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A Bayesian Analysis of Female Wage Dynamics Using Markov Chain Clustering

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Abstract

In this work, we analyze wage careers of women in Austria. We identify groups of female employees with similar patterns in their earnings development. Covariates such as e.g. the age of entry, the number of children or maternity leave help to detect these groups. We find three different types of female employees: (1) “high-wage mums”, women with high income and one or two children, (2) “low-wage mums”, women with low income and ‘many’ children and (3) “childless careers”, women who climb up the career ladder and do not have children. We use a Markov chain clustering approach to find groups in the discretevalued time series of income states. Additional covariates are included when modeling group membership via a multinomial logit model.

Suggested Citation

  • Christoph Pamminger & Regina Tüchler, 2011. "A Bayesian Analysis of Female Wage Dynamics Using Markov Chain Clustering," NRN working papers 2011-04, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
  • Handle: RePEc:jku:nrnwps:2011_04
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    1. Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
    2. Andrea Weber, 2002. "State dependence and wage dynamics: a heterogeneous Markov chain model for wage mobility in Austria," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 D2-2, International Conferences on Panel Data.
    3. Josef Zweim�ller & Rudolf Winter-Ebmer & Rafael Lalive & Andreas Kuhn & Jean-Philippe Wuellrich & Oliver Ruf & Simon B�chi, 2009. "Austrian social security database," IEW - Working Papers 410, Institute for Empirical Research in Economics - University of Zurich.
      • Josef Zweimüller & Rudolf Winter-Ebmer & Rafael Lalive & Andreas Kuhn & Jean-Philippe Wuellrich & Oliver Ruf & Simon Büchi, 2009. "Austrian Social Security Database," NRN working papers 2009-03, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
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    Cited by:

    1. Sylvia Frühwirth-Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter-Ebmer, 2016. "Mothers' long-run career patterns after first birth," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 707-725, June.
    2. Sylvia Frühwirth-Schnatter & Stefan Pittner & Andrea Weber & Rudolf Winter-Ebmer, 2016. "Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering," Economics working papers 2016-10, Department of Economics, Johannes Kepler University Linz, Austria.

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    Keywords

    Income Career; Transition Data; Multinomial Logit; Auxiliary Mixture Sampler; Markov Chain Monte Carlo;

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