Corruption Control, Innovation, Transport Infrastructure, and Economic Development as Determinants of Global Competitiveness: Evidence from a 2017–2019 Panel Analysis

This study investigates the determinants of global competitiveness using panel data from 129 countries between 2017 and 2019. Drawing on institutional economics, endogenous growth theory, and spatial economics, it examines four central drivers: corruption (CPI), innovation (GII), transport infrastructure, and economic development (GNI per capita). Unlike prior research that often considered these factors in isolation, the analysis integrates them within a unified framework while asking how their effects shift across stages of development. A fixed-effects panel model with interaction terms is employed to account for country-specific heterogeneity and to test for conditional relationships. Robustness checks, including multicollinearity diagnostics and alternative specifications, lend support to the stability of the results despite the relatively short time horizon. Findings indicate that infrastructure, innovation, and economic development are significant contributors to competitiveness, although their influence is far from uniform. Advanced economies appear to benefit more from innovation and infrastructure, reflecting stronger absorptive capacity and institutional support, while the role of corruption emerges as inconsistent and context-dependent. For developing economies, basic infrastructure seems to deliver greater returns than innovation-focused strategies, at least in the early stages of growth. A comparison of top- and bottom-ranked countries further highlights these asymmetries. The results suggest that one-size-fits-all prescriptions are unlikely to succeed. Countries that align reform priorities with institutional strength and development stage are more likely to achieve sustainable and inclusive competitiveness. Nonetheless, the short three-year panel constrains the ability to capture longer-term effects, pointing to the need for extended datasets and alternative identification strategies in future research.