Human Resources in Europe. Estimation, Clusterization, Machine Learning and Prediction

Angelo Leogrande and Alberto Costantiello ()

MPRA Paper from University Library of Munich, Germany

Abstract: We estimate the relationships between innovation and human resources in Europe using the European Innovation Scoreboard of the European Commission for 36 countries for the period 2010-2019. We perform Panel Data with Fixed Effects, Random Effects, Pooled OLS, Dynamic Panel and WLS. We found that Human resources is positively associated to “Basic-school entrepreneurial education and training”, “Employment MHT manufacturing KIS services”, “Employment share Manufacturing (SD)”, “Lifelong learning”, “New doctorate graduates”, “R&D expenditure business sector”, “R&D expenditure public sector”, “Tertiary education”. Our results also show that “Human Resources” is negatively associated to “Government procurement of advanced technology products”, “Medium and high-tech product exports”, “SMEs innovating in-house”, “Venture capital”. In adjunct we perform a clusterization with k-Means algorithm and we find the presence of three clusters. Clusterization shows the presence of Central and Northern European countries that has higher levels of Human Resources, while Southern and Eastern Europe has very low degree of Human Resources. Finally, we use seven machine learning algorithms to predict the value of Human Resources in Europe Countries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.

Keywords: Innovation and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Technological Change: Choices and Consequences; Diffusion Processes Intellectual Property and Intellectual Capital; Open Innovation; Government Policy. (search for similar items in EconPapers)
JEL-codes: O30 O31 O32 O33 O34 O38 (search for similar items in EconPapers)
Date: 2021-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cse, nep-eec, nep-eur, nep-ino, nep-isf, nep-sbm and nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/109749/1/MPRA_paper_109749.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:109749

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().