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The Influence of ESG Factors on Sovereign Credit Ratings in Sub-Saharan Africa: A LASSO and Random Forest Approach

DOI: 10.4236/oalib.1113346, PP. 1-22

Subject Areas: Development Economics, Risk Management, Business Finance and Investment, Business Statistics and Analytics, Business Analysis, Strategic Management Policy, Financial Reporting, Corporate Governance, Managerial Economics, Finance, Business and Economics Education, Statistics and Econometrics

Keywords: ESG Factors, Sovereign Credit Ratings, Random Forest, Macroeconomic Variables, Sub-Saharan Africa

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Abstract

This study examines the impact of Environmental, Social, and Governance (ESG) factors on sovereign credit ratings in Sub-Saharan African countries using LASSO and Random Forest models. Sovereign credit ratings, issued by major rating agencies, play a crucial role in determining a country’s borrowing costs and investment attractiveness. Traditionally, these ratings have been influenced by macroeconomic factors; however, recent trends suggest that ESG considerations are becoming increasingly relevant. By applying statistical prediction models, this study assesses the relative importance of ESG factors versus non-ESG (macroeconomic) variables in credit rating determination. The results reveal that governance and macroeconomic factors are the dominant determinants of sovereign credit ratings in upper-middle-income Sub-Saharan African countries, whereas environmental factors play a crucial role in lower-income nations. The Random Forest model outperformed the LASSO regression in predictive accuracy, reinforcing the significance of non-linear relationships in credit rating determinants. The findings highlight the necessity for policymakers to integrate ESG considerations into national financial and economic strategies to improve creditworthiness and attract sustainable investments.

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Ishimwe, M. , Niyonshuti, M. , Su, M. , Mintah, C. , Nketiah, E. A. and Garnet, E. (2025). The Influence of ESG Factors on Sovereign Credit Ratings in Sub-Saharan Africa: A LASSO and Random Forest Approach. Open Access Library Journal, 12, e3346. doi: http://dx.doi.org/10.4236/oalib.1113346.

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