Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering. (English) Zbl 1405.62239

Summary: 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 40 quarters – whether these workers manage to return to a steady career path. To analyse these discrete-valued panel data, we 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 probability of belonging to a certain cluster as depending on 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.


62P25 Applications of statistics to social sciences
62M02 Markov processes: hypothesis testing
62H30 Classification and discrimination; cluster analysis (statistical aspects)
Full Text: DOI Euclid


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