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An empirical approach on labour segmentation. Applications with individual duration data
Institution:1. Universidad de Sevilla, Spain;2. Universidad Pablo de Olavide, Seville, Spain;1. Fujian Branch of China Construction Bank, PR China;2. Xiamen University, PR China;3. Economics & Management of Shanghai Jiao Tong University, PR China;4. School of Economics and Management, Fuzhou University, PR China;1. CREST-Ecole Nationale de la Statistique et de l''Analyse de l''Information (Ensai) and ULCO, Campus de Ker-Lann, Rue Blaise Pascal, 35170 Bruz, France;2. Univ Lyon, Université Lumière Lyon 2, Institut Universitaire de France, GATE L-SE UMR 5824, F-69130 Ecully, France;3. Concordia University and CIREQ, Canada
Abstract:This article proposes empirical tools to account for the role of heterogeneities in the labour matching process, and shows an application to the Andalusian labour market which relies on individual data. The central idea of the paper is that the labour market is segmented, and this segmentation can be treated empirically by grouping workers, jobs and matches into labour groups according to their characteristics. In a segmented labour market the probability that a match occurs in a particular job group affects the probability that a match occurs in a particular worker group or vice versa. We propose two empirical measures related to this idea: propensity to match, and segmentation in worker and job groups. The usefulness of this empirical framework is shown by its application to different labour market analyses. Firstly, we use a clustering methodology, based on a similarity measure, to obtain a better overview of the structure of the labour market. Secondly, we propose a measure of mobility based on our similarity measure, and estimate a regression model that relates mobility to worker and job characteristics and to the economic cycle. Finally, these tools are included in an unemployment duration model. The proposed methodology may be useful in labour intermediation by helping seekers to follow a ‘roadmap’ of successful paths.
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