Labor market entry and earnings dynamics: Bayesian inference using mixtures-of-experts Markov chain clustering

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative dataset. Further, we investigate the effects of labor market co...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2012-11, Vol.27 (7), p.1116-1137
Main Authors: Frühwirth-Schnatter, Sylvia, Pamminger, Christoph, Weber, Andrea, Winter-Ebmer, Rudolf
Format: Article
Language:English
Subjects:
Arbeitskraft
Bayes-Statistik
Bayesian inference
Bayesian method
Berufseintritt
Causal inference
Cluster analysis
Earnings
Econometrics
Economic models
Erwerbsverlauf
Labor market
Labor markets
Labour market
Life cycles
Lohn
Market conditions
Market entry
Markov analysis
Markov chains
Markovian processes
Markowscher Prozess
Monte-Carlo-Methode
Parametric models
Regression coefficients
Schätzung
Statistical models
Studies
Time series
Time series models
Wages
Younger workers
Österreich
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Summary:This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative dataset. 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 identifying groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, 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 probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model.
ISSN:0883-7252
1099-1255
1099-1255
DOI:10.1002/jae.1249