COVID 19 and Bank Profitability in Low Income Countries: The Case of Uganda
Bibliographic record
Abstract
This study investigates the impact of the COVID-19 pandemic on banking sector profitability in Uganda for the period spanning Q1 2000 to Q1 2021, using the autoregressive distributed lag (ARDL Bound) testing approach to co-integration while controlling for bank specific and macroeconomic determinants of bank profitability. Bank profitability is proxied by return on assets (ROA), return on equity (ROE), and net interest margin (NIM). The study finds that the COVID 19 pandemic has a significant negative effect on bank profitability only in the long run. Generally, the explanatory variables used in the study have short run and long run effects on bank profitability, although the impact is not uniform across the different measures of bank profitability. In the short run, bank profitability is generally negatively and significantly affected by the non-performing loans ratio, liquidity ratio, and market sensitivity risk, while the Treasury Bill interest rate and lending rate have a significant positive effect on bank profitability. In addition, the study finds that bank profitability has a tendency to persist in the short run, although persistence is only moderate, suggesting that the Ugandan banking sector may not have large deviations from a perfectly competitive market structure. In the long run, bank profitability is broadly positively and significantly affected by the non-performing loan ratio;, real GDP, lending rate and Treasury Bill interest rate while market sensitivity risk and the exchange rate significantly and negatively affect bank profitability. Surprisingly, the study finds inflation does not significantly affect bank profitability over both the short- and long-term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".