Statistical and machine learning approaches to assessing foreign aid effectiveness in Kenya: an ARDL framework

Abstract

This study investigates the impact of foreign aid and other macroeconomic factors on household consumption in Kenya, using household consumption as a proxy for poverty. It adopts a hybrid methodological approach, combining traditional econometric modelling with modern machine modelling techniques to balance causal inference with predictive accuracy. The analysis is anchored in the Autoregressive Distributed Lag (ARDL) framework, which is well-suited to small samples and mixed integration orders. After establishing the presence of cointegration among variables, the model is reparameterised into an Error Correction Model (ECM) to distinguish between short-run and long-run effects. Diagnostic tests confirm the model’s robustness. A Granger causality test reveals no temporal precedence from foreign aid to household consumption, while GDP per capita consistently emerges as a significant long-run driver. To complement the explanatory power of ARDL, three machine learning models, LASSO regression, Random Forest, and XGBoost, are implemented to assess their ability to predict changes in household consumption. The LASSO model demonstrates the best performance across all evaluation metrics (MAE, RMSE, R2), outperforming traditional ARDL and more complex ML models. Feature importance analyses using permutation importance and SHAP values reinforce the dominance of GDP per capita and lagged effects of foreign aid as key predictors. Findings indicate that while econometric methods offer nuanced insight into short- and long-term dynamics, machine learning provides superior predictive power. The study underscores the potential of a hybrid modelling approach in low-frequency macroeconomic contexts, where data constraints limit the application of purely data-hungry methods. Ultimately, the results contribute to the discussion on how aid and macroeconomic variables influence poverty outcomes in developing economies.