Changes in the Global Competitiveness Index 4.0 Methodology

Magdalena Olczyk, Marta Kuc-Czarnecka, Andrea Saltelli

Changes in the Global Competitiveness Index 4.0 Methodology

Číslo: 1/2022
Periodikum: Journal of Competitiveness
DOI: 10.7441/joc.2022.01.07

Klíčová slova: competitiveness, composite indicators, Global Competitiveness Index, sensitivity analysis

Pro získání musíte mít účet v Citace PRO.

Přečíst po přihlášení

Anotace: The Global Competitiveness Index (GCI) developed by the World Economic Forum (WEF) is used as a standard for measuring a country’s competitiveness. However, in literature, the GCI has been accused of numerous methodological flaws. Consequently, in 2018, the WEF introduced significant methodological changes. This study aims to examine whether the methodological modifications in the GCI’s structure increase its ability to capture the real competitiveness of economies. In addition, the study considers whether the selection of weights of individual elements included in the GCI is optimal or could be improved. By employing a sensitivity-based analysis, we find that the change in methodology resulted in fewer pillars of marginal importance. In the case of the GCI 2017, there were four pillars, whereas in that of the GCI 4.0, there were only two pillars: product market and labor market. Furthermore, we reveal that the WEF weights do not reflect the measured importance of the variables. In the optimization process, numerous variables (primarily opinion-based indicators) were insignificant in explaining the GCI variance and could be eliminated from the set of diagnostic variables without affecting the index’s value. For instance, in the case of the GCI 4.0, 35 out of 103 variables could be eliminated. The new rankings obtained by weight optimization and reduction of the diagnostic variables demonstrated a strong positive correlation with the original rankings. This research contributes to the literature from both a theoretical perspective (indicating the most vital indicators in the GCI) and a practical standpoint (reducing the costs and time of obtaining redundant data).