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Spatio-Temporal Patterns of the Talent Labour Market Across European Countries

Author

Listed:
  • Cristina Lincaru

    (National Scientific Research Institute for Labour and Social Protection, INCSMPS, Romania)

  • SperanÈ›a Pîrciog

    (National Scientific Research Institute for Labour and Social Protection, INCSMPS, Romania)

  • Adrian Grigorescu

    (National University of Political Studies and Public Administration, Romania)

  • Gabriela Tudose

    (National Scientific Research Institute for Labour and Social Protection, INCSMPS, Romania)

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.

Suggested Citation

  • Cristina Lincaru & SperanÈ›a Pîrciog & Adrian Grigorescu & Gabriela Tudose, 2022. "Spatio-Temporal Patterns of the Talent Labour Market Across European Countries," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 16(2), pages 18-38, DECEMBER.
  • Handle: RePEc:rrs:journl:v:16:y:2022:i:2:p:18-38
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    References listed on IDEAS

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    1. Simón Sánchez-Moral & Alfonso Arellano & Roberto Díez-Pisonero, 2018. "Interregional mobility of talent in Spain: The role of job opportunities and qualities of places during the recent economic crisis," Environment and Planning A, , vol. 50(4), pages 789-808, June.
    2. Yevgeny Kuznetsov & Charles F. Sabel, 2006. "Global Mobility of Talent from a Perspective of New Industrial Policy: Open Migration Chains and Diaspora Networks," WIDER Working Paper Series RP2006-144, World Institute for Development Economic Research (UNU-WIDER).
    3. Hannes Hauswald & Andreas Hack & Franz W. Kellermanns & Holger Patzelt, 2016. "Attracting New Talent to Family Firms: Who is Attracted and under what Conditions?," Entrepreneurship Theory and Practice, , vol. 40(5), pages 963-989, September.
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    More about this item

    Keywords

    Talents; Geography of knowledge; Similarity check – Grouping Analysis; Spatial statistics.;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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