Does Restricting Managers' Discretion through <scp>GAAP</scp> Impact the Usefulness of Accounting Information in Debt Contracting?†
Bibliographic record
Abstract
ABSTRACT We examine whether restricting managers' discretion through GAAP impacts the usefulness of accounting information in debt contracting. Our study informs standard setters and regulators regarding the debt contracting implications of limiting managers' discretion via accounting standards. We predict and find that under more restrictive standards, lenders make more non‐GAAP modifications to GAAP‐based performance measures, suggesting that restrictions of managers' discretion reduce the usefulness of accounting information. We perform two additional analyses to enhance identification. First, in line‐item‐level analysis, we document a positive relation between the exclusion of specific nonrecurring items from contractual definitions of earnings and the number of restrictions in the GAAP standards that apply to each specific item each year. Second, using difference‐in‐differences tests around standard changes, we find that the propensity to exclude items varies positively with changes in the restrictiveness of related standards. Moreover, we predict and find that restrictive standards are also positively associated with loan spreads but significantly less so when lenders adjust GAAP numbers in loan contracts. Overall, this study improves our understanding of how attributes of accounting standards impact the usefulness of accounting information.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".