Corporate Investment Incentives and Accounting‐Based Debt Covenants*
Bibliographic record
Abstract
Abstract This paper studies the conditions under which accounting‐based debt covenants increase firm value in a setting that incorporates the conflicting incentives of shareholders, bondholders, and managers. We construct a model in which debt is needed to discipline managerial investment decisions despite endogenous compensation contracts. We show that accounting covenants increase value when (1) debt serves as a credible commitment to penalize poor investment decisions; (2) the firm faces other (exogenous) sources of uncertainty that can make debt risky despite good investment decisions; and (3) accounting information serves as a contractible proxy for firm's economic performance. In these circumstances, accounting covenants ensure that shareholders do not offer compensation schemes that would encourage bondholder wealth expropriation when the debt becomes risky. A covenant specifying a required level of accounting performance provides additional bondholder power when performance is low. An accounting‐based dividend covenant allows a disbursement to maintain investment incentives when performance is high without allowing dividend‐based expropriation. The optimal covenants depend on the reliability of accounting information, and the interaction between accounting performance and the different incentive conflicts provides new insight into the empirical literature on accounting‐based covenants.
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 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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".