Strings of Adulthood: A Sequence Analysis of Young British Women’s Work-Family Trajectories

Abstract

Employment, union formation and childbearing are central processes within young individuals' transition to adulthood. These processes interact in highly complex ways, and they shape actual life-course trajectories that may be seen as a conceptual unit. In this article we use a methodology to cluster life-course experiences, where all three processes are embedded explicitly, in order to study young women's trajectories in Great Britain. Drawing on a sample from the British Household Panel Survey (BHPS), we define life-courses as sequences on a monthly time scale and we apply optimal matching analysis to compute dissimilarities between individuals. We then use standard clustering algorithms and we identify nine distinctive groups of women. Our results are then shown using a new representation of clusters and interpreted in the light of the existing socio-demographic literature on the dynamic work-family link.Keywords Work-family trajectories Á Sequence analysis Á Transition to adulthood Á Life-course analysis Á BHPS Résumé Le travail, la mise en union et la procréation sont des processus centraux dans le passage vers l'âge adulte. Ces processus interagissent de manière très complexe, et modèlent les trajectoires biographiques, qui peuvent être considérées comme une unité conceptuelle. Dans cet article, nous utilisons une méthodologie pour catégoriser des expériences biographiques, en considérant que les 3 processus sont explicitement imbriqués, de manière à étudier les trajectoires des jeunes femmes en Grande Bretagne. A partir d'un échantillon du Panel Britannique des Ménages («BHPS»), la biographie est découpée en séquences sur une base mensuelle, et une analyse d'appariement optimale est appliquée pour calculer les dissemblances entre individus. Des algorithmes d'analyse de groupes sont ensuite utilisés, permettant d'identifier 9 groupes distincts de femmes. Les résultats sont illustrés à l'aide d'une nouvelle représentation des groupes, et interprétés à la lumière de la littérature sociodémographique sur la dynamique des relations famille-travail.

Sibling Similarity in Family Formation

“…3 therefore show a selection of representative sequences for each cluster that comprise 2 % of the sample (186 representative sequences). They are chosen as the medoid sequence (Aassve et al 2007)-that is, the sequence with the minimum distance to all other sequences within equal-sized frequency groups across the clusters (for details, see Fasang and Liao 2014). The dissimilarity-to-medoid plots in the right panels of Fig.…”

The timing of family commitments in the early work career

“…Aassve, Billari, and Piccarreta 2007). Our data allow for a relatively 5 detailed classification of each respondent's working career.…”

“…Starting from the individual sequences we constructed a typology of work-family trajectories by applying optimal matching (OM) (Abbott and Forest 1986;Abbott and Hrycak 1990;Abbott and Tsay 2000;Abbott 1984;Lesnard 2006; and more specifically for the analysis of work-family trajectories Aassve, Billari, and Piccarreta 2007). This method determines the distance between each pair of sequences for the subjects in the sample which expresses how difficult it is for one sequence to be transformed into another 6 .…”

“…In addition to identifying the main work-family trajectories among young women and men, we also aim to explain the differences and similarities between these trajectories. According to the literature, the heterogeneity in life courses is attributable to socio-structural and cultural factors, or to subjective factors and preferences (Aassve, Billari, and Piccarreta 2007;McRae 2003). Education seems to play a crucial role, as a wide range of studies have shown that an individual's educational level can affect (the timing of) his or her family formation decisions and labour outcomes Krause 2002, 2003;Budig and Hodges 2010;Dex et al 1998;Gustafsson 2003;Liefbroer and Corijn 1999;Rindfuss, Morgan, and Offutt 1996).…”

LLCS Vol 1, Issue 1, Full Issue

“…A first strategy involves first using optimal matching to compute one distance matrix for each dimension and then to carry out a linear combination of these matrices into one by means of linear combination (Han & Moen, 1999). A second strategy involves creating a new state variable combining the single states associated to each dimension (see for instance Blair-Loy, 1999;Aassve, Billari & Piccarreta, 2007;Pollock, 2007;Robette, 2010): for example, a combined state might be "female with a part-time job, male with a full-time job, one child". The advantage of this second alternative -the one we chose -is to take into account the interdependency of the dimensions right from the coding stage.…”