FOREIGN CURRENCY LENDING AND BANKING SYSTEM STABILITY : NEW EVIDENCE FROM TURKEY
Bibliographic record
Abstract
This paper studies the drivers of foreign currency lending by Turkish banks along with its consequences for the banking system in particular and for the economy in general for the period between 2003 and 2009. Our sample ends in 2009 because foreign currency lending to households in Turkey is banned starting in the second quarter of 2009. We highlight possible risks to the Turkish banking system as a result of the system's heavy exposure to exchange rate and default risks. Our findings show that deposit dollarization seems to be the most important driver of loan dollarization in the case of Turkey. We also find evidence that larger banks in general tend to lend more in foreign currency. There is no evidence that bank cash holdings and their balances with the Central Bank affect bank lending behavior. We also evaluate whether the decision taken by the regulatory authorities in Turkey in 2009 to ban foreign currency lending to households had merits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".