A Task-Based Indicator for Labour Market Mismatch

Abstract

This article uses a task-based approach to measure labour market mismatch and to study the wage effects of mismatch. We propose a new indicator—cognitive mismatch—and contrast it with the commonly used overqualification indicator. We argue that considering the tasks performed in each occupation captures more adequately the complexity of job requirements and tackles the major drawbacks of the existing approaches measuring mismatch. Using rich administrative data from the German Sample of Integrated Labour Market Biographies, we find that 4.8% of the total employment episodes are matched in terms of qualification but mismatched in terms of the tasks performed. Fixed Effects models show that the largest wage loss occurs in cases of mismatch in both education level and tasks performed at work. We conclude that each indicator is capturing different facets of mismatch and that they should be used complementarily in labour market mismatch analyses.

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Fig. 1

Source: SIAB, 2003–2014, own computations

Notes

  1. 1.

    The risk of mismatch varies for instance with social background, gender, migration status or region (Erdsiek 2016; Kracke 2016; Prokic-Breuer and McManus 2016; Ramos and Sanromá 2013; Rafferty 2012).

  2. 2.

    With data from the Brazilian Census, Reis (2018) come to similar conclusions for horizontal mismatch. Among horizontally mismatched workers, earnings decrease with the distance between required occupational skills and the field of study.

  3. 3.

    Employing for instance data from Programme for International Assessment of Adult Competencies (PIAAC) and its predecessors.

  4. 4.

    Even if overqualification falls short of capturing skills mismatch it is nevertheless an incident of mismatch, leading to negative labour market outcomes like lower wages (cf. Kracke et al. 2017).

  5. 5.

    For instance, in an extreme case, consider individuals that were trained and work in the same occupation. While the mismatch indicator based on education would always consider them as matched, the task-based indicator acknowledges that it could be mismatched if the task composition has changed significantly.

  6. 6.

    The main entry from the education system into the labour market is channelled by the vocational education and training (VET) system (Solga et al. 2014), so it is relevant to study the mismatch phenomenon of this group of graduates. A priori, the phenomenon of labour market mismatch is expected to be a minor topic for these graduates as this system is half way between theoretical learning and on the job learning (Blossfeld 1992), provides occupation-specific skills that are of immediate need in the labour market and has a high level of standardisation (Allmendinger 1989). Accordingly, it is associated with a quick and smooth transition into the labour market (Konietzka 2008; Müller and Gangl 2003; Scherer 2005). While indeed it is well known in the literature that graduates from the dual system of vocational training have a lower risk of being mismatched employed compared to university graduates (Rukwid 2012), they are still affected by the phenomenon (Pollmann-Schult and Mayer 2004; Solga and Konietzka 2000; Béduwé and Giret 2011).

  7. 7.

    The dataset on occupations’ task composition is available for 2011, 2012 and 2013 but the data for each occupation barely changes across these years. We assume that the tasks have been fairly stable before 2010 at least at the level of aggregation considered here (cognitive and manual). In order to have a sufficient sample of graduates who can be examined up to 5 years after graduation we decided to keep in the sample graduates from 2003 onwards. To check the stability of our results, we recalculated our multivariate models only for the years 2011, 2012 and 2013 and found similar results. Hence, our results and conclusions are not driven by the decision to include data previous to 2011.

  8. 8.

    This “expert knowledge” corresponds to BERUFENET, a free online information platform for all occupations in Germany. It is arranged and constantly maintained by the German Federal Employment Agency and is mainly used for career guidance and job placement. Currently, BERUFENET describes approximately 3,900 occupations (at the most detailed level of occupations—8-digit code) and provides a rich set of occupational information, such as the required qualifications, certificates and licenses, requirements in an occupational activity, equipment used, working conditions, potential specialisations and further training (Dengler et al. 2016).

  9. 9.

    See Dengler et al. (2014) for more details.

  10. 10.

    The decision to merge the five types of tasks into two aimed to simplify the analysis. Keeping the five types of tasks would require a subjective hierarchization in order to define upward and downward mismatch.

  11. 11.

    As a robustness check, we recalculated all models excluding those occupations that have a very similar percentage of cognitive and non-cognitive tasks. The results did not change substantially.

  12. 12.

    The school leaving degree is a dummy variable that equals one if individuals have a high school graduation level, which serves as university entrance diploma in Germany (Abitur). The social background information is captured by whether the individual's household received social benefits (Hartz IV) prior to or during the apprenticeship (cf. Dietrich et al. 2016).

  13. 13.

    All continuous variables are centered on their sample mean.

  14. 14.

    The share of individuals doing these changes is about 20%, and therefore not negligible.

  15. 15.

    Table 6 in the appendix shows the percentage of cognitive mismatch and overqualified workers by occupational segment.

  16. 16.

    We carried out several robustness checks of our results. We re-estimated the models considering different gaps between the end of apprenticeship and starting the first job. We excluded from the sample individuals with implausible wage changes during apprenticeship. Our conclusions are not substantially different in any of these robustness checks. The results are available upon request.

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Acknowledgements

The authors would like to thank the valuable comments and suggestions from Brita Matthes and Malte Reichelt and from the participants: of the “Fourth Lisbon Research Workshop on Economics, Statistics and Econometrics of Education”; of the session “Recognition of Education and Skills at the Labour Market” at the “13th Conference of the European Sociological Association” and of the “Second LEER Conference on Education Economics”.

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Appendix

Appendix

See Tables 6 and 7.

Table 6 Share of cognitive mismatch and overqualification by occupational segments.
Table 7 Estimated effect of mismatch on net daily wage (full model).

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Kracke, N., Rodrigues, M. A Task-Based Indicator for Labour Market Mismatch. Soc Indic Res 149, 399–421 (2020). https://doi.org/10.1007/s11205-019-02261-2

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Keywords

  • Occupational skills
  • Overqualification
  • Mismatch
  • Tasks

JEL Classification

  • I26
  • J24
  • J31