Catching The Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning - Isaac Kwesi Ofori (ZBW - Leibniz Information Centre for Economics, 2021)
A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum Schwarz Bayesian Information Criterion (Minimum BIC) lasso, and the Adaptive lasso to study patterns in a dataset comprising 97 covariates of inclusive growth for 43 SSA countries. First, the regularization results show that only 13 variables are key for driving inclusive growth in SSA. Further, the results show that out of the 13, the poverty headcount (US1.90) matters most. Second, the findings reveal that `Minimum BIC lasso' is best for predicting inclusive growth in SSA. Policy recommendations are provided in line with the region's green agenda and the coming into force of the African Continental Free Trade Area.
Keywords: C01; C14; C51; C52; C55; F43; O4; O55; 330; Clean Fuel; Economic Growth; Machine Learning; Lasso; Sub-Saharan Africa; Regularization; Poverty