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Clustering work and family trajectories by using a divisive algorithm

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  • Raffaella Piccarreta
  • Francesco C. Billari

Abstract

We present an approach to the construction of clusters of life course trajectories and use it to obtain ideal types of trajectories that can be interpreted and analysed meaningfully. We represent life courses as sequences on a monthly timescale and apply optimal matching analysis to compute dissimilarities between individuals. We introduce a new divisive clustering algorithm which has features that are in common with both Ward's agglomerative algorithm and classification and regression trees. We analyse British Household Panel Survey data on the employment and family trajectories of women. Our method produces clusters of sequences for which it is straightforward to determine who belongs to each cluster, making it easier to interpret the relative importance of life course factors in distinguishing subgroups of the population. Moreover our method gives guidance on selecting the number of clusters. Copyright 2007 Royal Statistical Society.

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Bibliographic Info

Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

Volume (Year): 170 (2007)
Issue (Month): 4 ()
Pages: 1061-1078

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Handle: RePEc:bla:jorssa:v:170:y:2007:i:4:p:1061-1078

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Cited by:
  1. Marco Bonetti & Raffaella Piccarreta & Gaia Salford, 2013. "Parametric and Nonparametric Analysis of Life Courses: An Application to Family Formation Patterns," Demography, Springer, vol. 50(3), pages 881-902, June.
  2. Dlugosz, Stephan, 2011. "Clustering life trajectories: A new divisive hierarchical clustering algorithm for discrete-valued discrete time series," ZEW Discussion Papers 11-015, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  3. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.

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