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

ABSTRACT: We estimate the relationships between innovation and Human Resources in Europe using theEuropean Innovation Scoreboard of the European Commission for 36 countries for the period 2010-2019. Weperform Panel Data with Fixed Effects, Random Effects, Pooled OLS, Dynamic Panel and WLS. We found thatHuman Resources is positively associated to “Basic-school entrepreneurial education and training”,“Employment MHT manufacturing KIS services”, “Employment share Manufacturing (SD)”, “Lifelonglearning”, “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“Governmentprocurement of advanced technology products”, “Medium and high-tech product exports”, “SMEs innovatingin-house”, “Venture capital”. In adjunct we perform a clusterization with k-Means algorithm and we find thepresence of three clusters. Clusterization shows the presence of Central and Northern European countries thathave higher levels of Human Resources, while Southern and Eastern European countries have very low degreeof Human Resources. Finally, we use seven machine learningalgorithms to predict the value of HumanResources in Europeancountries using data in the period 2014-2021 and we show that the linear regressionalgorithm performs at the highest level.JEL CODE: O31, O32, O34, O36, O38.

Keywords:Innovation and Invention: Processes and Incentives, Management of Technological Innovation andR&D, Technological Change: Choices and Consequences, Diffusion Processes Intellectual Property andIntellectual Capital, Open Innovation, Government Policy.