Job Duration and Bayesian Learning: Evidence from Germany

In a job matching context, Bayesian learning is assumed in order to provide an optimising framework for the analysis of workers' labour turnover decisions. This framework allows workers' labour turnover behaviour to be affected not only by the wage rate but also by a vector of non-wage job attributes and self-reported satisfaction variables. Assuming that workers' behaviour sufficiently conforms with the normative guidelines suggested by such a Bayesian learning model, the importance of the wage rate relative to the importance of satisfaction and non-wage variables in determining job duration in Germany is examined using econometric survival analysis. To capture the dynamic nature of workers' labour turnover behaviour, survival analysis with "time-varying" covariates is used. The empirical results, based on information from the German Socio-Economic Panel data set, confirm the importance of non-wage attributes and satisfaction variables in determining job duration and they are broadly consistent with the non-monotonic hazard function for job separations suggested by the above theoretical framework.