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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\u2010based clustering approach. The statistical challenge in our application comes from the difficulty in extending distance\u2010based clustering approaches to the problem of identifying groups of similar time series in a panel of discrete\u2010valued time series. We use Markov chain clustering, which is an approach for clustering discrete\u2010valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first\u2010order time\u2010homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model. 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