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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

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

Suggested Citation

  • Marco Bonetti & Raffaella Piccarreta & Gaia Salford, 2013. "Parametric and Nonparametric Analysis of Life Courses: An Application to Family Formation Patterns," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 881-902, June.
  • Handle: RePEc:spr:demogr:v:50:y:2013:i:3:p:881-902
    DOI: 10.1007/s13524-012-0191-z
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    References listed on IDEAS

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    6. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
    7. Francesco Billari & Raffaella Piccarreta, 2005. "Analyzing Demographic Life Courses through Sequence Analysis," Mathematical Population Studies, Taylor & Francis Journals, vol. 12(2), pages 81-106.
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    11. Liming Cai & Mark D. Hayward & Yasuhiko Saito & James Lubitz & Aaron Hagedorn & Eileen Crimmins, 2010. "Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(6), pages 129-158.
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    Cited by:

    1. Marco Raffaella Piccarreta & Marco Bonetti & Stefano Lombardi, 2018. "Comparing models for sequence data: prediction and dissimilarities," Working Papers 113, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    2. Anette Fasang & Marcel Raab, 2014. "Beyond Transmission: Intergenerational Patterns of Family Formation Among Middle-Class American Families," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1703-1728, October.
    3. Cees H. Elzinga & Matthias Studer, 2015. "Spell Sequences, State Proximities, and Distance Metrics," Sociological Methods & Research, , vol. 44(1), pages 3-47, February.
    4. Piccarreta, Raffaella & Bonetti, Marco, 2019. "Assessing and comparing models for sequence data by microsimulation (with Supplementary Material)," SocArXiv 3mcfp, Center for Open Science.
    5. Júlia Mikolai & Hill Kulu, 2019. "Union dissolution and housing trajectories in Britain," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(7), pages 161-196.

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