The Role of Non-R&D Expenditures in Promoting Innovation in Europe

Angelo Leogrande, Alberto Costantiello () and Lucio Laureti

MPRA Paper from University Library of Munich, Germany

Abstract: In this article we estimate the value of “Non-R&D Innovation Expenditures” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission from the period 2010-2019. We test data with the following econometric models i.e.: Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. We found that “Non-R&D Innovation Expenditures” is positively associated among others to “Innovation Index” and “Firm Investments” and negatively associated among others to “Human Resources” and “Government Procurement of Advanced Technology Products”. We use the k-Means algorithm with either the Silhouette Coefficient and the Elbow Method in a confrontation with the network analysis optimized with the Distance of Manhattan and we find that the optimal number of clusters is four. Furthermore, we propose a confrontation among eight machine learning algorithms to predict the level of “Non-R&D Innovation Expenditures” either with Original Data-OD either with Augmented Data-AD. We found that Gradient Boost Trees Regression is the best predictor for OD while Tree Ensemble Regression is the best Predictor for AD. Finally, we verify that the prediction with AD is more efficient of that with OD with a reduction in the average value of statistical errors equal to 40,50%.

Keywords: Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation. (search for similar items in EconPapers)
JEL-codes: O30 O31 O32 O33 O34 (search for similar items in EconPapers)
Date: 2022-09-11
New Economics Papers: this item is included in nep-big, nep-cse, nep-ind, nep-ino, nep-sbm and nep-tid
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:114526

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