Abstract
Increasing labor mobility is high on the political agenda because of its supposedly positive effects on labor market functioning. However, little attention has been paid to information imperfections, and to what extent they limit potential efficiency gains of labor mobility. When the quality of a new job offer is known ex ante, job quits serve as a stepping stone to better jobs. Yet, if job quality is only observed ex post, job quits may lead to worse matches. This paper argues that actual job quit behavior is characterized by a mixture of both, and investigates the relative empirical content of both extremes in quit decisions. A variance decomposition shows that for nearly 70% of job quits job quality was observed ex-ante; the remaining 30% was learned ex post. Hence, stimulating job mobility mostly improves labor market outcomes, though governments may aim to further reduce information imperfections in order to maximize the efficacy of labor policies. J28, J62
Highlights
During recent decades, enhancing labor market flexibility has been one of the main pillars of European labor market policy
Given the recent evidence that workers usually gain in job satisfaction and obtain higher wages after a job quit (e.g. Perez and Rebollo (2005); Chi et al (2008)), the fact that many decide to change jobs again shortly after an initial quit raises the question whether these job changes always contribute to better matching efficiency or whether some changes constitute a relocation of labor without any increase in match quality
This paper argues that actual job quit behavior is characterized by a mixture of both
Summary
During recent decades, enhancing labor market flexibility has been one of the main pillars of European labor market policy. The main aim of the paper is to study the extent to which repeated job quits can be explained by the stepping-stone theory versus the learning model, and to determine the relative importance of both models in job quit decisions This information is crucial for policy makers aiming to improve labor market functioning by stimulating labor mobility. In the last − and most important − part of this paper testable predictions from both theoretical models concerning the variance in match quality in the new job are used to investigate the relative empirical content of the stepping stone and the learning model. The larger s, the more effective is labor policy aimed at improving labor market outcomes through increased labor mobility
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