Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering
AbstractThis 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 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 diÂ±culty 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 Pamminger and FrÃ¼hwirth-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 probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 27 (2012)
Issue (Month): 7 (November)
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
Other versions of this item:
- Sylvia Frühwirth-Schnatter & Andrea Weber & Rudolf Winter-Ebmer, 2010. "Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering," Economics working papers 2010-11, Department of Economics, Johannes Kepler University Linz, Austria.
- Sylvia Frühwirth-Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter-Ebmer, 2010. "Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering," NRN working papers 2010-14, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
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