The Impacts of Publicly and Privately-Owned Banks on Economic Growth in Turkey: A Comparative Analysis
Bibliographic record
Abstract
Certain transfer mechanisms can be expressed with the help of very different theories about how financial development influences economic growth. The ongoing debates on how the banking system contributes to the economy are tried to be explained by those transmission mechanisms. Among these mechanisms, there are bank deposits and credits related to savings and investment indicators, which are essential to the banking system. In this study, the impact of the banking system on the gross domestic product (GDP) is tried to be examined. However, unlike other similar studies, the development of the banking system is taken into consideration on the basis of total assets, which offers a broader perspective on the total of bank deposits and/or credits. Accordingly, the study aims to explicate the possible association between total assets of the publicly-owned commercial banks as well as the privately-owned banks operating in Turkey and the GDP. The analyses performed on the basis of the quarterly data cover the period between the first quarter of 2010 and the third quarter of 2019. The results obtained from the cointegration test and the FMOLS, DOLS, and CCR cointegration regression analyses reveal that both publicly-owned deposit banks and privately-owned deposit banks are related to the GDP in the long-run. Although it is determined that the total assets within both bank groups has a positive impact on the GDP in the long-run, it is found that publicly-owned deposit banks have a greater impact on the GDP in comparison to privately-owned banks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
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".