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Time use during the life course in the USA, Norway, and the Netherlands: a HAPC-analysis

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  • Versantvoort, Maroesjka
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    Abstract

    This paper analyses life course variations by means of Hierarchical Age-Period-Cohort-modelling (HAPC) of time use data for thee welfare states: the USA, Norway, and the Netherlands. By means of analyzing time use data insight is gained in the (relative) importance of various life spheres as paid work, household work, volunteer aid, care, anc education in and over people's life. The relevance of an integrated insight in the relation between paid work and these other life spheres seems to have grown with the introduction and (policy) application of the idea of "transitional labour markets". This paper aims to find out the relevance of age, period and cohort as underlying factors in population ageing and change. The author compares the fixed versus the random-effects model specifications for APC-analysis. The random-effects HAPC-model appears the most appropriate specification. The HAPC analyses find evidence in support of quadratic age effects on time use. Furthermore, the HAPC analyses find proof in support of the contentions in the literature that both cohort and period effects should be distinguished in life course analyses. Finally, the analyses show clear differences in time use patterns during the life course between the welfare states. These may indicate a non-negligible sensitivity for welfare policies with respect to reconciling life domains during the life course.

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    File URL: http://mpra.ub.uni-muenchen.de/20980/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 20980.

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    Date of creation: 2008
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    Handle: RePEc:pra:mprapa:20980

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    Related research

    Keywords: Age-Period-Cohort analysis; hierarchical linear modeling; life course; time use; welfare states;

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    1. S. Illeris & G. Akehurst, 2001. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 21(1), pages 1-4, January.
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