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Classification of longitudinal career paths

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  • Pietro Lovaglio

  • Mario Mezzanzanica

Abstract

The aim of the present article is to classify, in terms of contractual stability, the careers of the workers in a specified territorial context (Province of Milan-Italy), utilizing large administrative archives. The final goal is a synthetic clustering that identifies individuals in homogeneous groups regarding the longitudinal sequences of contractual typologies occurring in the evolution of vocational experiences during their career, identifying, on the one hand, the worker profiles that remain stable in each contractual typology and on the other hand, the profiles that improve or worsen contractual stability over time. Methodologically, our approach uses a combination of scaling methods to estimate stability scores of each contractual typology and Latent mixture models to cluster similar trajectories. Specifically, the scores of contractual stability were performed by Multidimensional Scaling with individual preferences, taking into account the ordinal nature of distances among contractual typologies and the heterogeneity factors of the subjects. Further, Latent Growth Mixture models, capitalizing the longitudinal property of data sequences, were proposed to identify distinctive, prototypical developmental trajectories of contractual stability within the analyzed population. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Pietro Lovaglio & Mario Mezzanzanica, 2013. "Classification of longitudinal career paths," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 989-1008, February.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:2:p:989-1008
    DOI: 10.1007/s11135-011-9578-y
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    1. Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
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    3. Colombo, Emilio & Mercorio, Fabio & Mezzanzanica, Mario, 2019. "AI meets labor market: Exploring the link between automation and skills," Information Economics and Policy, Elsevier, vol. 47(C), pages 27-37.

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