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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IHS Economics Series ; No. 324

Classification and Topics

• Classification: Wirtschaft
• Subject: Transition data ; Markov Chain Monte Carlo ; Multinomial Logit ; Panel data ; Inhomogeneous Markov chains

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Frühwirth-Schnatter, Sylvia ; Pittner, Stefan ; Weber, Andrea ; Winter-Ebmer, Rudolf
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/148269
• Last update: 10.03.2025, 11:45 AM CET