Arbeitspapier

Job durations with worker and firm specific effects: MCMC estimation with longitudinal employer-employee data

We study job durations using a multivariate hazard model allowing for worker-specific and firm-specific unobserved determinants. The latter are captured by unobserved heterogeneity terms or random effects, one at the firm level and another at the worker level. This enables us to decompose the variation in job durations into the relative contribution of the worker and the firm. We also allow the unobserved terms to be correlated. For the empirical analysis we use a Portuguese longitudinal matched employer-employee data set. The model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) estimation method. The results imply that firm characteristics explain around 30% of the variation in log job durations. In addition, we find a positive correlation between unobserved worker and firm characteristics.

Language: Englisch

Bibliographic citation: Series: IZA Discussion Papers ; No. 3992

Classification: Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Duration Analysis; Optimal Timing Strategies
Demand and Supply of Labor: General
Labor Contracts
Job, Occupational, and Intergenerational Mobility; Promotion

Subject: Job transitions
assortative matching
Gibbs sampling
frailties
dynamic models
matched employer-employee data
Humankapital
Arbeitsmobilität
Statistische Bestandsanalyse
Bayes-Statistik
Monte-Carlo-Methode
Portugal

Event: Geistige Schöpfung

(who): Horny, Guillaume
Mendes, Rute
van den Berg, Gerard J.

Event: Veröffentlichung

(who): Institute for the Study of Labor (IZA)

(where): Bonn

(when): 2009

Handle: http://hdl.handle.net/10419/35686

URN: urn:nbn:de:101:1-20090304472

Last update: 10.03.2025, 11:45 AM CET

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Object type

Arbeitspapier

Associated

Horny, Guillaume
Mendes, Rute
van den Berg, Gerard J.
Institute for the Study of Labor (IZA)

Time of origin

2009

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Job durations with worker and firm specific effects: MCMC estimation with longitudinal employer-employee data

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Other Objects (12)

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