Evaluating the Economic Impacts of the G20 Compact Initiative: Evidence from Causal Inference Using Advanced Machine Learning Techniques

The G20 Compact with Africa (CwA) initiative, launched in 2017 under the German G20 Presidency, aims to enhance the attractiveness of private investment in Africa by improving member countries’ macro, business, and financing frameworks. This study evaluates the CwA initiative's impact on FDI, GDP per capita, gross capital formation, exports, and employment using targeted maximum likelihood estimation. In the initial Q model, we employed machine learning models like Random Forest, Gradient Boosting, and XGBoost to estimate the outcome given the covariates. Subsequently, we used OLS to update the initial estimate through the clever covariate to improve the efficiency and accuracy of the estimated treatment effect. Our findings indicate that the CwA initiative is significantly associated with increased FDI and export growth in member countries, but these gains have not yet led to broader economic growth, such as improvements in gross capital formation and GDP per capita.