Can adult education delay retirement from the labour market?

Abstract

We examine whether adult education delays retirement to potentially increase labour force participation among the elderly, a mechanism suggested in the OECD strategy for “active ageing” and the “Lisbon strategy” of the EU. Using register data from Sweden, we analyse transcripts from adult education for the period 1979–2004 and annual earnings 1982–2004. We match samples of treated individuals, in adult education 1986–1989, and untreated on the propensity score. The timing of exit from the workforce is assessed by non-parametric estimation of survival rates in the labour force. The results indicate no effects of adult education on the timing of retirement.

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Notes

  1. 1.

    E.g., interest rate is below market price and required repayments never exceed 4% of earnings.

  2. 2.

    Mixed results presented in Albrecht et al. (2004) and Ekström (2003) are reconciled in Stenberg (2009).

  3. 3.

    In each period, an individual is assumed to compare the expected utility from immediate retirement with the utility from retiring at a future date. Workers remain in the labour force as long as the option value of work (alternately referred to as the option value of delayed retirement) is positive.

  4. 4.

    However, Bartel and Sicherman (1993) find that workers in industries with high rates of technological change retire later to recoup the returns on late investments in on-the-job-training; nevertheless, a technological shock would (ceteris paribus) induce workers to retire sooner because of increased costs for re-training.

  5. 5.

    On-the-job-training typically involves high-skilled individuals (Brunello 2003; Arulampalam et al. 2004).

  6. 6.

    The policy until the mid-1990s was that in order not to generate perverse incentives, formal education was not offered as an active labour market program.

  7. 7.

    Migration to another regional labour market is a very uncommon event in these age cohorts (Eliasson and Westerlund 2009).

  8. 8.

    The average among untreated individuals is 8.5 credits. In the empirical section, the mean among matched comparisons never exceeds 20 credits (i.e. about 10 days).

  9. 9.

    p it is the sum of part-time pensions, retirement (i.e. old-age) pensions, national supplementary pensions (ATP), and early-retirement pensions, including sickness pensions and various occupational pensions.

  10. 10.

    Until 2004, the last year of observation, the proportion of censored individuals was below 4.5%.

  11. 11.

    The macroeconomic background made the probability of retirement higher during the economic downturn at the beginning of the 1990s, but note that the regime under which individuals retire is the same for the treated and the untreated at different points in time.

  12. 12.

    Blöndahl and Scarpetta (1999) estimate the average age of transition to inactivity in 1995. For Sweden, this was found to be 63 for males and 62 for females. The corresponding figures for the US were 64 and 62 years of age, and for Germany, they were 61 for males and 58 for females. Labour force participation and employment rates among 55–64 year olds are higher in Sweden than the OECD average. See also Eklöf and Hallberg (2004, 2010) and Karlström et al. (2004).

  13. 13.

    We match on the propensity score, i.e. the probability of entering AE conditional on the covariates; see Rosenbaum and Rubin (1983) for a theoretical justification. Matching all covariates may be difficult due to “the curse of dimensionality.” That is, finding close matches on many dimensions is not possible.

  14. 14.

    The stable unit treatment value assumption (SUTVA) is standard in the program evaluation literature. In a theoretical contribution, Albrecht et al. (2009) show that a large AE sector is detrimental for those who remain low skilled, but that general equilibrium effects and externalities would enhance economic growth in general. Assumption (A) would in that case be violated and the returns to AE overstated.

  15. 15.

    Covariates are here variables assumed not to be affected by treatment and typically measured before treatment. Conditioning the analysis on variables which are affected by treatment will bias the analysis, as it removes indirect effects which work through such covariates.

  16. 16.

    As mentioned in Section 2, censoring is associated with death, migration abroad and, for the comparison group, enrolment in AE.

  17. 17.

    An estimator for the variance of the estimator is given in de Luna and Johansson (2010, Sec. 4.2); using this tool, confidence intervals can be computed with a normal approximation.

  18. 18.

    As suggested by an anonymous referee, the results could be driven by AE individuals who left the labour market after only 1 or 2 years. However, conditioning the sample to have survived in two successive years (1990 and 1991), the results are very similar (not displayed).

  19. 19.

    The full set of figures and confidence interval tables is available upon request from the authors.

  20. 20.

    The point estimates sometimes tend to be below zero. Although estimates are very small and not significantly different from zero, a negative effect on the survival rate is of course theoretically feasible, as described in the introduction.

  21. 21.

    To further check for proportional effects of AE, an anonymous referee suggested to include the number of registered credits as a single covariate in a proportional hazard regression (matched sample). The coefficient estimate for males was not significantly different from zero (p value of 0.57) whereas the estimate for females was positive and significant (younger participants are linked with higher number of credits), but not when applied to the thick support sample of females discussed above (p value 0.91 and N treated = 1,653). We also found it interesting to use an OLS regression on the number of registered credits as the first stage (instead of a probit). The balancing of the samples based on the fitted values work well, and the results indicate no significant differences in survival rates between treated and matched comparisons.

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Acknowledgements

The authors wish to thank seminar participants at the European Society for Population Economics 2008 conference and especially two anonymous referees and the editor in charge who helped us improve the quality of the manuscript. We also gratefully acknowledge financial support from The Swedish Research Council through the Ageing and Living Condition (ALC) program and the Swedish Initiative for Research on Microdata in the Social and Medical Sciences (SIMSAM) at Umeå University, and The Bank of Sweden Tercentenary Foundation.

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Correspondence to Anders Stenberg.

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Stenberg, A., de Luna, X. & Westerlund, O. Can adult education delay retirement from the labour market?. J Popul Econ 25, 677–696 (2012). https://doi.org/10.1007/s00148-010-0350-8

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Keywords

  • Human capital
  • Labour supply
  • Pensions

JEL Classification

  • H52
  • H55
  • I28