Relationship Between Innovation and Economic Growth: A Study on EU Countries

Wish Gumbeze

Master's thesis

Abstract:

This master's thesis delves into the intricate relationship between innovation and economic growth within the European Union (EU). It centers on examining the interplay among patents (PAT), Gross Domestic Product Growth (GDP), and scientific articles (SCIJ). Employing various statistical methods such as panel regression models, fixed and random effects models, the Hausman test, and the Generalized Method of Moments (GMM), the empirical analysis extends from 2003 to 2020. Organized into three chapters, the study begins with a theoretical evaluation of innovation and economic growth, progressing to scrutinize the innovation system in EU countries. The third chapter unveils empirical research findings gleaned from data originating from 24 EU countries, illuminating the intricate dynamics between innovation and economic growth. Summary statistics offer insights into the distribution and characteristics of variables, highlighting variability and dependencies across different cross-sectional units. The correlation matrix uncovers the strength and direction of relationships among variables, emphasizing both positive and negative associations. The cross-sectional dependence (CD) test accentuates the interconnectedness among countries by rejecting the null hypothesis of independence for all variables. Significant insights into the relationship between GDP growth and independent variables are provided by Pooled OLS regression analysis and Random-Effects (GLS) model results, showcasing both positive and negative associations. Although the Generalized Method of Moments (GMM) models delve deeper into these relationships, concerns arise over the reliability of estimates due to an undefined standard error. The findings underscore the significance of balanced innovation policies and coordinated international efforts in fostering sustainable economic development.

Abstract:

This master's thesis delves into the intricate relationship between innovation and economic growth within the European Union (EU). It centers on examining the interplay among patents (PAT), Gross Domestic Product Growth (GDP), and scientific articles (SCIJ). Employing various statistical methods such as panel regression models, fixed and random effects models, the Hausman test, and the Generalized Method of Moments (GMM), the empirical analysis extends from 2003 to 2020. Organized into three chapters, the study begins with a theoretical evaluation of innovation and economic growth, progressing to scrutinize the innovation system in EU countries. The third chapter unveils empirical research findings gleaned from data originating from 24 EU countries, illuminating the intricate dynamics between innovation and economic growth. Summary statistics offer insights into the distribution and characteristics of variables, highlighting variability and dependencies across different cross-sectional units. The correlation matrix uncovers the strength and direction of relationships among variables, emphasizing both positive and negative associations. The cross-sectional dependence (CD) test accentuates the interconnectedness among countries by rejecting the null hypothesis of independence for all variables. Significant insights into the relationship between GDP growth and independent variables are provided by Pooled OLS regression analysis and Random-Effects (GLS) model results, showcasing both positive and negative associations. Although the Generalized Method of Moments (GMM) models delve deeper into these relationships, concerns arise over the reliability of estimates due to an undefined standard error. The findings underscore the significance of balanced innovation policies and coordinated international efforts in fostering sustainable economic development.

Keywords

Innovation, European Union, Economic growth

Keywords

Economic growth, Innovation, European Union

Language used: English

Date on which the thesis was submitted / produced: 11. 3. 2024

Thesis defence

• Date of defence: 12. 6. 2024
• Supervisor: Karel Brůna
• Reader: Kateřina Šímová

Vysoká škola ekonomická v Praze

Master programme:
Finance and Accounting