The Innovation Index in Europe

Abstract

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

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

“…There is therefore a positive relationship between the value of the innovation index and the value of exports of medium and high technology products. This relationship is since the growth in the degree of technological innovation at the country level also increases productivity and directs companies to export [19]. • Foreign-controlled enterprises -share of value added: is a measure that takes into consideration the added value of foreign-controlled companies at the cost of factors in millions of euros.…”

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

“…This relationship reflects the overall condition of technological innovation and research and development in a broad sense. In fact, if the systems of scientific research are highly developed then positive effects are generated both in terms of innovation index and in terms of the growth of publications that have a greater number of citations [19].…”

The Exports of Knowledge Intensive Services. A Complex Metric Approach

“…In addition, the countries that have a higher level of the most infection Index are also those that have the greatest possibility of increasing productivity, and generally greater productivity is positively associated with an export orientation. It therefore follows that if the countries want to increase the percentage of services that come to be exported, they must improve the investment in creating a model of virtuous technological innovation [21]. • Sales impacts: is a variable made up of three different sub-variables: "Medium and High Tech product Exports", "Knowledge Intensive Services Exports", "Sales of New or Improved Products".…”