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Student work during secondary education, educational achievement, and later employment: a dynamic approach

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Abstract

This study examines the direct and indirect impact (via educational achievement) of student work during secondary education on later employment outcomes. To this end, we jointly model student work and later schooling and employment outcomes as discrete choices, while correcting for these outcomes’ unobserved determinants. Using unique longitudinal Belgian data, we find that pupils who work during the summer holidays are more likely to be employed three months after leaving school. This premium to student work in secondary education is higher when pupils also work during the school year. Decomposing this total effect shows that the direct return to student work during secondary education overcompensates its non-positive indirect effect via educational achievement. This effect is also found to decline over time, with the premium to a combination of work during the summer and the school year becoming statistically insignificant five years after graduation.

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Data availability

Our analysis is based on the SONAR-data. This data collection was sponsored by the Flemish Government in the framework of the Flemish Policy Research Centre on Study and School Careers (www.steunpuntssl.be). To get access to the data, external users have to send a motivated request to the Policy Research Centre on Educational Research (sono@ugent.be), which is the successor of the Policy Research Centre on Study and School Careers. A similar procedure can be used when researchers would like to replicate the results in the paper.

Notes

  1. For instance, Kalenkoski and Pabilonia (2012) show that employment during the school year decreases the time that secondary school students spend on homework.

  2. Most previous studies examining the causal impact of working while studying have examined its impact on educational outcomes (Eckstein and Wolpin 1993; Stinebrickner & Stinebrickner 2003) or focussed on the effect of student work during tertiary education (Geel and Backes-Gellner 2002; Stinebrickner and Stinebrickner 2003; Häkkinen 2006; Joensen 2009; Passaretta and Triventi 2015). However, as the effect of working while studying on educational outcomes differs from its effect on labour market outcomes and as the effect of student work during tertiary education differs from the effect of student work during secondary education (Neyt et al. 2018), these studies are unable to answer the research questions we pose in this study.

  3. Because they only focus on a very specific labour market outcome, i.e. job interview invitations, Baert et al. (2016) are not included in this table. Scott-Clayton and Minaya (2016) are not included too because they focus on the effect of the subsidised Federal Work-Study programme on individual outcomes rather than on the effect of student work per se. In addition, we are aware of some recent discussion papers by economists, to which we also refer in Sect. 2 (Peng and Yang 2008; Alam et al. 2013). Finally, the association between working while studying and later employment outcomes has also been studied in other fields, like sociology (Carr et al. 1996; Brody 1996; Marsh and Kleitman 2005).

  4. This importance of focusing on employment chances is, among others, witnessed by the substantial gap in employment rates between high and low skilled. For instance, in 2019 in Belgium, these rates were 83.8 and 46.3%, respectively (Eurostat 2020).

  5. Similarly, for studies outside the field of economics, Carr et al. (1996) and Marsh and Kleitman (2005) solely control for observable differences between working and non-working students, such as students’ background characteristics, earlier educational performance, and educational aspirations.

  6. Alam et al. (2013) use variation in the allocation of public summer jobs as an instrument.

  7. Instrumental variable estimation techniques only isolate a local average treatment effect (LATE), i.e. they only capture the effect of student work for pupils who are affected by the chosen instrument (Angrist et al. 2000).

  8. Analyses in which these individuals were retained yielded the same conclusions as those mentioned in Sect. 5.5. The results of the additional analyses are available on request.

  9. Due to physical and/or mental disability, serious behavioural and/or emotional problems, or serious learning difficulties.

  10. As a—favourable—consequence of this choice, we were able to model student work during secondary education by means of an ordered logit specification (see below). As a robustness check, we included these pupils in the option of working during the summer and the school year. This hardly changed the estimation and simulation results.

  11. But Plug (2004) fills a gap in the literature by using a sample of adoptees to show that the impact of the schooling of the mother on the schooling of the child disappears when taking into account assortative mating and heritable abilities.

  12. Nonetheless, in case one were willing to rely on more strict assumptions of utility maximising behaviour, one could interpret our model as being a reduced-form version of a structural model in the spirit of Eckstein and Wolpin (1999). In such a model, students trade off the time spent on various activities (working, studying, leisure) while taking into account both their consumption values and their effects on human capital accumulation (and, therefore, also future labour market outcomes).

  13. The terms ‘choices’ and ‘outcomes’ are sometimes used interchangeably in the dynamic discrete choice literature. We prefer to use the latter term as our model is a reduced-form model that does not allow one to differentiate between outcomes that result from explicit (and unconstrained) choices and other outcomes (see also Belzil and Poinas, 2010).

  14. We also estimated our model with this outcome specified as multinomial logit instead of ordered logit. This did not change the research conclusions.

  15. The coefficients on these variables, which are not the focus of our study, are thus not to be interpreted causally.

  16. This is a similar assumption as in the case of matching estimators, except that our model also conditions on unobservables that affect both student employment and the outcomes (cf. Heckman and Navarro, 2007; Heckman, Humphries, and Veramendi, 2016). As an example that contributes to this residual variation, one could, for instance, think of an accidental offer to participate in a job while being in high school.

  17. Following the argument in Gaure et al. (2007), we believe that the AIC is the preferable criterion for our sample size.

  18. For instance, following the equation for \({p}_{k}(q)\), p2 = exp(− 1.174)/(exp(0) + exp(− 1.174) + exp(− 2.618)).

  19. For instance, the total effect of student work during the summer on employment three months after leaving school is affected by (i) coefficient 0.380 in Panel E of Appendix Table 5 (direct effect) as well as by (ii) the interplay between coefficient 0.309 in Panel E and coefficient 0.123 in Panel C (indirect effect via secondary education graduation) and (iii) the interplay between coefficient 0.191 in Panel E and coefficient − 0.147 in Panel D (indirect effect via tertiary education enrolment).

  20. Results do not substantially differ when estimating ATTs or ATNTs (see Table 7 in Appendix D).

  21.  − 16.9% = 0.831 − 1.

  22. According to the coefficients in Panels C and E of Table 5 in Appendix B, secondary education graduation is (i) insignificantly positively associated with student work only during the summer and (ii) significantly positively associated with employment three months after leaving school. In addition, following the coefficients in Panels D and E of Table 5 in Appendix B, tertiary education enrolment is (i) insignificantly negatively associated with student work only during the summer and (ii) insignificantly positively associated with employment three months after leaving school.

  23. This negative effect is driven by the negative association between this form of student work and the schooling outcomes (Panels C and D of Table 5 in Appendix B) and these schooling outcomes’ positive association with employment three months after leaving school (Panel E of Appendix B, Table 5).

  24. However, Baert et al. (2016) provided some suggestive evidence in this respect. They measured the effect of student work on résumés on employers’ hiring decisions, i.e. an effect that could only be driven by employer side preferences and perceptions. As these authors did not find a significant treatment effect, their results suggest that the employee side mechanisms (human capital and social capital) are crucial.

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Acknowledgements

We thank Lorenzo Navarini for his excellent research assistance. We also thank Koen Declercq, Olivier De Groote, and the participants in the Leuven Economics of Education Research (LEER) Workshop, the 29th conference of the European Association of Labour Economists (EALE), and the 25th annual workshop of the European Research Network on Transitions in Youth (TIY) for their insightful comments and suggestions, which have helped to improve this study considerably. Data collection in the framework of the SONAR research programme was financed by the Flemish Government

Funding

Data collection in the framework of the SONAR research programme was financed by the Flemish Government. Vlaamse regering, SONAR Research Program

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Authors and Affiliations

  1. Ghent University, Ghent, Belgium

    By Stijn Baert, Brecht Neyt, Eddy Omey & Dieter Verhaest

  2. KU Leuven, Brussels, Belgium

    Dieter Verhaest

  3. University of Antwerp, Antwerp, Belgium

    By Stijn Baert

  4. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    By Stijn Baert

  5. IZA, Bonn, Germany

    By Stijn Baert

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  1. By Stijn Baert
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  2. Brecht Neyt
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Correspondence to Dieter Verhaest.

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Appendices

Appendix A

See Table 4.

Table 4 Model selection
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Appendix B

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Table 5 Full estimation results
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5.

Appendix C

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Table 6 Goodness of fit
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6.

Appendix D

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Table 7 ATTs versus ATNTs
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7.

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Baert, B.S., Neyt, B., Omey, E. et al. Student work during secondary education, educational achievement, and later employment: a dynamic approach. Empir Econ 63, 1605–1635 (2022). https://doi.org/10.1007/s00181-021-02172-7

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