La concentración en el mercado de créditos y la estabilidad del sistema bancario en Latinoamérica
Bibliographic record
Abstract
Recent international crises have research focused on determining the role of the market structure in financial stability. This research is focused on determining the impact of the concentration of the credit market, considering the banking system of 17 countries in Latin America, USA. and Canada, and taking into account the annual period from 1996 to 2017. The Bank Z score was used as an indicator of financial stability, as well as the concentration ratio of the five largest countries in each country (CR5) as an indicator of concentration, and the Boone indicator as a competition proxy, maintaining an inverse relationship with it. Using a fixed effects model, the results obtained rejected the significance of the CR5; however, the Boone indicator will have a negative relationship with the dependent variable. Then, an increase in this indicator, that is, a decrease in the degree of competitiveness, translates into a reduction in financial stability. In addition, the Lerner index has a positive relationship with the Bank Z score, so there isn´t a Too Big To Fail (TBTF) behavior in Latin American banking systems. In this way, the concentration-fragility hypothesis is sustained, but without considering the increase in market power as a determinant of fragility at the financial system.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".