The Impact of Non-banking Credit Organization Credits on Economic Growth in Azerbaijan
Bibliographic record
Abstract
Financial institutions plays remarkable role in Azerbaijan economy. The general structure of financial institutions in the country includes banks, local branches of foreign banks and non-bank financial institutions, while among these organizations banks have a leading position. Most of the lending activity in Azerbaijan is carried out through banks. This situation shows their special importance in the country's economy. However, NBCOs (Non-Banking Credit Organizations) also have an important position in lending to the individuals in the economy. This article investigates the impact of the NBCOs credits on non-oil GDP in Azerbaijan. For this purpose, the quarterly data covering 2005-2018 were used. For the evaluation, the cointegration methods as CCR (Canonical Cointegrating Regression), DOLS (Dynamic Ordinary Least Squares) and FMOLS (Fully Modified Ordinary Least Squares) methods were employed. The results of cointegration tests conclude that there is cointegration relationship between the variables in long-term. The results of the estimation show that a 1% rise in credits and physical capital investments increases non-oil GDP by 0.26% and 0.92, respectively. Findings of study are in line with both the theory and the results of other researches, and may be considered adequate for Azerbaijan economy. The management and other decision-makers of relevant organizations may adopt effective decisions for more efficient operation of credit organizations by taking into account the results of the research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".