Career-path analysis using drifting Markov models (DMM) and self-organizing maps

Sébastien Massoni, Madalina Olteanu () and Patrick Rousset
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Madalina Olteanu: SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) - UP1 - Université Paris 1 Panthéon-Sorbonne
Patrick Rousset: CEREQ - Centre d'études et de recherches sur les qualifications - ministère de l'Emploi, cohésion sociale et logement - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: Analyzing school-to-work transitions is an important challenge for the specialists of the labor-market. The aim of this paper is to study the insertion of graduates and to identify the main career-paths typologies. We introduce a new methodology for clustering career-paths by combining statistical estimation of non-homogeneous Markov chains with self-organizing maps. The proposed methodology is tested on real-life data issued from the survey ''Generation 98'' elaborated by CEREQ, France (http://www.cereq.fr/)

Keywords: Career paths; categorical data; drifting Markov model; self organizing maps (search for similar items in EconPapers)
Date: 2010
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Published in MASHS, 2010, Lille, France

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Working Paper: Career-path analysis using drifting Markov models (DMM) and self-organizing maps (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:hal-00443530

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