 Spatio-Temporal Patterns of the Talent Labour Market Across European CountriesCristina Lincaru (),
Speranța Pîrciog,
Adrian Grigorescu and
Gabriela Tudose
Romanian Journal of Regional Science, 2022, vol. 16, issue 2, 18-38 Abstract: The new strategical Europe’s framework shapes the double green and digital transformation. These objectives demand highly specialised and formalised labour markets, with high human capital strongly connected to lifelong learning and highly geographically mobile, in short, demand talents. Frelak et al. (2020) point out that "EU Member States have been less successful than other OECD countries in attracting skilled migrants"(p.13) to fill the talent labour market deficit. Our research question is: are similar or dissimilar talent labour markets at NUTS 0 level? We analyse the talents, as defined by Marie Skłodowska-Curie Actions (MSCA), concerning national labour markets by two main dimensions: a) the work intensity defined by the working programme - full-time or part-time; and b) the institutional employer type - public, private, Ppivate government dependent or private government independent as well as the working relationship (working contract, freelancer, selfemployed, relationship service) described by the type of the employer (public institutions, private institutions, private government dependent institutions, private government independent institutions). The education linked with the employment characteristics analysis approach shapes the roadmap towards a new paradigm shift in skills. The New 2020 European Skills Agenda launched this paradigm for sustainable competitiveness, social fairness, and resilience (COM/2020/274 final). We analyze for the period 2013-2019 the NUTS0 the spatial pattern of the students enrolled in ISCED 8: doctoral or equivalent level by type of institution and intensity of participation in knowledge and innovation economy, with Eurostat data. We apply the Spatio-temporal Analysis method called Spatially Constrained Multivariate Clustering, one of the Similarity checks –Grouping Analysis ARC GIS tool. We evaluate the optimal number of groups - using the Calinski-Harabasz pseudo-F-statistic. Conclusion remarks point that if learning is work in a knowledge economy, then if Europe intends to attract talent has to ensure employment quality for talents: security prevails over flexibility! The successful European model to attract talent has a full-time program work intensity, and public institutional employers prevail. Our opinion is that talent employment policies must be re-designed under the job security need for talents and processes more visible post-December 2019 Covid pandemic. Further research directions are employment contract and work intensity across ERA in Covid times with new tendencies to change the transactional model with the relational model. Keywords: Talents; Geography of knowledge; Similarity check – Grouping Analysis; Spatial statistics. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:rrs:journl:v:16:y:2022:i:2:p:18-38 Access Statistics for this article More articles in Romanian Journal of Regional Science from Romanian Regional Science Association |
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