Measuring Life Course Complexity with Dynamic Sequence Analysis
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Measuring Life Course Complexity with Dynamic Sequence Analysis David Pelletier1,2,3 · Simona Bignami‑Van Assche3,4,5 · Anaïs Simard‑Gendron3 Accepted: 4 August 2020 © Springer Nature B.V. 2020
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. Keywords Life course · Sequence analysis · Complexity indicators · Turbulence · Crosscohort change
1 Introduction Since the mid-twentieth century, industrialized countries underwent profound demographic, social, and economic changes that have accompanied the rise of individualism and post-modernist values (Lesthaeghe 2010). To understand these secular transformations, individuals and their life courses have been put at the center of research in the social * David Pelletier [email protected] 1
Centre Urbanisation Culture Société, Institut national de la recherche scientifique (INRS), Montreal, Canada
2
Department of Sociology, McGill University, Montreal, Canada
3
Department of Demography, Université de Montréal, Montreal, Canada
4
CIRANO, Montreal, Canada
5
Center for Population Dynamics, McGill University, Montreal, Canada
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sciences for the past 30 years. The empirical measurement of this transformation has been an object of study in its own right and the tools of sequence analysis have been identified as one of the most appropriate methodological frameworks for this purpose (Abbott 1995; Abbott and Tsay 2000; Billari 2001a; Billari and Piccarreta 2005). Indeed, sequence analysis allows researchers to move beyond a focus on specific events—such as first union formation, full-time employment, or childbearing—and adopt a more holistic vision of trajectories comprising several events and spanning different domains. Complexity (or differentiation) is one of several dimensions of the life course that has received increasing attention in recent studies (Aisenbrey and Fasang 2010; Brückner and Mayer
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