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Where are the returns to lifelong learning?

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Abstract

Participation in formal education during adulthood (ages 25–54) is a key part of lifelong learning. Employing unique longitudinal data for Australia, we highlight the prevalence of such study, the varied reasons for undertaking it (consumption, career development, job and home disruption), and investigate whether it is socially valuable. Our more detailed estimates of the labour market return to adult education (wage rates, employment, hours of work and occupational status) confirm previous studies that generally found such returns to be small and isolated. We contribute to this literature by also estimating the effect of adult education on job satisfaction and satisfaction with employment opportunities. Increases in satisfaction help rationalise the education enrolment decisions of these adults.

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Notes

  1. We focus on formal education and training in this article, i.e. study that can lead to the receipt of a formal qualification such as a degree, diploma or certificate. Non-formal training and learning within and outside the workplace are also important components of continual skill formation, but are not analysed here.

  2. The analysis reported in this article is an extension and refinement of Coelli et al. (2012).

  3. Note that Buchler et al. (2014) and Chesters (2015) report significant positive effects of mature age education on occupational prestige.

  4. VET qualifications include sub-Bachelor’s certificates and diplomas in the Australian system.

  5. Enrolment rates using all HILDA responders in 2014 are provided in Appendix Table B1.

  6. See Appendix Table 5 for a description of the different educational levels in Australia.

  7. Certificates I and II are generally considered to be a lower level of education attainment than completion of year 12 of high school in Australia.

  8. These log wage regressions included measures of work experience and its square, indicators of highest education level, interactions of work experience and education level, the proportion of time since leaving full-time education not working, immigrant status, marital status, number of children, disability status, and state of residence.

  9. Job satisfaction is reported on a scale from 0 to 10. We grouped these reports into four categories.

  10. Such strategies have been employed in several recent related studies such as Blanden et al. (2010) for the UK, Zhang and Palameta (2006) for Canada, plus Jepsen et al. (2014) in the US, in addition to several studies listed in the Sect. 1.

  11. Only coefficients of variables that change over time (marital status, state of residence, disability status and age) are identified once individual fixed effects are included. We include interactions of age and age squared with initial education level indicators to allow lifecycle profiles to differ by education. For estimates of log hourly wages, we also include a quartic in years of work experience.

  12. Parent’s approach heavily draws on the strategy developed by Altonji and Shakotko (1987) to investigate the relation between wages and job seniority.

  13. While Parent (1999) employed a generalised least squares estimation technique that allows for correlation of residuals over time within individuals, we implement the IV strategy maintaining the inclusion of individual fixed effects to be consistent with our other estimates.

  14. These estimates were constructed after excluding a small number of individuals that had more than one education spell during the period. Including these multiple education spell individuals did not materially change the estimates, but did lower their precision slightly.

  15. Estimates for hourly wage rates use the subset of individuals who have positive labour market earnings: zero earnings observations were treated as missing during those specific periods only. Estimates in Fig. 1a suggested that employment changes were not large from before to after education. Such a restriction should thus not result in sample selection bias that is particularly troubling.

  16. Note that these estimates only include individuals with positive work hours during the wave (an unbalanced panel).

  17. There are at least four specification differences between the two studies that are likely to result in the estimates of Buchler et al. (2014) being more positive than those in Coelli et al. (2012) and in this study. Buchler et al. (2014) do not appear to include individual wave (time) indicators, do not differentiate years of study from years prior to study, do not exclude individuals already studying in wave 1, and do not include interacted education level by age variables as covariates.

  18. Job satisfaction is only measured for employed individuals. It is measured on a scale from zero (totally dissatisfied) to ten (totally satisfied). For simplicity, we model job satisfaction using linear regression.

  19. Using SEM and Spanish cross-sectional data, Fabra and Camison (2009) confirm previous findings in the related literature by reporting a negative direct relationship between education level (not focusing on adult education) and job satisfaction, but a more than offsetting indirect effect via higher wages and better job characteristics.

  20. Satisfaction with employment opportunities is measured irrespective of current employment status. It is also measured on a scale from zero (totally dissatisfied) to ten (totally satisfied).

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Acknowledgements

We are indebted to Rezida Zakirova for excellent research assistance. We are grateful to the Editor and two anonymous referees for their very useful suggestions on an earlier version of the paper. We thank Jeff Borland and Moshe Justman for useful comments, along with seminar participants at ESPE 2013 and the University of Melbourne. Central to the analysis are unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Survey is funded by the Australian Government Department of Social Services (DSS) and managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). Any interpretation of data is the responsibility of the authors.

Funding

This work was supported by the Australian Government National Centre for Vocational Education Research (NCVER) under the National VET Research and Evaluation Program 2011–2013 with the MIAESR. The views expressed in this report are those of the authors and do not represent those of the MIAESR or NCVER.

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Authors and Affiliations

  1. Department of Economics, University of Melbourne, Parkville, Australia

    Michael Coelli

  2. Department of Economics, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Domenico Tabasso

  3. IZA, Bonn, Germany

    Domenico Tabasso

Authors
  1. Michael Coelli
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  2. Domenico Tabasso
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Correspondence to Domenico Tabasso.

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Supplementary material 1 (DOCX 25 kb)

Appendix A

Appendix A

See Tables 5, 6, 7 and 8.

Table 5 Details of Australian education levels
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Table 6 Marginal effects on engaging in education, 2002–2014—males
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Table 7 Marginal effects on engaging in education, 2002–2014—females
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Table 8 Main reason for studying for a formal qualification in previous 12 months
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Coelli, M., Tabasso, D. Where are the returns to lifelong learning?. Empir Econ 57, 205–237 (2019). https://doi.org/10.1007/s00181-018-1433-8

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