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This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. The authors identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further, they investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate the statistical model they use a model-based clustering approach. The statistical challenge in the 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.
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