Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/244219 
Autor:innen: 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
AGDI Working Paper No. WP/21/044
Verlag: 
African Governance and Development Institute (AGDI), Yaoundé
Zusammenfassung: 
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.
Schlagwörter: 
Clean Fuel
Economic Growth
Machine Learning
Lasso
Sub-Saharan Africa
Regularization
Poverty
JEL: 
C01
C14
C51
C52
C55
F43
O4
O55
Dokumentart: 
Working Paper

Datei(en):
Datei
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