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 globalization, 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 globalization, and technological progress in Africa.
| Original language | Undefined/Unknown |
|---|---|
| Pages (from-to) | 2124–2156 |
| Number of pages | 32 |
| Journal | Applied Artificial Intelligence |
| Volume | 35 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - 15 Dec 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- Africa
- Financial development
- machine learning (ML)
- Lasso
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