A Bayesian Analysis of Female Wage Dynamics Using Markov Chain Clustering - Christoph Pamminger; Regina Tüchler (Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State, 2011)
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.
Keywords: 330; Income Career; Transition Data; Multinomial Logit; Auxiliary Mixture Sampler; Markov Chain Monte Carlo