Bibliographic record
Abstract
The commercial banks occupy a prominent position in stimulating economic activity through the role of financial intermediary between savers and borrowers, and contributing in the money supply.The study attempts to answer the question: Is there a relationship between Bank credit maturity structure and economic growth in Saudi Arabia?. to answer this question the study uses annual data during the period 1995 -2020, using the vector autoregression VAR, Granger causality tests, the Impulse Response Function, and the Variance Decomposition.The results of both the Impulse Response Function, and the Variance Decomposition indicate that the increases in RGDP are associated with higher bank credit, especially the long-term credit, which confirms the effectiveness of the credit channel in Saudi Arabia.Further, Granger causality tests suggest a bi-directional causal relationship between RGDP growth and long-term bank credit in KSA.The analytical results supported the hypothesis that the expansion of bank credit enhances economic growth.Where the study concluded that the expansion of bank credit, especially long-term credit in the KSA acts as a credit channel to stimulate economic growth.Therefore, economic growth is faster as commercial banks provide more long-term credit.The study recommends the need for more long-term bank credit as it is an important channel through which economic growth is nourished.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".