Predicting the Effect of Foreign Direct Investment on Macroeconomic Indicators in Somalia using Decision Trees and Generalized Additive Models

This study investigates the influence of Foreign Direct Investment (FDI) on Somalia’s key macroeconomic indicators, specifically Gross Domestic Product (GDP), unemployment, and inflation, using Decision Trees and Generalized Additive Models (GAMs) to capture both linear and nonlinear dynamics. The research employs 32 years of secondary data (1991–2023) from the World Bank Development Indicators. Decision Tree analysis identified critical FDI thresholds influencing unemployment and GDP, with the model showing strong predictive accuracy for unemployment (R² = 0.851) and GDP (R² = 0.837), while performance was weaker for inflation (R² = 0.780). GAMs revealed a significant nonlinear effect of FDI on inflation (pseudo-R² = 0.401), suggesting that changes in investment influence price levels in a non-straightforward manner, whereas the effects on GDP and unemployment were modest. Model comparison indicated that Decision Trees better capture segmented patterns in unemployment, whereas GAMs are more effective at modeling smooth nonlinear trends in inflation. Forecasts for the next ten years predict steady growth in FDI and GDP, a gradual rise in unemployment, and a decline in inflation, highlighting potential areas for policy intervention. The findings support targeted FDI policies, particularly toward labor-intensive sectors, and emphasize the importance of selecting appropriate predictive models to inform macroeconomic planning. Overall, the study demonstrates that FDI can be leveraged to improve Somalia’s economic stability, provided that threshold effects and nonlinear interactions are carefully considered.