Banking and Cooperatives in Ecuador: Comparative Evidence of Technical Efficiency and Financial Resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Ecuador’s financial system, private banks and savings and credit cooperatives coexist, both playing a key role in financial intermediation and the economic inclusion of traditionally underserved sectors. During the COVID-19 pandemic, these institutions faced unprecedented challenges that tested their adaptability and operational efficiency. In this context, the present study evaluates the technical efficiency of banks and cooperatives in Ecuador over the 2015–2023 period, using a combined approach involving Data Envelopment Analysis (DEA) and mixed linear models (MLMs). A longitudinal and comparative methodology is adopted, allowing for the analysis of efficiency trends over time and the identification of their main structural determinants. The results show that cooperatives exhibit a higher average technical efficiency than banks, as well as greater resilience during the health crisis. The analysis reveals that operating expenses negatively impact efficiency, while equity and social capital show no significant effects. By combining DEA and MLMs, the study offers a more comprehensive and nuanced understanding of the factors influencing efficiency, underscoring the importance of tailored policies and institutional strategies focused on resource optimization and continuous improvement. The study concludes that efficiency does not rely solely on size or asset volume, but rather on managerial capacity and organizational adaptability in complex and changing environments.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it