Tatyana Rudyanova

Higher education institutions train professional and scientific personnel. Therefore, the quality of higher education and its funding are vital for training highly qualified specialists. This study analyzes the annual volume of expenses (investments) per student in groups of countries, divided according to their socio-economic development, and competitiveness of higher education. The division of countries into groups is based on simultaneous compliance with the criteria for the quality of higher education and the level of social and economic development. The Ward’s clustering method was applied. The analysis was conducted based on data from 32 OECD countries and partner countries. The paper found a significant direct correlation between the level of competitiveness of higher education and the amount of its funding per student (R = 0.895). At the same time, a significant direct correlation was revealed between the level of competitiveness of higher education and the human development index (R = 0.787) and the global competitiveness index (R = 0.888). Finally, a significant direct correlation between the amount of expenditures and the level of competitiveness of higher education was found only in the cluster with the highest indicators of socio-economic development (Rs = 0.707). In other clusters, the correlation is weak or weakly inverse.

The study aims to determine the influence of the R&D expenditure structure funded by different sectors of stakeholders on the level of innovation development. The data sample involves values of GII and R&D expenditure funded by business, government, higher education, private non-profit sectors, and foreign sources for 10 countries – Ukraine and 9 top countries in GII-2022 for 2011–2020. Pearson/Spearman correlation analysis considers time lags to determine the nature and strength of relationships. For GII’s top countries, the relationship with innovation development level is confirmed as direct for funding R&D by business (in 8 from 9 countries), higher education (5 from 7), and foreign sources (5 from 9) with power from moderate to very high and 0-3-year lag. In Ukraine, the direct relationship is for financing by business (very high power and 3-year lag) and foreign sources (high power and 1-year lag). The regression modeling of dependences (Arellano-Bover/Blundell-Bond dynamic model for panel data and linear model for Ukraine) was also applied using STATA 18. In GII’s top countries, increasing the share of R&D expenditures financed by business by 1% contributes to increasing GII’s score by 0.25%, higher education – 2.47%; government, non-profit sector, and foreign sources – decreasing by 0.89%, 1.68% and 0.81% accordingly. In Ukraine, increasing financing R&D by the government by 1% leads to a similar decrease of GII estimate by 0.19% with a 2-year lag, and the business sector – an increase of 0.16% with a 3-year time lag. Vice versa, in Ukraine, R&D expenditures financed by higher education lead to GII’s score decreasing.