What Drives Financial Sector Development in Africa? Insights from Machine Learning

Isaac K. Ofori, Christopher Quaidoo and Pamela E. Ofori
Authors registered in the RePEc Author Service: Isaac Kwesi Ofori ()

EconStor Open Access Articles and Book Chapters, 2021, issue forthcoming

Abstract: This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques— the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the Elasticnet are trained based on a dataset containing 86 covariates of financial development for the period 1990 – 2019. The results show that variables such as cell phones, economic globalisation, institutional effectiveness, and literacy are crucial for financial sector development in Africa. Evidence from the Partialing-out lasso instrumental variable regression reveals that while inflation and agricultural sector employment suppress financial sector development, cell phones and institutional effectiveness are remarkable in spurring financial sector development in Africa. Policy recommendations are provided in line with the rise in globalisation, and technological progress in Africa.

Keywords: Africa; Elasticnet; Financial Development; Financial Inclusion; Lasso; Regularization; Variable Selection (search for similar items in EconPapers)
JEL-codes: C01 C14 C52 C53 C55 E5 O55 (search for similar items in EconPapers)
Date: 2021
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https://www.econstor.eu/bitstream/10419/246273/1/F ... e-Learning-Final.pdf (application/pdf)

Related works:
Working Paper: What Drives Financial Sector Development in Africa? Insights from Machine Learning (2021)
Working Paper: What Drives Financial Sector Development in Africa? Insights from Machine Learning (2021)
Working Paper: What Drives Financial Sector Development in Africa? Insights from Machine Learning (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:246273

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