Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/73582 
Year of Publication: 
2010
Series/Report no.: 
Working Paper No. 1011
Publisher: 
Johannes Kepler University of Linz, Department of Economics, Linz
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.
Subjects: 
Labor Market Entry Conditions
Transition Data
Markov Chain Monte Carlo
Multinomial Logit
Panel Data
Auxiliary Mixture Sampler
Bayesian Statistics
Document Type: 
Working Paper

Files in This Item:
File
Size
341.95 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.