A Bayesian Analysis of FemaleWage Dynamics Using Markov Chain Clustering
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.
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