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Validating Tree Descriptions of Women's Labor Participation with Deviance-based Criteria

Author

Listed:
  • Gilbert Ritschard

    (UNIGE - Université de Genève = University of Geneva)

  • Fabio Beniamino Losa

    (African Development Bank Group - African Development Bank Group)

  • Origoni Pau

    (UNIL - Université de Lausanne = University of Lausanne)

Abstract

This chapter presents a full scaled application of induction trees for non-classificatory purposes. The grown trees are used for high-lighting regional differences in the women's labor participation, by using data from the Swiss Population Census. Hence, the focus is on their descriptive rather than predictive power. A first tree provides evidence for three separate analyses for non-mothers, married or wid-owed mothers, and divorced or single mothers. For each group, trees grown by language regions exhibit fundamental cultural differences supporting the hypothesis of cultural models in female participation. From the methodological standpoint, the main difficulties with such a non-classificatory use of trees have to do with their validation, since 1 the classical classification error rate does not make sense in this setting. We comment on this aspect and propose deviance-based solutions that are both consistent with our non-classificatory usage and easy to compute.

Suggested Citation

  • Gilbert Ritschard & Fabio Beniamino Losa & Origoni Pau, 2013. "Validating Tree Descriptions of Women's Labor Participation with Deviance-based Criteria," Post-Print halshs-01182913, HAL.
    • Handle: RePEc:hal:journl:halshs-01182913
    as

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