Determinants of the Efficiencies in Turkish Banking Sector (Tobit Analysis)
Bibliographic record
Abstract
<p>The competition that has been faced in Turkish banking sector compels banks to use their sources efficiently. Efficiency and productivity analyses are important management tools to determine to what extent inputs have been used in the process of acquiring required outputs of banks. Efficient and productive functioning of the banks in Turkey has a major importance in terms of national economy. Being different from other economic sectors, the banking undertakes the duty of financial intermediation which determines resource allocation. This places banking to a central position for the economic development of the country. For this reason, analysis of efficiency and productivity measurements is necessary to carry out performance analysis of the banking sector.</p><p>This research aims at investigating the determinant factors of the technical, pure technical, and scale efficiencies of Turkish Banking Sector for the period of 2007-2013 with a sample of 4 Participation Banks (PBs) and 28 Conventional Deposit Banks (DBs) by using Tobit Regression Analysis.</p>The findings from Tobit regression analysis suggest that while the factors in terms of size, risk and bank management quality have negative impact on technical efficiency of Turkish banking sector, market share and profitability variables have positive impact. On the other hand, while the factors like market share, risk, bank management quality and year 2010 (as the dummy variable to capture the financial impact) have negative impact on pure technical efficiency, size and loan intensity have positive affect. Lastly, while the factors like size, risk and bank management quality is found affecting the scale efficiency negatively, loan intensity, market share and profitability variables have positive influence.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".