<![CDATA[This study investigates the influence of corruption control, economic growth, trade openness, inflation, and unemployment on Foreign Direct Investment (FDI) inflows across countries, utilizing secondary panel data from the World Bank. The research employs both traditional panel regression techniques Pooled Ordinary Least Squares (POLS), Fixed Effects Model (FEM), and Random Effects Model (REM) along with a Random Forest machine learning algorithm to determine variable importance and validate robustness. Empirical findings reveal that corruption control significantly and positively impacts FDI inflows, suggesting that institutional integrity and governance transparency are vital in attracting foreign capital. Economic growth and trade openness also show consistent positive effects on FDI, reaffirming their roles as indicators of market potential and international integration. Inflation and unemployment demonstrate more nuance influences, with their effects varying by model and context. Furthermore, Random Forest analysis highlights corruption control, economic growth, and trade openness as the most critical variables affecting FDI. This methodological innovation contributes to the literature by combining econometric analysis with data-driven learning algorithms for deeper insight. The study provides practical recommendations for policymakers, emphasizing the importance of anti-corruption frameworks, stable macroeconomic policies, and liberal trade systems to enhance foreign investment attractiveness. These findings are valuable for governments, investors, and researchers aiming to understand and improve the determinants of FDI in emerging and developing economies.]]>
Copyrights © 2026
