Remuneration, Personnel Expenses and Third Party Expenses in Times of Covid-19 in the Financial Sector
Bibliographic record
Abstract
The objective of this research was to determine the statistically significant differences between the semiannual percentages of salaries, personnel expenses and third party expenses of the Peruvian financial sector during the “COVID-19” pandemic. The percentage of administrative expenses of 46 financial entities in Peru was analyzed, and in what proportion they invested in favor of their collaborators; As a result, in terms of remuneration, the Municipal Savings Banks invested in a greater proportion (50.69%), regarding personnel expenses, the Municipal Savings Banks stand out with 16.8%, and in terms of third-party payments, the financial entity that invested the most was Edpymes with 42.30%. Likewise, through the Kruskal Wallis test it was obtained that there are no statistically significant differences in remuneration, payment of personnel and payment to third parties among financial entities in Peru (p value> 0.05). It is concluded that the semester percentage variation in time of COVID-19 with respect to the first semester 2019 has had a minimal change (+ 1.20%. In salaries, -1.6% in personnel payments and -0.05% in third party expenses).
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How this classification was reachedexpand
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".