Determinants of Interest Rate Spread: Empirical Evidence from Uganda’s Banking Sector
Bibliographic record
Abstract
Interest rate spreads are one of the main indicators of banking sector efficiency as well as economic growth. Uganda has one of the highest interest rate spread in Sub-Saharan Africa (SSA). However, there is limited understanding of the key drivers of these high interest rate spread in Uganda. In this study we provide new evidence on the underlying causes of interest rate spread in Uganda. To explore the drivers of interest rate spread in Uganda’s banking sector, we use a novel quarterly bank-level data for the period 2008 to 2022 and an Auto Regressive Distributed Lag (ARDL) modelling approach. The findings indicate Liquidity risk and Bank rate have a significant positive long run influence on spread whereas Credit risk, Return on Assets, Operational Efficiency, GDP growth and Financial Sector Development had a significant negative long run impact on spread. However, Reserve Requirement and Inflation had an insignificant impact on spread in Uganda’s banking sector. The study suggests that commercial banks and policy makers in Uganda should consider the internal, industry and macroeconomic environment in coming up with measures to enhance the sector’s efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".