Analyzing the influence of educational quality and socioeconomic factors on Finland's economic growth : a machine learning approach
Nguyên, Mia (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024121636496
https://urn.fi/URN:NBN:fi:amk-2024121636496
Tiivistelmä
This study investigates the influence of educational quality and socioeconomic factors on Finland’s economic growth from 1990 to 2023. The research addresses the relationship between these variables and explores their combined impact on GDP growth.
To analyze the complex dynamics of economic growth, machine learning models such as Random Forest, XGBoost, and logistic regression were employed. These models allowed for the identification of key predictors and the examination of nonlinear relationships among variables.
The findings highlight that Finland’s high-quality education system, characterized by strong educational attainment and significant R&D investment, contributes substantially to economic stability and growth. Additionally, socioeconomic factors—such as labor force participation, unemployment, and internet penetration—play a critical role in influencing GDP growth. Population growth and digitalization emerged as particularly influential predictors, showcasing the importance of combining educational and socioeconomic strategies to drive economic performance.
This research underscores the need for integrated policies that support both education and socioeconomic development to sustain long-term economic growth. The study also demonstrates the potential of machine learning methods for uncovering complex, nonlinear relationships in economic analysis. Future research should incorporate institutional quality indicators and expand the analysis to other high-achieving countries to validate and generalize the findings.
To analyze the complex dynamics of economic growth, machine learning models such as Random Forest, XGBoost, and logistic regression were employed. These models allowed for the identification of key predictors and the examination of nonlinear relationships among variables.
The findings highlight that Finland’s high-quality education system, characterized by strong educational attainment and significant R&D investment, contributes substantially to economic stability and growth. Additionally, socioeconomic factors—such as labor force participation, unemployment, and internet penetration—play a critical role in influencing GDP growth. Population growth and digitalization emerged as particularly influential predictors, showcasing the importance of combining educational and socioeconomic strategies to drive economic performance.
This research underscores the need for integrated policies that support both education and socioeconomic development to sustain long-term economic growth. The study also demonstrates the potential of machine learning methods for uncovering complex, nonlinear relationships in economic analysis. Future research should incorporate institutional quality indicators and expand the analysis to other high-achieving countries to validate and generalize the findings.