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Union Histories of Dissolution: What Can They Say About Childlessness?

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Abstract

This study investigates how the association between union dissolution and childlessness depends on life course context. Data on union histories and fertility are taken from the Norwegian GGS. To observe union histories up to age 45, I include men and women born 1927–1962. I further condition on having experienced at least one union dissolution before age 45, giving a study sample of 883 men and 1110 women. To capture the life course context of union dissolutions, I group union histories similar in timing, occurrence and ordering of events using sequence analysis. Eight well-clustered groups of union histories are distinguished. Four consist of life courses dominated by a long first or second union and display low levels of childlessness. The highest proportion childlessness is found among individuals who entered a first union late and dissolved it quickly. Groups characterised by long spells alone after a dissolution or many short unions also displayed a high proportion of childlessness. In contrast to findings from the USA, neither union trajectories nor their link with childlessness varies by educational attainment.

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Notes

  1. For a total of 105 unions in this sample, the previous union was reported as dissolved before the next union was entered into. For these unions, the time of union dissolution was set to two months before entry into the next union.

  2. While it could also be of great substantive interest to also look at work or earnings histories, these are not available in data for the full sample.

  3. i.e. the distance between two states is inversely proportional to the frequency of transition between these two states (Lesnard 2010, p. 401).

  4. In general, AGNES algorithms start with N clusters, merging clusters stepwise until it reaches one cluster with N observations (Kaufman and Rousseeuw 2005, p. 199). For calculation of dissimilarity between clusters, the Ward method is applied, as suggested for sequence analysis (Gabadinho et al. 2011).

  5. Birth cohorts are grouped into 5-year categories with two exceptions: the oldest cohorts (1927–1934) are grouped together for statistical power, and the youngest cohort (1960–1962) has a narrower range. A quadratic specification of birth cohort did not improve the efficiency of the model, but failed to capture the strong nonlinearities in the cohort trend, and a dummy specification was hence preferred.

  6. Model 1d omits the interaction between educational attainment and cluster for statistical power and is hence built stepwise from Model 1b.

  7. Weights for region of residence, centrality, sex, and educational attainment are included to correct for selective non-response (Bjørshol et al. 2010).

  8. Predictions are made based on estimates in Model 1a. The predicted probability for cluster k is defined as \(pp_k = \hat{\beta }_0 + \hat{\beta }_k\).

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Acknowledgements

The work was supported by the Norwegian Research Council under Grants No. 202442S20 and 236926.

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Authors and Affiliations

  1. Department of Sociology and Human Geography, University of Oslo, Postboks 1096 Blindern, 0317, Oslo, Norway

    Rannveig Kaldager Hart

  2. Research Department, Statistics Norway, Oslo, Norway

    Rannveig Kaldager Hart

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Correspondence to Rannveig Kaldager Hart.

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Earlier drafts of this paper have been presented at the PAA 2014 in Boston, EPC 2014 in Budapest, and the graduate students writing seminar at Department of Demography, UC Berkeley. I am grateful to participants in these meetings, as well as Synøve N. Andersen, Janna Bergsvik, Paul Chung, Joshua Goldstein, Trude Lappegård, Torkild H. Lyngstad, Kjetil Telle and Elina Vinberg for comments on earlier drafts. I acknowledge assistance from Elina Vinberg and Torkild H. Lyngstad in preparation of the data set.

Appendix

Appendix

See Figs.2, 3 and Table 6.

Fig. 2
figure 2

Distribution on cluster by cohort

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Fig. 3
figure 3

Within–between ratio (left panel) and average silhouette width (right panel) for different sequencing techniques and cluster solutions

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Table 6 Model 1e: The association between union history (as captured by cluster) and the probability to remain childless
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Hart, R.K. Union Histories of Dissolution: What Can They Say About Childlessness?. Eur J Population 35, 101–131 (2019). https://doi.org/10.1007/s10680-018-9464-6

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