The Determinants of Lifelong Learning in Europe

Abstract

The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%.

The Export of Medium and High-Tech Products Manufactured in Europe

“…There is a positive relationship between lifelong learning and exports of medium and high technology products. This relationship means that in the countries that export the most, there is also a greater overall orientation towards investment in capital within a large breast, which is precisely captured by the lifelong learning variable [21]. • Basic-school entrepreneurial education and training: It is a variable that considers the presence of entrepreneurship education present in the basic school system of the countries.…”

The Impact of New Doctorate Graduates on Innovation Systems in Europe

“…• Lifelong Learning: is a variable that considers the percentage of the population aged between 25 and 64 who is committed to lifelong learning [30]. It is a variable that considers training and education that are relevant for the acquisition of new skills that can be used in the world of work.…”

K-Means Clusterization and Machine Learning Prediction of European Most Cited Scientific Publications

“…• Human Resources: it is a variable consisting of three sub-variables namely New Doctorate Graduates, Population Aged 25-34 with Tertiary Education, Lifelong Learning [39]. There is a negative relationship between the value of human resources [40] and the value of the most cited scientific publications at international level.…”