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

Sylvia Frühwirth-Schnatter, Stefan Pittner, Andrea Weber, Rudolf Winter-Ebmer

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

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.

Original languageEnglish
Pages (from-to)1796-1830
Number of pages35
JournalAnnals of Applied Statistics
Volume12
Issue number3
DOIs
StatePublished - Sep 2018

Keywords

  • Inhomogeneous Markov chains
  • Markov chain Monte Carlo
  • Multinomial logit
  • Panel data
  • Transition data

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