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

Sylvia Frühwirth-Schnatter, Andrea Weber and Rudolf Winter-Ebmer

No 2010-11, Economics working papers from Department of Economics, Johannes Kepler University Linz, Austria

Abstract: This paper analyzes patterns in the earnings development of young labor market en- trants over their life cycle. We identify four distinctly di®erent types of transition patterns between discrete earnings states in a large administrative data set. Further, we investigate the e®ects 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 Pam- minger and FrÄuhwirth-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 ¯nite mixtures of ¯rst-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a prob- abilistic model for the latent group indicators within the Bayesian classi¯cation 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
New Economics Papers: this item is included in nep-ecm and nep-lab
References: View references in EconPapers View complete reference list from CitEc
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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