Version 1
: Received: 4 September 2022 / Approved: 5 September 2022 / Online: 5 September 2022 (08:56:35 CEST)
How to cite:
Leogrande, A.; Costantiello, A.; Laureti, L. The Impact of New Doctorate Graduates on Innovation Systems in Europe. Preprints2022, 2022090053. https://doi.org/10.20944/preprints202209.0053.v1
Leogrande, A.; Costantiello, A.; Laureti, L. The Impact of New Doctorate Graduates on Innovation Systems in Europe. Preprints 2022, 2022090053. https://doi.org/10.20944/preprints202209.0053.v1
Leogrande, A.; Costantiello, A.; Laureti, L. The Impact of New Doctorate Graduates on Innovation Systems in Europe. Preprints2022, 2022090053. https://doi.org/10.20944/preprints202209.0053.v1
APA Style
Leogrande, A., Costantiello, A., & Laureti, L. (2022). The Impact of New Doctorate Graduates on Innovation Systems in Europe. Preprints. https://doi.org/10.20944/preprints202209.0053.v1
Chicago/Turabian Style
Leogrande, A., Alberto Costantiello and Lucio Laureti. 2022 "The Impact of New Doctorate Graduates on Innovation Systems in Europe" Preprints. https://doi.org/10.20944/preprints202209.0053.v1
Abstract
In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.
Keywords
Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation.
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.