semilogarithmic equations is given by exp(coefficient)-1. We thank an anonymous referee for pointing out this question.

  13. The coefficients of the numeracy skill are hardly to interpret in a direct way. However, since we are estimating a semi-logarithmic model, the elasticity on the average can easily be calculated by multiplying the estimated coefficient by the mean value of the variable.

  14. Since the estimates for these control variables are as expected according to previous literature, and consistent when skills mismatch is considered, we will not extend on these results hereafter.

  15. Talking about labor market flexibility (or rigidity) encompasses different labor market outcomes (e.g. labor adjustment, wages, mobility, labor costs, or unemployment, among others). However, in terms of wage adjustments, the Nordic countries seem to show greater rigidities (Andersen et al. 2014), as so do Asian (Hwang 2006) and Mediterranean countries (Chenic 2013), whereas Central and Eastern European countries (Svejnar 2002) and the Anglo-Saxon model (Aceleanu 2012) show greater wage flexibility.

  16. Complete estimates from the probit model are presented in “Appendix” section (Table 8). In addition, the “Appendix” section (Table 9) also offers the estimates of the ordered probit as a check of robustness for the pool of countries. All these estimates are also available for the different blocs of countries upon request.

  17. We do not extend here in the comment of other control variables since the results are as expected according to previous literature, with a positive and significant effect on the probability of being satisfied for those earning higher wages, those enjoying better health, females, employees with greater experience and those working in the public sector and doing supervisory tasks.

  18. In fact, several studies suggest that workers in East European and Asian countries attach greater importance to external aspects of the job instead of looking at intrinsic job characteristics (Huang and Van de Vliert 2003; Borooah 2009). For a deeper insight on the cultural and socio-economic characteristics explaining cross-national differences in job satisfaction, see for example Sousa-Poza and Sousa-Poza (2000) or Lok and Crawford (2004).

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Acknowledgements

The authors acknowledge support from the Spanish Ministry of Economy and Competitiveness (Research Project ECO2014-53702-P). Lucía Mateos-Romero also acknowledges support from the Spanish Ministry of Education for a FPU Program Grant.

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Authors and Affiliations

  1. Departmento de Economía, Facultad de CC. Económicas, Universidad de Extremadura, Avda. de Elvas s/n, 06006, Badajoz, Spain

    Lucía Mateos-Romero & María del Mar Salinas-Jiménez

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  1. Lucía Mateos-Romero
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  2. María del Mar Salinas-Jiménez
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Correspondence to María del Mar Salinas-Jiménez.

Appendix

Appendix

See Tables 7, 8 and 9.

Table 7 Educational mismatch and skills mismatch by country blocs.
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Table 8 Job satisfaction and educational and skills mismatches.
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Table 9 Job satisfaction and educational and skills mismatches.
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Mateos-Romero, L., Salinas-Jiménez, M.M. Labor Mismatches: Effects on Wages and on Job Satisfaction in 17 OECD Countries. Soc Indic Res 140, 369–391 (2018). https://doi.org/10.1007/s11205-017-1830-y

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