Cyclicality of Lending Behavior by Banking Sector for the Period (2000-2013): Evidence from Jordan
Bibliographic record
Abstract
All economic sectors and individuals in Jordan rely mainly on banks to cover their shortage in money, thus banking sector plays a vital role in enhancing investments and economic development by standing in the middle between deficit units and surplus units. The major goal of this study is to identify what are the main drivers that impact lending behavior in Jordan. Using panel data and applying multi regression analysis on (13) Jordanian Conventional banks and two Islamic banks for the period (2000-2013) that are covered in this research, we found that lending behavior is statistically significantly affected by internal factors (DV, IR and net profit after tax) and it is also affected significantly by external factor (RR, GDP, IFR, OWDR and Red. R). Also the analysis indicated that OWDR and Red. R as a proxy for monetary policy did have a negative impact on lending behavior but not significantly proven. The study also reached to a conclusion that the amount of loans and advances extended by Jordanian banks is not affected by rate of interest. We recommend that Jordanian banks’ management should take into consideration internal specific factors as well as external specific factor with more care while formulating their lending policy, moreover central bank in cooperation with the Jordanian banking sector should work in more productive relationship in order to enhance more the economic growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".