Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering - Sylvia Frühwirth-Schnatter; Stefan Pittner; Andrea Weber; Rudolf Winter-Ebmer (Johannes Kepler University Linz, Christian Doppler Laboratory Aging, Health and the Labor Market, 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