Article thumbnail



This paper analyzes patterns in the earnings development of young labor market en-trants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model-based clustering approach. The statistical challenge in our application comes from the difficulty in extending distance-based clustering approaches to the problem of identify groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, proposed by Pam-minger and Frühwirth-Schnatter (2010), which is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a prob-abilistic model for the latent group indicators within the Bayesian classification rule using

Topics: Labor Market Entry Conditions, Transition Data, Markov Chain Monte Carlo, Multinomial Logit, Panel Data, Auxiliary Mixture Sampler, Bayesian Statistics ∗Department of Applied Statistics, Johannes Kepler University Linz, Altenbergerstraße 69
Year: 2010
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.