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Commuting and timing of retirement

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Abstract

Interregional commuting is an important feature of labour supply and regional labour market adjustment. In this study, we examine the effect of long-distance commuting (LDC) on timing of retirement. Previous research indicates negative health effects and substantial disutility of commuting. Potentially, this may affect the labour supply of older workers via early retirement. Longitudinal population register data from Sweden on employed older workers are used for semi-parametric estimation of survival in the labour force. The results for men indicate shorter survival in the labour force/earlier retirement for LDCs, primarily among men with high education. For women, there is no evidence of LDC being associated with early retirement. For women with high education, there are indications of longer survival in the labour force among the commuters. The seemingly contradictory results for the highly educated may be due to gender differences in commuting distances and socio-economic attributes of commuters.

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Notes

  1. See, for example, Gutiérrez-i-Puigarnau and van Ommeren (2010) for an overview.

  2. Adjustments do take place by residential and/or job mobility when individuals seek new jobs closer to their home.

  3. The sample is hence restricted to employed individuals with an annual work income greater than zero. Also, the individuals are not retired by the formal definition presented above.

  4. \(p_{it}\) is the sum of age-related pensions and early retirement pensions, including sickness pensions and various occupational pensions.

  5. Re-entries by retired individuals into the labour market during the period studied concern around 4.6 % of the sample.

  6. A similar approach is used in Stenberg et al. (2012) and Sandow et al. (2014). See also, for example, Austin (2013).

  7. This potential source of selection bias is akin to potential bias due to “Ashenfelter’s dip”(Ashenfelter 1978).

  8. In contrast to estimates from propensity score matching, OLS estimates of treatment effects are proportional to how often a value of an explanatory variable occurs and to the variation in treatment for this value. When treatment effects are heterogeneous across individuals, the OLS estimate may deviate from the treatment effect on the treated as well as from the treatment effect on the non-treated (Angrist 1998).

  9. As mentioned earlier, the subjective randomization must be such that the treated and untreated samples do not differ with respect to the censoring mechanism. One censoring mechanism that potentially is dependent on treatment is related to mortality. However, a t test fails to reject the null hypothesis of equal unconditional mortality rates between the treatment group and the matched comparisons (at the 5 % level), for men and women and with equal variances of compared samples.

  10. Results available in Online Appendix or on request from the authors.

  11. Results available from the authors on request.

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Acknowledgments

The authors are grateful for valuable suggestions from two anonymous referees and financial support from the Swedish Research Council (Linnaeus Grant Number 2006-21576-36119-66) and Swedish Council for Health, Working Life and Welfare (FORTE, Grant Number 2006-1010).

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Authors and Affiliations

  1. Umeå School of Business and Economics, Umeå University, 901 87, Umeå, Sweden

    Peter Bäckström

  2. Swedish Defence Research Agency (FOI), 164 90 Stockholm Umeå School of Business and Economics, Umeå University, 901 87, Umeå, Sweden

    Peter Bäckström

  3. Department of Economics, Centre for Demographic and Ageing Research, Umeå School of Business and Economics, Umeå University, 901 87, Umeå, Sweden

    Erika Sandow

  4. Umeå School of Business and Economics, Centre for Demographic and Ageing Research, Umeå University, 901 87, Umeå, Sweden

    Olle Westerlund

Authors
  1. Peter Bäckström
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  2. Erika Sandow
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  3. Olle Westerlund
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Correspondence to Erika Sandow.

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Appendices

Appendix 1

See Figs. 5, 6, 7, 8, 9 and 10.

Fig. 5
figure 5

Means of survival rates for men, matched samples of long-distance commuters and comparison group, uncensored observations ages 56–69. Note Sample means for observations on common support in propensity score estimates

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Fig. 6
figure 6

Means of survival rates for women, matched samples of long-distance commuters and comparison group, uncensored observations ages 56–69

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Fig. 7
figure 7

Means of survival rates for men with high education, matched samples of long-distance commuters and comparison group, uncensored observations ages 56–69

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Fig. 8
figure 8

Means of survival rates for women with high education, matched samples of long-distance commuters and comparison group, uncensored observations ages 56–69

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Fig. 9
figure 9

Survival rates for men, 1994–2008. Alternative treatment, 20 km. Matched samples of long-distance commuters and comparison group. Note Log-rank test for equality of survivor functions: \(\chi ^{2}= 11.52\), p value \(=\) 0.0007. Wilcoxon (Breslow) test for equality of survivor functions: \(\chi ^{2}= 18.18\), p value \(=\) 0.0000. Nbr obs: treated \(=\) 5518, untreated \(=\) 11,200

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Fig. 10
figure 10

Survival rates for women, 1994–2008. Alternative treatment, 20 km. Matched samples of long-distance commuters and comparison group. Note Log-rank test for equality of survivor functions: \(\chi ^{2}= 0.75\), p value \(=\) 0.3853. Wilcoxon (Breslow) test for equality of survivor functions: \(\chi ^{2}= 0.05\), p value \(=\) 0.8231. Nbr obs: treated \(=\) 3116, untreated \(=\) 8727

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Appendix 2

See Appendix Tables 5, 6, and 7.

Table 5 Descriptive statistics
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Table 6 Sample means and balancing test of treated and controls in 1994, unless stated otherwise. Men
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Table 7 Sample means and balancing test of treated and controls in 1994, unless stated otherwise. Women
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Bäckström, P., Sandow, E. & Westerlund, O. Commuting and timing of retirement. Ann Reg Sci 56, 125–152 (2016). https://doi.org/10.1007/s00168-015-0723-8

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