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

Frühwirth-Schnatter, Sylvia and Pittner, Stefan and Weber, Andrea and Winter-Ebmer, Rudolf (2018) Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering. The Annals of Applied Statistics, 12 (3). pp. 1796-1830. ISSN 1932-6157


Download (1MB)


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 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
Additional Information: Supported by the Austrian Science Fund (FWF): S10309-G16 (NRN "The Austrian Center for Labor Economics and the Analysis of the Welfare State") and the CD Laboratory Ageing, Health and the Labor Market.
Keywords: Transition data, Markov chain Monte Carlo, multinomial logit, panel data, inhomogeneous Markov chains.
Divisions: Departments > Finance, Accounting and Statistics
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 01 Oct 2018 13:22
Last Modified: 26 Oct 2018 15:54
Related URLs:


View Item View Item


Downloads per month over past year

View more statistics