The Failure of Competition in the Credit Card Market in Turkey: The New Empirical Evidence
Bibliographic record
Abstract
The high credit card interest rates in Turkey attracted considerable attention in recent years to regulate the Turkish credit card industry. Before any regulation decision taken, there needs to be better conceptualization and analysis of the Turkish credit card market. First, we highlight the most striking aspects of the Turkish credit card market. After exposing the problem, we benefit from the existing theoretical and empirical studies on the structure of competition in the credit card industry. Potential reasons for the lack of competitions are denoted. Having the existing studies in mind, we finally, construct an empirical model to estimate the market structure in the Turkish credit card industry. Newly disseminated data on the Turkish credit card industry is first introduced in this paper. Our empirical results are based on the panel data set of 22 banks from the second quarter of 2001 to the third quarter of 2005. In addition to random and fixed effects regressions, instrumental variable fixed effect regressions are run on this sample. Our results robustly conclude that the credit cards interest rates in Turkey are economically insensitive to the changes in the cost of fund. This result shows lack of strong competition Turkish credit card market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".