In this article we estimate the determinants of broadband penetration in Europe. We use data from the European Innovation Scoreboard of the European Commission for 37 countries in the period 2010-2019. We apply Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, OLS and Dynamic Panel. We found that the level of “Broadband Penetration” in Europe is positively associated to “Enterprises Providing ICT Training”, “Innovative Sales Share”, “Intellectual Assets”, “Knowledge-Intensive Service Exports”, “Turnover Share SMEs”, “Innovation Friendly Environment” and negatively associated with “Government procurement of advanced technology products”, “Sales Impact”, “Firm Investments”, “Opportunity-Driven Entrepreneurship”, “Most Cited Publications”, “Rule of Law”. In adjunct we perform a clusterization with k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of three different clusters. Finally, we apply eight machine learning algorithms to predict the level of “Broadband Penetration” in Europe and we find that the Polynomial Regression algorithm is the best predictor and that the level of the variable is expected to increase of 10,4%.

The Broadband Penetration in Europe

Alberto Costantiello
;
Lucio Laureti
2021

Abstract

In this article we estimate the determinants of broadband penetration in Europe. We use data from the European Innovation Scoreboard of the European Commission for 37 countries in the period 2010-2019. We apply Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, OLS and Dynamic Panel. We found that the level of “Broadband Penetration” in Europe is positively associated to “Enterprises Providing ICT Training”, “Innovative Sales Share”, “Intellectual Assets”, “Knowledge-Intensive Service Exports”, “Turnover Share SMEs”, “Innovation Friendly Environment” and negatively associated with “Government procurement of advanced technology products”, “Sales Impact”, “Firm Investments”, “Opportunity-Driven Entrepreneurship”, “Most Cited Publications”, “Rule of Law”. In adjunct we perform a clusterization with k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of three different clusters. Finally, we apply eight machine learning algorithms to predict the level of “Broadband Penetration” in Europe and we find that the Polynomial Regression algorithm is the best predictor and that the level of the variable is expected to increase of 10,4%.
: General; Innovation and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Technological Change: Choices and Consequences; Intellectual Property and Intellectual Capital.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12572/7225
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