Abstract
The question of what really drives economic growth in sub-Saharan Africa (SSA) has been debated for many decades now. However, there is still a lack of clarity on the variables crucial for driving growth as prior contributions have been executed at the backdrop of preferential selection of covariates in the midst of several potential drivers of economic growth. The main challenge with such contributions is that even tenuous variables may be deemed influential under some model specifications and assumptions. To address this and inform policy appropriately, we train algorithms for four machine learning regularization techniques- the Standard lasso, the Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the ElasticNet- to study patterns in a dataset containing 113 covariates and identify the key variables affecting growth in SSA. We find that only 7 covariates are key for driving growth in SSA. The estimates of these variables are provided by running the lasso inferential techniques of double-selection linear regression, partialing-out lasso linear regression, and partialing-out lasso instrumental variable regression. Policy recommendations are also provided in line with the AfCFTA and the green growth agenda of the region.