COVID-19 Containment Measures and Loan Default in Sub-Saharan Africa: Evidence from South Africa, Kenya, Tanzania, and Burundi
Description
This dataset accompanies the empirical study examining how COVID-19 containment measures affected banking sector stability and loan default dynamics across four Sub-Saharan African countries—South Africa, Kenya, Tanzania, and Burundi—from 2018 to 2022. The data integrate macroeconomic indicators, financial stability variables, and COVID-19 policy indices to analyze the link between pandemic stringency and changes in non-performing loans (NPLs). Using panel regression and instrumental variable techniques, the dataset captures country-level annual observations combining publicly available macro-financial statistics with COVID-19 response indicators. The variables include the Change in NPL Ratios (ΔNPL), COVID-19 Stringency Index, GDP Growth Rate, and Inflation Rate. The dataset supports cross-country and temporal analysis of credit risk transmission under pandemic-related shocks. This dataset serves as a unique contribution to pandemic-finance research in Africa, providing an integrated resource for analyzing credit risk, financial stability, and policy responses during systemic crises.
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ChatGPT said: This dataset underpins the study “COVID-19 Containment Measures and Loan Default in Sub-Saharan Africa: Evidence from South Africa, Kenya, Tanzania, and Burundi”, examining how pandemic policies affected banking sector stability between 2018 and 2022. It integrates macroeconomic, financial, and COVID-19 policy data to analyze the relationship between containment stringency and non-performing loan (NPL) dynamics across the four countries. The dataset includes annual country-level observations of Change in NPL Ratios (ΔNPL), COVID-19 Stringency Index, GDP Growth, and Inflation, sourced from the IMF (2023), World Bank (2023), Oxford COVID-19 Government Response Tracker (Hale et al., 2021), and national central banks (SARB, CBK, BoT, BRB). Data were compiled by calculating yearly NPL changes, averaging daily stringency scores, and merging macroeconomic controls from official databases. The analysis applied panel regression and Two-Stage Least Squares (2SLS) estimation to address endogeneity, with instruments including international travel exposure, first confirmed case timing, and regional policy spillovers. Derived datasets include Average Stringency vs. NPL Ratios (2020–2021) and country-specific regression summaries. The results reveal that stricter containment policies correlated with higher short-term credit stress, moderated by fiscal and institutional factors. South Africa and Kenya showed stronger linkages, while Tanzania and Burundi exhibited muted effects due to limited lockdowns and data transparency. The dataset provides reproducible, policy-relevant insights into financial stability under systemic shocks and supports further research on the Financial Accelerator, Credit Channel, and Procyclicality of Credit Risk frameworks in developing economies.
Institutions
- Jomo Kenyatta University of Agriculture and Technology