Financial Reporting Quality, Private Information, Monitoring, and the Lease-versus-Buy Decision
Bibliographic record
Abstract
ABSTRACT: A flourishing stream of research suggests that liquidity-constrained firms with low accounting quality have limited access to capital for investments. We extend this research by investigating whether these firms are more likely to lease their assets. Lessors’ superior control rights allow them to provide capital to constrained firms with low-quality accounting reports. Consistent with this conjecture, we find that low accounting quality firms have a higher propensity to lease than purchase assets. To verify that leasing does not merely reflect these firms’ desire for off-balance-sheet accounting, we investigate whether banks’ access to private information and monitoring affect the relation between accounting quality and leasing. We find the association between accounting quality and leasing decreases when banks have higher monitoring incentives and when loans contain capital expenditure provisions. These results suggest that other mechanisms can substitute for the role of accounting quality in reducing information problems.
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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 | 0.010 | 0.142 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".