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Work and Family Trajectories: Changes Across Cohorts Born in the First Half of the 20th Century

Abstract

This paper deals with the relationship between family formation and employment in older cohorts of the English population born between 1916 and 1957. Based on retrospective life history data of the English Longitudinal Study of Ageing (ELSA) and using sequence and cluster analyses, we explore three dimensions in particular: employment, marital status, and having children, and the extent to which individuals’ life course trajectories on these three dimensions vary across cohorts, gender, and level of education. While the majority of men followed a trajectory of marriage and family formation with a (relatively) continuous career, the family-work trajectories of women varied noticeably from one cohort to the next, including increased labour market participation combined with fewer and shorter breaks from work to care for children. While the current perception is that the so-called ‘baby boomer’ generation born soon after World War Two was path-breaking in terms of life course innovations, our findings are not compatible with the assumption of a single cohort being particularly pioneering.

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Notes

  1. Another (contested) concept often used in this context is ‘individualisation’. However, individualisation implies an explanation of changing life course patterns (see for example Scherger 2009: 111), whereas (de-)standardisation strictly remains at the descriptive level, so that description and explanation can be separated allowing for the possibility of different explanations for the same patterns.

  2. Of course, childlessness is not only a matter of choice: there is a certain proportion of involuntary childlessness due to various problems related to infertility. However, assuming that the proportion of people who are infertile throughout the so-called ‘childbearing years’ is about the same in all cohorts and positing that infertility resulting from a delay in childbearing is caused by socially embedded decision processes, it is still justifiable to interpret changing rates of childlessness as an indicator of social change.

  3. For further details of the study see also Banks et al. (2008, 2012).

  4. The dynamic Hamming dissimilarity measure only defines substitution costs so that only sequences of equal length can be compared. For this reason incomplete trajectories were excluded from our analysis.

  5. The addition of cluster five to the four-cluster solution improves the ratio by 0.013, the addition of the sixth cluster by 0.018, the seventh by 0.001, the eighth by 0.014 and the ninth by 0.013.

  6. The index plots, which give an overview over the trajectories in each cluster, are provided in Fig. S1 in the Additional Online Material. The online material also contains the ten most frequent sequences by cluster (Online Material, Fig. S2) and the mean time spent in each state by cluster (Online Material, Fig. S3).

  7. Only around one quarter of the men in this cluster did not have one or several periods of ill health in their adult life, as opposed to around two thirds or more in most other clusters. The number of episodes and years of unemployment are also above average for the men in this cluster (table not shown).

  8. It has to be borne in mind that, in the calculation of states, a year was counted as one with paid employment even if only a very short period was spent in paid work. That means that breaks that are shorter than one to two years are not accounted for in our sequence analysis and the resulting clusters. Although this is a lack of precision, we would argue that it is exactly those visible longer breaks that have an enduring negative effect on women’s careers (Gangl and Ziefle 2009).

  9. For the composition of the clusters with regard to education see Table 3 in the Appendix. Although most non-work states at the beginning of the trajectories we observe will be times of education and thus the length of full-time education has entered the construction of the trajectories as well, the state of being in education has not been singled out and is regarded like other forms of non-employment. Educational qualification in itself has not entered the construction of the trajectories and the calculation of the clusters, so there is no problem of endogeneity concerning educational qualification. Occupational class is a very important influence as well, but it is problematic to include it in a multivariate model with cluster membership as a dependent variable because it is at the same time a cause and a result of the observed trajectories, the more so as the most precise measure available in the data relates to the end of the trajectories we observe.

  10. One more case has been excluded because the information on educational qualification is missing.

  11. The three categories of educational qualification are defined as follows: “high” includes NVQ4/NVQ5/Degree or equivalent and higher education below degree; “middle” refers to NVQ3/GCE A Level or equivalent, NVQ2/GCE O Level or equivalent, NVQ1/CSE other grade or equivalent or foreign/other qualification; and “none” refers to no formal educational qualification.

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Correspondence to Simone Scherger.

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Appendix

Table 2 Overview over sample characteristics
Table 3 Further cluster characteristics
Table 4 Multinomial Logit Model for cluster membership, men (cluster 6 excluded), relative risk ratios (95 %-confidence interval in brackets)
Table 5 Predicted Probabilities for cluster membership, men (cluster 6 excluded) (as shown in Figure 3 in main text)
Table 6 Multinomial Logit Model for cluster membership, women, relative risk ratios (95 %-confidence interval in brackets)
Table 7 Predicted Probabilities for cluster membership, women (as shown in Figure 4 in main text)

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Scherger, S., Nazroo, J. & May, V. Work and Family Trajectories: Changes Across Cohorts Born in the First Half of the 20th Century. Population Ageing 9, 131–155 (2016). https://doi.org/10.1007/s12062-015-9128-9

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Keywords

  • Employment careers
  • Family
  • Life courses
  • Cohorts
  • Baby boomers
  • Sequence analysis