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 have higher levels of Human Resources, while Southern and Eastern European countries have very low degree of Human Resources. Finally, we use seven machine learningalgorithms to predict the value of Human Resources in Europeancountries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.

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

Alberto Costantiello
2021

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 have higher levels of Human Resources, while Southern and Eastern European countries have very low degree of Human Resources. Finally, we use seven machine learningalgorithms to predict the value of Human Resources in Europeancountries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.
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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12572/7221
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