The relationship between knowledge-based economies and economic growth: an empirical analysis on the Asia-Pacific region 2011–2018

Purpose This paper aims to examine the relationship between knowledge-economy and economic growth in 16 Asia-Pacific (AP) countries during the period 2011–2018. The study also aims to investigate a diversity of knowledge-economy pillars, including tertiary education, domestic innovation, foreign innovation, economic incentives and institutional regime and information and communications technologies (ICTs) and their relation to economic growth. Design/methodology/approach The study applies a comparative empirical analysis using pooled ordinary least squares (OLS), one-step difference generalised methods of moments (GMM) and bias-corrected least-squares dummy variables (LSDVc) estimators to test this relationship. Findings Pooled OLS estimators deemed suboptimal to the panel data under study, while GMM results reveal a significant relationship between tertiary education, domestic and foreign innovation, government expenditure and investments with economic growth. Of these results, domestic innovation, investments and government consumption are positively correlated with economic growth, whereas tertiary education and foreign innovation show a negative relation. Meanwhile, institutions and ICT have insignificant relationships with economic growth. LSDVc results coincide with GMM results with respect to tertiary education, whereas institutions is the only additional significant and negatively correlated variable with economic growth. Research limitations/implications The main limitation of this research lies in the unavailability of proxy data for knowledge economy pillars in monetary terms, and hence, the paper relies on indices. Originality/value The novelty of the study lies in its aim to investigate economic growth in the AP region that is enhanced by domestic innovation, foreign innovation or both – an area which is empirically understudied in the knowledge-economy context. Further, the paper’s novelty lies in its application of a comparative empirical analysis between the most popular dynamic panel estimators – dynamic GMM and bias-corrected LSDVc for AP countries.