The use of cash flows metrics in <scp>CEO</scp> compensation and the design of loan contracts
Bibliographic record
Abstract
Abstract This study examines whether using cash‐flow‐based performance metrics (CFM) in CEO compensation contracts affects the design of loan contracts. Cash‐flow‐based performance evaluation explicitly motivates the CEO to improve the firm's cash flows, which may enhance debt repayment ability and reduce credit risk. We thus hypothesize that lenders, anticipating this incentive effect, offer lower loan spreads and reduce cash‐flow‐based performance covenants when firms use CFM in CEO compensation contracts. Consistent with our expectation, the use of CFM is associated with lower loan spreads and less use of cash‐flow‐based performance covenants. These findings remain robust after we account for endogeneity. Furthermore, these results are more pronounced in firms with higher credit risk or risk of cash flow shortfalls, suggesting that lenders consider internally generated cash flows more valuable when borrowers face higher external financing costs or have greater liquidity concerns. Additionally, we find that using CFM is associated with improved cash flow performance and enhanced creditworthiness, which supports the notion that CFM is an effective incentive mechanism. Overall, our evidence suggests that lenders consider the incentive effect of cash‐flow‐based performance evaluation in the debt contracting process.
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 | 0.014 | 0.070 |
| 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 | 0.001 |
| 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".