The competitive edge of credit unions in Costa Rica: From financial repression to the risks of a new financial environment
Bibliographic record
Abstract
This article argues that financial repression played a key role in the emergence of credit unions (CUs) in Costa Rica, along with other institutional factors. Credit unions took advantage of the opportunity to serve borrowers whose requests had been refused by banks. Given the sweeping reforms of the Costa Rican financial system aimed at reducing the scope of financial repression, this article poses the question of how those reforms impacted the competitiveness of CUs. Previous literature suggests that financial reform may lead to concentration in the financial sector, and not to the promotion of a more competitive environment. This article presents data showing that CUs in Costa Rica exhibited an enhanced ability to gain market share and also provides an explanation for the observed trend.RÉSUMÉL’article soutient que la répression financière avec d’autres facteurs institutionnels a joué un rôle clé dans l’émergence des coopératives financières (CF) au Costa Rica. Les CF ont profité de l’occasion pour servir les emprunteurs dont les demandes avaient été refusées par les banques. Compte tenu des réformes radicales du système financier costaricien, visant à réduire l’étendue de la répression financière, l’article pose la question de l’incidence de ces réformes sur la compétitivité des CF. La littérature précédente suggère que la réforme financière peut conduire à une concentration du secteur financier et non à la promotion d’un environnement plus compétitif. Notre article démontre que les CF du Costa Rica présentent une capacité à accroître leurs parts de marché. Nous fournissons une explication de la tendance observée.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".