Income Smoothing and the Usefulness of Earnings for Monitoring in Debt Contracting
Bibliographic record
Abstract
ABSTRACT We investigate whether income smoothing affects the usefulness of earnings for contracting through the monitoring role of earnings‐based debt covenants. First, we examine initial contract design and predict that income smoothing will increase (decrease) the use of earnings‐based covenants if income smoothing improves (reduces) the usefulness of earnings to monitor borrowers. We find that private debt contracts to borrowers with greater income smoothing are more likely to include earnings‐based covenants. A structural model that explores the cause of this relationship provides evidence that smoothing improves the ability of earnings to reflect credit risk. Second, we examine technical default following contract inception. We find that income smoothing is associated with a lower likelihood of spurious technical default (when the borrower's economic performance has not declined but the loan nevertheless enters technical default). In contrast, we find no association between income smoothing and performance technical default (when the borrower's economic performance has declined). Collectively, this evidence is consistent with income smoothing improving the effectiveness of earnings‐based information in monitoring borrowers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".