Family Trajectories Across Time and Space: Increasing Complexity in Family Life Courses in Europe?


Family life courses are thought to have become more complex in Europe. This study uses SHARELIFE data from 14 European countries to analyze the family life courses of individuals born in 1924–1956 from ages 15 to 50. A new methodological approach, combining complexity metrics developed in sequence analysis with cross-classified multilevel modeling, is used to simultaneously quantify the proportions of variance attributable to birth cohort and country differences. This approach allows the direct comparison of changing levels of family trajectory differentiation across birth cohorts with cross-national variation, which provides a benchmark against which temporal change may be evaluated. The results demonstrate that family trajectories have indeed become more differentiated but that change over time is minor compared with substantial cross-national variation. Further, cross-national differences in family trajectory differentiation correspond with differences in dominant family life course patterns. With regard to debates surrounding the second demographic transition thesis and the comparative life course literature, the results indicate that the degree of change over time tends to be overstated relative to large cross-national differences.

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  1. 1.

    Maternity leave is defined as a minimum of 14 weeks paid and job-protected leave of absence according to the International Labour Organization (ILO) convention on maternity leave. Paternal leave is defined as a minimum of one week paid and job-protected leave of absence following childbirth or maternity leave for fathers as part of a parental leave scheme.

  2. 2.

    See ILO Ratifications of Social Security Convention, 1952 (No. 102) ( and the Country Fact Files of HelpAge International (

  3. 3.

    This article uses data from SHARE Waves 1, 2, and 3 (SHARELIFE) (Borsch-Supan 2017a, b, c). See Borsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C), and from various national funding sources is gratefully acknowledged (see This article uses data from the generated Job Episodes Panel (Orso et al. 2016; for methodological details, see Antonova et al. 2014; Brugiavini et al. 2013). The Job Episodes Panel release 5.0.0 is based on SHARE Waves 1, 2, and 3 (SHARELIFE) (Borsch-Supan 2017a, b, c).

  4. 4.

    I performed the analyses with different sequence state definitions to ensure that the results are not dependent on the sequence alphabet. The results are robust to more differentiated sequence state definitions.

  5. 5.

    The models are estimated using restricted maximum likelihood estimates (REML) and identity covariance matrices.

  6. 6.

    Shi et al. (2010) recommended that researchers include random interaction effects because their simulations and empirical tests showed that higher-level random-effects estimates are biased if significant interacted crossed factors are omitted.

  7. 7.

    I use the TraMineR (Gabadinho et al. 2011) package to calculate sequence complexities and the WeightedCluster (Studer 2013) package to perform cluster analyses on the sequence-based distance matrixes in R, version 3.2.0. The cross-classified regressions are calculated using the mixed command in STATA, version 14.

  8. 8.

    Country, cohort, and country-cohort balanced panels were generated and analyzed to test the robustness of the empirical Bayes predictions (i.e., predictions without reliability coefficients). The results do not change substantially and lead to the same substantive conclusions.

  9. 9.

    This response could mean that there was no main breadwinner or that the main breadwinner had no occupation (e.g., was unemployed).

  10. 10.

    Relative frequency sequence plots are generated by (1) sorting the sequences, (2) dividing the sorted sample into subgroups, (3) choosing medoid sequences from the subgroups to represent them, (4) plotting the medoid sequences, and (5) plotting dissimilarities from the medoid sequences as boxplots with R 2 and F statistics that evaluate the goodness of fit of the chosen medoid sequences. I sort the sequences using multidimensional scaling and divide the sample into 100 mediod sequences. The dissimilarities are calculated using OM distance. The plots were created with the seqplot.rf function developed by Matthias Studer, Anette Fasang, and Tim Liao implemented in the TraMineRextras package using R, version 3.2.0.

  11. 11.

    Although the Generations and Gender Programme has extremely rich life history data on a number of countries, using them would only extend the cohort range past 1956 for Poland, Belgium, and Sweden for long life trajectories. An alternative data source would be the 2006 European Social Survey used by Hofäcker and Chaloupková (2014) to analyze family formation trajectories across 23 European countries for those aged 18–35; however, these data would not expand the cohort range of individuals observed until age 50. Moreover, the 2006 European Social Survey did not collect information on the timing of divorce, which is necessary when analyzing family life courses until age 50.


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I thank Anette Fasang, Markus Schrenker, Jan Van Bavel, Brienna Perelli-Harris, Marcel Raab, Elizebeth Thomson, the participants of the Social Policy and Inequality writing workshop at the WZB, the participants of the Colloquium for Quantitative Research at the Institute for Social Sciences of the Humboldt University Berlin, and the editors and reviewers of Demography for insightful and constructive comments at various stages of the manuscript.

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Table 3 List of sequence state elements and average durations
Table 4 Sample frequencies cross-classified by country and birth cohort
Table 5 Descriptive statistics on the study countries and birth cohorts by cluster grouping
Table 6 Summary statistics by cluster grouping

Calculation of Sequence Complexity for Fig. 1

Maximum Longitudinal Entropy

Maximum entropy of a sequence with 10 consecutive states and eight possible sequence elements is calculated. An equal occurrence of each state element implies that each element is observed 1.25 times.

$$ {\displaystyle \begin{array}{l}{h}_{max}=-\left[10\cdotp \left(\frac{1.25}{10}\log \frac{1.25}{10}\right)\right]\\ {}\kern2.75em =2.079\end{array}} $$

Family Trajectory 1: P/2 S/2 M/2 MC/4

$$ {\displaystyle \begin{array}{l}h(x)=-\left[3\cdotp \left(\frac{2}{10}\log \frac{2}{10}\right)+\left(\frac{4}{10}\log \frac{4}{10}\right)\right]\\ {}\kern2.5em =1.332\end{array}} $$
$$ {\displaystyle \begin{array}{l}C(x)=100\cdotp \sqrt{\frac{3}{9}\cdotp \frac{1.332}{2.079}}\\ {}\kern2.5em =46.213\end{array}} $$

Family Trajectory 2: P/2 S/2 C/2 M/2 MC/2

$$ {\displaystyle \begin{array}{l}h(x)=-\left[5\cdotp \left(\frac{2}{10}\log \frac{2}{10}\right)\right]\\ {}\kern2.5em =1.609\end{array}} $$
$$ {\displaystyle \begin{array}{l}C(x)=100\cdotp \sqrt{\frac{4}{9}\cdotp \frac{1.609}{2.079}}\\ {}\kern2.5em =58.648\end{array}} $$

Family Trajectory 3: P/1 S/1 C/1 M/1 MC/6

$$ {\displaystyle \begin{array}{l}h(x)=-\left[4\cdotp \left(\frac{1}{10}\log \frac{1}{10}\right)+\left(\frac{6}{10}\log \frac{6}{10}\right)\right]\\ {}\kern2.5em =1.227\end{array}} $$
$$ {\displaystyle \begin{array}{l}C(x)=100\cdotp \sqrt{\frac{4}{9}\cdotp \frac{1.227}{2.079}}\\ {}\kern2.5em =51.215\end{array}} $$

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Van Winkle, Z. Family Trajectories Across Time and Space: Increasing Complexity in Family Life Courses in Europe?. Demography 55, 135–164 (2018).

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  • Family
  • Sequence analysis
  • Multilevel modeling
  • Comparative
  • Life course