Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering

Frühwirth-Schnatter, Sylvia; Pittner, Stefan; Weber, Andrea and Winter-Ebmer, RudolfORCID: (2018) Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering. Annals of Applied Statistics, 12 (3), pp. 1796-1830.


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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 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.

Item Type: Article in Academic Journal
Keywords: Transition data, Markov Chain Monte Carlo, Multinomial Logit, Panel data, Inhomogeneous Markov chains
Research Units: Former Research Units (until 2020) > Labor Market and Social Policy
Date Deposited: 08 May 2018 10:13
Last Modified: 04 Jun 2019 08:13
DOI: 10.1214/17-AOAS1132
ISSN: 1932-6157

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