Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering - Sylvia Frühwirth-Schnatter; Stefan Pittner; Andrea Weber; Rudolf Winter-Ebmer (Institute for Advanced Studies (IHS), 2016)
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.
Keywords: 330; Transition data; Markov Chain Monte Carlo; Multinomial Logit; Panel data; Inhomogeneous Markov chains