Determinants of bank lending rates: Empirical evidence from conventional retail banks in Bahrain
Bibliographic record
Abstract
The study attempts to identify the determinants of lending rates in the Kingdom of Bahrain. It examines the impact of certain macroeconomic and banks’ aggregate data variables on the level of interest rates on loans charged by Bahraini conventional retail banks using quarterly data for the period from the 4th quarter of 2012 to the 4th quarter of 2021. The study tests the impact of a consumer price index (CPI), GDP growth rates, loan-to-total assets (loan ratio), liquid assets as a proportion of total assets (liquidity position), personal lending rate, loan-to-deposit ratio, money supply (M2) growth, non-performing loans (NPL) ratio, and return on assets (ROA) on banks’ lending rates. The study is mainly based on data retrieved from the publications of the Central Bank of Bahrain and the CEIC Data Global Database. The study uses EViews 12 The results reveal that CPI, liquidity position, the lending rate for personal loans, deposit ratio, and return on assets are the major determinants of bank lending rates to businesses. The study found that GDP growth, money supply growth, and non-performing loans ratio are insignificant in determining the lending rate to businesses in Bahrain. In addition to yielding insights to the respective authorities, this study also helps creditors, investors, and borrowers predict interest rates and thus manage their assets and liabilities more efficiently.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".