Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering

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 mixtureof-
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.