The Broadband Penetration in Europe

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%.

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The SMEs Innovation in Europe

10.21203/rs.3.rs-1087106/v1 ◽

2021 ◽

Author(s):

Alberto Costantiello ◽

Laureti Lucio ◽

Leogrande Angelo

Keyword(s):

Panel Data ◽

Population Density ◽

Fixed Effects ◽

Government Procurement ◽

Advanced Technology ◽

High Tech ◽

Dynamic Panel ◽

Technology Products ◽

Firm Investments ◽

Intellectual Assets

Abstract In this article we investigate the determinants of SMEs Innovation in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed through Panel Data with Fixed Effects, Random Effects, Dynamic Panel at 1 Stage and WLS. Results show that the presence of Innovators is positively associated with “Enterprise births”, “Government Procurement of Advanced Technology Products”, “Firm Investments”, “Intellectual Assets”, “Sales Impacts”, “Share High and Medium High-Tech Manufacturing” and negatively associated to “FDI Net Inflows” and “Population Density”.

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The Intellectual Assets in Europe

10.21203/rs.3.rs-1051250/v1 ◽

2021 ◽

Author(s):

Costantiello Alberto ◽

Laureti Lucio ◽

Leogrande Angelo

Keyword(s):

Fixed Effects ◽

Government Procurement ◽

Advanced Technology ◽

High Tech ◽

Dynamic Panel ◽

Research Systems ◽

Technology Products ◽

Employment Share ◽

Intellectual Assets ◽

Knowledge Intensive

Abstract In this article we investigate the determinants of the Intellectual Assets in Europe. We use data from the European Innovation Scoreboard of the European Commission in the period 2000-2019 for 36 countries. Data are analyzed using Panel with Fixed Effects, Random Effects, WLS, Pooled OLS, Dynamic Panel at 1 Stage. Results show that the presence of Intellectual Assets in Europe is positively associated with “Enterprise Births”, “Top R&D Spending Enterprises per 10 mln Population”, “Employment Share Manufacturing”, “Share High and Medium high-tech Manufacturing”, “Attractive Research Systems”, “Finance and Support”, “Innovators”, “Sales Impact” and is negatively associated to “Government Procurement of Advanced Technology Products” and “Share Knowledge-Intensive Services”

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The Determinants of Design Applications in Europe

10.21203/rs.3.rs-1051552/v1 ◽

2021 ◽

Author(s):

Angelo Leogrande ◽

Alberto Costantiello ◽

Lucio Laureti ◽

Domenico Leogrande

Keyword(s):

Machine Learning ◽

Panel Data ◽

Fixed Effects ◽

Econometric Models ◽

Machine Learning Algorithms ◽

Dynamic Panel ◽

A Value ◽

Silhouette Coefficient ◽

Firm Investments ◽

Intellectual Assets

Abstract In this article we estimate the level of “Design Application” in 37 European Countries in the period 2010-2019. We use data from the European Innovation Scoreboard-EIS of the European Commission. We perform four econometric models i.e., Pooled OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel. We found that the level of Design Applications is negatively associated to “Enterprise Births”, “Finance and Support”, “Firm Investments” and positively associated with “Venture Capital”, “Turnover share large enterprises”, “R&D expenditure public sector”, “Intellectual Assets”. In adjunct we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we found three different clusters. Finally, we confront eight different machine learning algorithms to predict the level of “Design Application” and we found that the Tree Ensemble is the best predictor with a value for the 30% of the dataset analyzed that is expected to decrease in mean of -12,86%.

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

10.21203/rs.3.rs-910383/v1 ◽

2021 ◽

Author(s):

ANGELO LEOGRANDE ◽

ALBERTO COSTANTIELLO

Keyword(s):

Machine Learning ◽

Human Resources ◽

Fixed Effects ◽

Tertiary Education ◽

Government Procurement ◽

Machine Learning Algorithms ◽

Advanced Technology ◽

High Tech ◽

Technology Products ◽

Using Data

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 has higher levels of Human Resources, while Southern and Eastern Europe has very low degree of Human Resources. Finally, we use seven machine learning algorithms to predict the value of Human Resources in Europe Countries using data in the period 2014-2021 and we show that the linear regression algorithm performs at the highest level.

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The Impact of Venture Capital Expenditures on Innovation in Europe

10.21203/rs.3.rs-936328/v1 ◽

2021 ◽

Author(s):

Angelo Leogrande ◽

ALBERTO COSTANTIELLO ◽

LUCIO LAURETI

Keyword(s):

Panel Data ◽

Venture Capital ◽

Fixed Effects ◽

Government Procurement ◽

Advanced Technology ◽

Capital Expenditures ◽

High Tech ◽

Technology Products ◽

The Impact ◽

The Relationship

Abstract We investigate the relationship between “Venture Capital Expenditures” and innovation in Europe. Data are collected from the European Innovation Scoreboard for 36 countries in the period 2010-2019. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. Results show that the level of Venture Capitalist Expenditure is positively associated to “Foreign Doctorate Students” and “Innovation Index” and negatively related to “Government Procurement of Advanced Technology Products”, “Innovators”, “Medium and High-Tech Products Exports”, “Public-Private Co-Publications”. In adjunct, cluster analysis is realized with the algorithm k-Means and the Silhouette coefficient, and we found the presence of four different clusters for the level of “Venture Capital Expenditures”. Finally, we propose a confrontation among 8 different algorithms of machine learning to predict the level of “Venture Capital Expenditures” and we find that the linear regression generates the best results in terms of minimization of MAE, MSE, RMSE.

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The Corporate Innovation in Europe

10.21203/rs.3.rs-1087099/v1 ◽

2021 ◽

Author(s):

Alberto Costantiello ◽

Lucio Laureti ◽

Leogrande Angelo

Keyword(s):

Research And Development ◽

Tertiary Education ◽

Value Added ◽

Entrepreneurial Activity ◽

High Tech ◽

High Tech Product ◽

Firm Investments ◽

Intellectual Assets ◽

Education Design ◽

Knowledge Intensive

Abstract In this article we investigate the political and industrial determinants of firm investment in Research and Development. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019. We found that firm investments in Research and Development are positively associated with “Linkages”, “Innovation Index”, “International Co-publications”, “Medium and high-tech product exports”, “Non-R&D innovation expenditure”, “Turnover share large enterprises”, “Human Resources”, “Intellectual Assets”. Firm investments in Research and Development are negatively associated to “Foreign doctorate students”, “Knowledge-intensive services exports”, “Private co-funding of public R&D expenditures”, “Basic-school entrepreneurial education and training (SD)”, “New doctorate graduates”, “Trademark applications”, “Tertiary education” “Design applications”, “Lifelong Learning”, “Foreign-controlled enterprises – share of value added (SD)”, “Total Entrepreneurial Activity (TEA) (SD)”.

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The Employment in Innovative Enterprises in Europe

10.21203/rs.3.rs-1221158/v1 ◽

2022 ◽

Author(s):

Lucio Laureti ◽

Costantiello Alberto ◽

Marco Maria Matarrese ◽

Angelo Leogrande

Keyword(s):

Machine Learning ◽

Panel Data ◽

Fixed Effects ◽

Value Added ◽

Machine Learning Algorithms ◽

High Tech ◽

Dynamic Panel ◽

Silhouette Coefficient ◽

High Tech Product ◽

And Training

Abstract In this article we evaluate the determinants of the Employment in Innovative Enterprises in Europe. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019 with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. We found that the “Employment in Innovative Enterprises in Europe” is positively associated with “Broadband Penetration in Europe”, “Foreign Controlled Enterprises Share of Value Added”, “Innovation Index”, “Medium and High-Tech Product Exports” and negatively associated to “Basic School Entrepreneurial Education and Training”, “International Co-Publications”, and “Marketing or Organizational Innovators”. Secondly, we perform a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found the presence of four different clusters. Finally, we perform a comparison among eight different machine learning algorithms to predict the level of “Employment in Innovative Enterprises” in Europe and we found that the Linear Regression is the best predictor.

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The Determinants of Landscape and Cultural Heritage Among Italian Regions in the Period 2004-2019

10.21203/rs.3.rs-1112258/v1 ◽

2021 ◽

Author(s):

Angelo Leogrande ◽

Alberto Costantiello ◽

Laureti Lucio ◽

Domenico Leogrande

Keyword(s):

Panel Data ◽

Cultural Heritage ◽

Fixed Effects ◽

Machine Learning Algorithms ◽

Urban Dispersion ◽

Dynamic Panel ◽

Rural Space ◽

Italian Regions ◽

Level Of Concern ◽

Using Data

Abstract We estimate the Landscape and Cultural Heritage among Italian regions in the period 2004-2019 using data from ISTAT-BES. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. We found that the Landscape and Cultural Heritage is negatively associated with “Dissatisfaction with the landscape of the place of life”, “Illegal building”, “Density and relevance of the museum heritage”, “Internal material consumption”, “Erosion of the rural space due to abandonment”, “Availability of urban green”, and positively associated with “Pressure from mining activities”, “Erosion of the rural space by urban dispersion”, “Concern about the deterioration of the landscape”, “Diffusion of agritourism farms”, “Current expenditure of the Municipalities for culture”. Secondly, we have realized a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found two clusters in the sense of “Concern about the deterioration of the landscape”. Finally, we use eight different machine learning algorithms to predict the level of “Concern about the deterioration of the landscape” and we found that the Tree Ensemble Regression is the best predictor.

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