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Measuring Life Course Complexity with Dynamic Sequence Analysis

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Abstract

The transformation of life courses in industrialized countries since the mid-twentieth century can be analyzed through the lens of life course complexity, a function of the number of transitions or states experienced by individuals over a given time span. Life course complexity is often measured with composite indices in a static sequence analysis framework (i.e. over a single age interval), but this method has seldom been evaluated. This paper fills this gap. We review nine indicators of life course complexity and explore the advantages of a dynamic approach to sequence analysis (i.e. examining many nested or consecutive age intervals). An application to data on the partnership histories of American and French women is used to show the properties of each measure. We conclude that simple indicators, used alone or in combination, provide a more easily interpretable description of changes and differentials in life course complexity than commonly used composite indices. In addition, we show that, for all indicators, a dynamic approach allows a more nuanced illustration of age-related transformations of life course complexity than the static approach does.

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Notes

  1. In empirical applications, this is in part a matter of state space definition. For instance, “marriage” is a repeatable state (one could alternate between spells of time being married and not married), but “first marriage”, “second marriage”, etc. are not repeatable states (once one leaves the first-marriage state through divorce, one cannot come back to it and would enter the second-marriage state upon remarriage).

  2. An introduction to sequence analysis can be found in Billari (2001a) and an overview of the method’s evolution in Halpin (2013). More recent methodological advances are illustrated in Ritschard and Studer (2018).

  3. In sequence analysis, as in many other forms of process analysis (e.g. Markov chains, survival or multistate analysis), states usually belong to a discrete, predefined, and mutually exclusive set. Therefore, results depend at least to some extent on how the set of states was defined by researchers in the first place. For instance, does one consider employment as a single state or distinguish between various types of employment (full-time vs. part-time; manual vs. clerical; permanent vs. contractual; etc.)? While defining the state space is a critical step in any empirical analysis, it is also very specific to each research application and theoretical approach. Therefore, we do not address this issue further.

  4. See Manzoni and Mooi-Reci (2011) for an exception involving complexity indicators.

  5. Shannon entropy, calculated cross-sectionally, has also been used as a measure of destandardisation (e.g. Billari 2001b; Fussell et al. 2007).

  6. Various logarithm bases can be used in the calculation. Here the issue is of no consequence because we use the normalized version of Shannon entropy, which is independent of the base.

  7. We identify Elzinga’s complexity indices with the symbol CE to distinguish them from Gabadinho, Ritschard, Studer, and Müller’s own “complexity index” (described in the next section), which we identify as CG.

  8. Elzinga (2010) also discusses the extraction of subsequences from sequences of elements, but recommends the approach described in the text, which is furthermore the approach used in software implementations.

  9. In the formula, we present the simple version of T, identified as T’ in Elzinga (2010). This is also the version presented in Elzinga and Liefbroer (2007).

  10. Note that using confidence intervals based on the t distribution gave substantially similar results.

  11. The minimum and maximum values of \(\phi \left( x \right)\) and CE(x) depend on the number of spells and the size of the state space. For instance, given a sequence with only one spell, \(C^{E}_{min} = C^{E}_{max} = 1 =\) the number of spells, whatever the size of the state space. But given a sequence containing five spells, \(C^{E}_{min} = C^{E}_{max} = 4.32\) if the state space contains two states, but \(C^{E}_{min} = 4.32\) and \(C^{E}_{max} = 5\) = the number of spells if the state space contains at least five possible states. Therefore, CE(x) can depart from the number of spells only in sequences with many spells but a small state space. But even in sequences with as many as 20 spells, which would be a rare situation in social sequence analysis, \(C^{E}_{min}\) would still be quite close to the number of spells, representing approximately 75% of its value.

  12. We use 29 rather than 30 in Table 3 because it’s the highest age reached in all six country-cohorts by more than two-thirds of members. More than half of members reached age 30 in all cohorts.

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Acknowledgements

The authors wish to thank Anne H. Gauthier for helpful comments on an earlier draft. The first author’s work was funded by the Fonds de recherche du Québec – Société et culture. This study uses data from the Harmonized Histories data file created by the Non-Marital Childbearing Network (www.nonmarital.org) (see Perelli-Harris et al. 2010). This file harmonizes childbearing and marital histories from 14 countries in the Generations and Gender Programme (GGP) with data from Spain (Spanish Fertility Survey), United Kingdom (British Household Panel Study) and United States (National Survey for Family Growth). Thank you to everyone who helped collect, clean, and harmonize the Harmonized Histories data, especially Karolin Kubisch at MPIDR. Data are available at www.ggp-i.org.

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Authors and Affiliations

  1. Centre Urbanisation Culture Société, Institut national de la recherche scientifique (INRS), Montreal, Canada

    David Pelletier

  2. Department of Sociology, McGill University, Montreal, Canada

    David Pelletier

  3. Department of Demography, Université de Montréal, Montreal, Canada

    David Pelletier, Simona Bignami-Van Assche & Anaïs Simard-Gendron

  4. CIRANO, Montreal, Canada

    Simona Bignami-Van Assche

  5. Center for Population Dynamics, McGill University, Montreal, Canada

    Simona Bignami-Van Assche

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Pelletier, D., Bignami-Van Assche, S. & Simard-Gendron, A. Measuring Life Course Complexity with Dynamic Sequence Analysis. Soc Indic Res 152, 1127–1151 (2020). https://doi.org/10.1007/s11205-020-02464-y

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