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08.01.2025

Mapping ICT skills demand: a latent transition analysis of online job advertisements in Italy

verfasst von: Francesca Giambona, Adham Kahlawi

Erschienen in: Quality & Quantity

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Abstract

Integrating traditional statistical sources on the labour market with online data availability is challenging as, together with official statistics, online information offers detailed and timely information about the specific skills required by businesses. In the light of this, online job advisements (OJAs) data carried out by Lightcast for Italy for ICT professionals, from the year 2019 to 2022, are used to analyse the evolution of skills required, paying particular attention to skill type and regional detail. From a methodological point of view, a latent class transition analysis is applied. Skills are classified by using as indicator variables the importance of each skill in order to: (i) classify skills into homogenous classes characterised by different levels of importance, (ii) assess whether changes in latent class membership occurred over time, and (iii) evaluate the effect of skill type and region where job is required on the latent status membership and transition probability. Empirical findings uncovered three latent statuses of skills importance from the low to the high intensity. The levels of skill importance remain quite stable over the period considered, but some interesting changes are detected for some skill types and some Italian regions.

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Fußnoten
1
The Lightcast dataset also includes the so-called stacks, that is, skills classified according to criteria chosen by the data producer but not corresponding to national or international classifications. Since they are the result of a classification not shared by the scientific community and in order to contain the possible classification error, stacks have not been included in the dataset used for the purposes of this research.
 
3
For the purpose of this paper we have chosen the model with 3 latent states because it is the one with the lowest value of the classification error and because it allows us a better interpretation of the data. Probably a model with a greater number of states could be better also in order to keep under control the problem of local dependence (measured by the bivariate residuals) but the interpretation of the classes would become more complex. Table A.1 in Appendix provides some measures for the model fit.
 
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Metadaten
Titel
Mapping ICT skills demand: a latent transition analysis of online job advertisements in Italy
verfasst von
Francesca Giambona
Adham Kahlawi
Publikationsdatum
08.01.2025
Verlag
Springer Netherlands
Erschienen in
Quality & Quantity
Print ISSN: 0033-5177
Elektronische ISSN: 1573-7845
DOI
https://doi.org/10.1007/s11135-024-02047-w

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