Ralph Sonntag

The purpose of the paper is to determine the causal relationship between financial and investment indicators and the level of innovation development in GII leading countries and Ukraine. For a sample of 10 leaders in GII-2022 and Ukraine for 2011–2020, a correlation analysis was conducted based on the following indicators: the value of GII, foreign direct investment (net inflows), domestic credit to the private sector, ease of getting credit, protecting minority investors, and real interest rate. A positive relationship (with moderate/high strength) between innovation development and foreign direct investment has been proven in 7 out of 11 countries with a time lag of 0-2 years; domestic credit to the private sector – in 6 countries (lag of 0-3 years); and protecting minority investors – in 9 countries (lag of 0-2 years). For other indicators, the relationship is negative. Through VAR-modelling and Granger test, it is proven that the change in the value of foreign direct investment causes the change in the value of GII in 6 countries (bidirectional causality exists only in Ukraine); domestic credit to the private sector – in 6 countries, protecting minority investors and real interest rate – in 2 countries, and ease of getting credit – only in Switzerland. The results show that foreign direct investment and domestic credit to the private sector are the reasons for increasing the level of innovation development and have potentially the highest influence. In Ukraine, compared to GII leaders, only the factor of foreign direct investment is identified as a cause of innovation development.

The study aims to test whether increasing a share of R&D expenditure in GDP strengthens the level of innovation development in Ukraine compared with top countries in the Global Innovation Index. It models the impact of changing a share of R&D expenditure in GDP on the level of innovation development based on 10 countries-leaders in GII 2022 and Ukraine. Correlation analysis proved the existence of a relationship between the levels of R&D expenditure (as percent of GDP) and innovation development (the overall score of GII); its strength and direction are characterized (for 2011–2020). The results show that in GII’s top countries, the relationship between innovation development and R&D expenditure is direct in 70% of the sample’s countries, mostly with high and very high relationship power without time lag or 1-2-year time lag. This relationship is inverse in Ukraine, with high relationship power and a 1-year time lag. The system dynamic linear panel-data model is built to determine and formalize the impact of changing a share of R&D expenditure in GDP on the level of innovation development for GII’s top countries and the linear regression model – for Ukraine. For GII’s top countries, it is confirmed that with an increase in R&D expenditures by 1%, innovation development potentially increases by an average of 2.71%, and in Ukraine – it decreases by an average of 4.8%. This discrepancy is explained by the need to improve state policy and regulatory framework in innovation development and its financing in Ukraine.

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.

Knowledge-based economy causes changes in the higher education system: university graduates must have the ability to constantly learn and improve their skills, generate and disseminate new knowledge, form and multiply the knowledge capital of business. This paper aims to investigate a pairwise interconnection between higher education indicators and sets of parameters characterizing knowledge creation, impact, and diffusion. The following higher education indicators are used: expenditure on education, tertiary enrollment, graduates in science and engineering, tertiary inbound mobility, researcher, gross expenditure on R&D, top 3 global corporate R&D investors, top 3QS university ranking. Knowledge creation indicators are patents by origin, PCT patents by origin, utility models by origin, scientific and technical articles, citable documents, H-index. Knowledge impact is characterized through labor productivity growth, new businesses, software spending, ISO 9001 quality certificates, high-tech manufacturing. Knowledge diffusion parameters include intellectual property receipts, production and export complexity, high-tech exports, ICT services exports. The information base of the study is the data of the Global Innovation Index Report from the World Intellectual Property Organization for 40 European countries (selected depending on the availability of statistics) for 2022, research method – Canonical Correlation Analysis. The strongest positive correlation was found between higher education indicators and knowledge creation parameters. The second position takes connection between higher education indicators and knowledge diffusion parameters, the third – between higher education indicators and knowledge impact indicators. Among the higher education indicators, the most significant were gross expenditure on R&D, top 3 global corporate R&D investors, top 3 QS university ranking.