Variances of the Impact of Meso-Economic Factors on Global Competitiveness

Abstract

This study explored the variances of the impact of meso-economic actors of global competitiveness through cluster analysis and revealed statistical modeling of the combinations of these actors. The World Economic Forum’s Global Competitiveness Index (GCI) of 2017 was used to cluster the countries according to meso-economic features of their global competitiveness corresponding to variables such as innovation, technological readiness, higher education and skills, and health and primary education. The findings generated four clusters of countries where countries belonging to cluster 3 have favorable characteristics having very high values in the four variables. When innovation and technological readiness were combined, each to the two education actors (higher education and skills, and health and primary education), the combination of innovation and the two education actors yielded an ideal model as characterized by countries belonging to cluster 2 compared to the combination of technological readiness and the two education actors. The model generated by countries in cluster 2 indicated that as innovation and the quality of education increase, the competitiveness of countries also increases. The regression model in cluster 2 can be explained by the 26.70% of the variances of the data. The combination of education and innovation contributes to the countries’ global competitiveness in cluster 2 than when technological readiness and education are combined. Technological readiness can only impact to the global competitiveness of countries as a whole when education translates its use into innovation. Unless the education sector will use technology more than a tool to drive teaching and learning transforming its output to innovation, only then will it have significant impact to the global competitiveness.