Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162453
Authors: 
Frühwirth-Schnatter, Sylvia
Pittner, Stefan
Weber, Andrea
Winter-Ebmer, Rudolf
Year of Publication: 
2016
Series/Report no.: 
Working Paper, Department of Economics, Johannes Kepler University of Linz 1610
Abstract: 
In this paper, we study data on discrete labor market transitions from Austria. In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe - over a period of forty quarters - whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we develop and apply a new method of Bayesian Markov chain clustering analysis based on inhomogeneous first order Markov transition processes with time-varying transition matrices. In addition, a mixture-of-experts approach allows us to model the prior probability to belong to a certain cluster in dependence of a set of covariates via a multinomial logit model. Our cluster analysis identifies five career patterns after plant closure and reveals that some workers cope quite easily with a job loss whereas others suffer large losses over extended periods of time.
Subjects: 
Transition data
Markov Chain Monte Carlo
Multinomial Logit
Panel data
Inhomogeneous Markov chains
Document Type: 
Working Paper

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