Determinants of Interest Rate Spread: Some Empirical Evidence from Kenya’s Banking Sector
Bibliographic record
Abstract
This paper analyzes the role played by bank and industry-specific factors as well as macroeconomic variables in the determination of interest margins in Kenya’s banking sector. Decomposition of the spread using income and balance sheet of the banking sector as a whole and panel data analysis of 39 commercial banks yielded consistent results which highlight the significant role played by bank and industry specific factors and macroeconomic variables in interest rate spread determination. It is shown that between 7 – 10 per cent of the interest margin was attributable to operating costs. Moreover, a 1 per cent increase in operating costs translates to 0.38 per cent increase in interest margins for the sample of banks studied. In addition, a 1 per cent increase in non-performing loans leads to an upward adjustment of interest margins by 0.12 per cent. Macroeconomic factors also contribute to changes in the interest margin. A 1 per cent increase in Treasury bill rates leads to an upward adjustment of interest margins by 0.1 per cent. Likewise, a 1 per cent increase in GDP growth and exchange rate variability results in 0.05 and 0.06 per cent increase in interest spread respectively. In contrast, a 1 per cent increase in loans-liabilities ratio (reflecting degree of intermediation) results in interest margin reduction by 0.17per cent. The results therefore emphasize the need to improve banking sector efficiency, deal with non-performing loans and maintain general macroeconomic stability.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".