Title: The use of classification models to identify factors differentiating the competitiveness of the EU-15 and EU-13 countries

Authors: Agnieszka Kleszcz

Addresses: Faculty of Natural Sciences, Jan Kochanowski University of Kielce, Kielce, Poland

Abstract: This paper reports on a study of the Global Competitiveness Index pillars, aiming to differentiate the European Union countries grouped by their accession year in terms of their competitiveness. A linear (regularised logistic regression) and nonlinear (random forests) classifiers are proposed, to model the relationship between multidimensional economic condition indicators and the country's group. The key discriminators of the competitiveness of the EU-15 (accession before 2004) and the EU-13 (accession in or after 2004) are obtained by analysis of feature importance in classification models. Upon study of 12 competitive indicators from the World Economic Reports (2007-2017 edition) we conclude that the highest disparities between the groups of countries can be observed in infrastructure. Innovation, market size and institutions are the next three most important differentiating factors. A major methodological contribution of the paper is the use of explainable statistical models for identifying key features differentiating groups of countries.

Keywords: logistic regression; random forest; European Union; Global Competitiveness Index; GCI; feature importance.

DOI: 10.1504/IJCEE.2023.127296

International Journal of Computational Economics and Econometrics, 2023 Vol.13 No.1, pp.110 - 128

Received: 14 Apr 2021
Accepted: 19 Oct 2021
Published online: 30 Nov 2022 *