Bibliographic record
Abstract
Abstract This study examines the impact of auditor quality on financial covenants in debt contracts. We conjecture that high‐quality auditors have two related effects on these debt covenants: (i) they encourage fewer and less restrictive covenants by providing assurance to lenders at contract inception and, consequently, (ii) they ensure a lower probability of eventual covenant violations. Consistent with the conjectures, we find that auditor quality is negatively associated with the intensity and tightness of financial covenants. Specifically, high‐quality auditors are associated with fewer covenants (especially performance covenants) and less binding covenants. Additionally, we find that auditor quality is negatively associated with the likelihood of covenant violations. In an ancillary test, we provide evidence that high‐quality auditors mitigate the detrimental effect of covenant violations on the cost of borrowing. Together, these findings highlight the important role of auditors in debt contracting.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".