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Parametric and Nonparametric Analysis of Life Courses: An Application to Family Formation Patterns

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  • Marco Bonetti

    ()

  • Raffaella Piccarreta

    ()

  • Gaia Salford

    ()

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    Abstract

    We discuss a unified approach to the description and explanation of life course patterns represented as sequences of states observed in discrete time. In particular, we study life course data collected as part of the Dutch Fertility and Family Surveys (FFS) to learn about the family formation behavior of 1,897 women born between 1953 and 1962. Retrospective monthly data were available on each 18- to 30-year-old woman living either with or without children as single, married, or cohabiting. We first study via a nonparametric approach which factors explain the pairwise dissimilarities observed between life courses. Permutation distribution inference allows for the study of the statistical significance of the effect of a set of covariates of interest. We then develop a parametric model for the sequence-generating process that can be used to describe state transitions and durations conditional on covariates and conditional on having observed an initial segment of the trajectory. Fitting ofthe proposed model and the corresponding model selection process are based on the observed data likelihood. We discuss the application of the methods to the FFS. Copyright Population Association of America 2013

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    File URL: http://hdl.handle.net/10.1007/s13524-012-0191-z
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    Bibliographic Info

    Article provided by Springer in its journal Demography.

    Volume (Year): 50 (2013)
    Issue (Month): 3 (June)
    Pages: 881-902

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    Handle: RePEc:spr:demogr:v:50:y:2013:i:3:p:881-902

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    Web page: http://www.springer.com/economics/journal/13524

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    Related research

    Keywords: Sequence analysis; Dissimilarity; Event history model; State change model;

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    1. Hilde Bras & Aart Liefbroer & Cees Elzinga, 2010. "Standardization of pathways to adulthood? an analysis of Dutch cohorts born between 1850 and 1900," Demography, Springer, vol. 47(4), pages 1013-1034, November.
    2. Cees H. Elzinga, 2005. "Combinatorial Representations of Token Sequences," Journal of Classification, Springer, vol. 22(1), pages 87-118, June.
    3. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
    4. Francesco Billari & Raffaella Piccarreta, 2005. "Analyzing Demographic Life Courses through Sequence Analysis," Mathematical Population Studies, Taylor & Francis Journals, vol. 12(2), pages 81-106.
    5. Raffaella Piccarreta & Francesco C. Billari, 2007. "Clustering work and family trajectories by using a divisive algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1061-1078.
    6. Duncan McVicar & Michael Anyadike-Danes, 2002. "Predicting successful and unsuccessful transitions from school to work by using sequence methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 317-334.
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