Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering

Sylvia Frühwirth-Schnatter (), Christoph Pamminger (), Andrea Weber and Rudolf Winter-Ebmer
Additional contact information
Sylvia Frühwirth-Schnatter: Department of Applied Statistics, Johannes Kepler University Linz, Austria, http://www.ifas.jku.at/e2571/e2626/index_ger.html
Christoph Pamminger: Department of Applied Statistics, Johannes Kepler University Linz, Austria,, http://www.ifas.jku.at/e2571/e2698/index_ger.html

No 2010-14, NRN working papers from The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria

Abstract: 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 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 di±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 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 probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model.

Keywords: Labor Market Entry Conditions; Transition Data; Markov Chain Monte Carlo; Multinomial Logit; Panel Data; Auxiliary Mixture Sampler; Bayesian Statistics (search for similar items in EconPapers)
Pages: 31 pages
Date: 2010-11
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering (2012)
Working Paper: Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering (2010)
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