The Impact of R&D Expenditures on Economic Growth in Türkiye: New Evidence from Machine Learning Method

Abstract

Purpose: This study analyzes the impacts of R&D expenditures on economic growth in Türkiye.
 Methodology: In this study, we explore the impact of R&D expenditure on economic growth in Türkiye. Annual time series from 1990 to 2021 are considered for this research examination based on the data availability. R&D expenditure, Gross Domestic Product (GDP) per capita, gross fixed capital formation, labor force, and tertiary ratio variables are used for the analysis and obtained from the World Bank. Based on machine learning, analyses were conducted using the Kernel Regularized Least Square method.
 Findings: The empirical analysis using KRLS shows that higher spending on research and development leads to a significant boost in economic growth. Furthermore, labor force participation, school enrolment (tertiary) ratio, and gross fixed capital formation are all significantly and positively associated with economic growth in Türkiye.
 Originality: The contribution of the paper is twofold: (1) it provides new scientific evidence based on the machine learning econometric method, the Kernel Regularized Least Square (KRLS); (2) many papers in the literature have only examined the relationship between R&D expenditures and economic growth, without controlling for other variables. We have used possible control variables such as labor force participation rate, school enrolment (tertiary) ratio, and gross fixed capital formation, which are also linked to economic growth models.