Catching The Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning

Isaac Kwesi Ofori

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

Abstract: 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 (US$1.90) 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: Clean Fuel; Economic Growth; Machine Learning; Lasso; Sub-Saharan Africa; Regularization; Poverty (search for similar items in EconPapers)
JEL-codes: C01 C14 C51 C52 C55 F43 O4 O55 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-afr, nep-big and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/235482/1/O ... Machine-Learning.pdf (application/pdf)

Related works:
Working Paper: Catching the Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning (2021)
Working Paper: Catching the Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning (2021)
Working Paper: Catching the Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning (2021)
Working Paper: Catching The Drivers of Inclusive Growth In Sub-Saharan Africa: An Application of Machine Learning (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:235482

Access Statistics for this paper

More papers in EconStor Preprints from ZBW - Leibniz Information Centre for Economics Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().