1 Regulations and Competition in Credit Card Market in Turkey
Bibliographic record
Abstract
Turkish credit card market experienced a strong growth with the establishment of stability in the economy and the decline in the inflation rate after 2001 crisis. Turkey became the 3 rd biggest market in Europe with 32 million credit cards in 2006. However, the quick response of the credit card interest rates to the increase in the cost of funds during the 2001 crisis was not observed in the case of decreasing cost of funds afterwards. Credit card interest rates stayed persistently high compared to the declining market interest rates and the interest rates in other credit markets such as home, auto or consumer credits. This study is an attempt to analyze the failure of competition in the Turkish credit card market. Moreover, a quarterly data set of the credit card interest rates of the all 22 issuers in the Turkish market is employed to provide empirical evidence for the failure of price competition in the market. The data set spans from the second quarter of 2001 to the last quarter of 2006. One-step and two-step difference and system GMM regressions are run on the panel data set. The study concludes that the credit card interest rates in Turkey are economically insensitive to the changes in the cost of funds during this period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".