The Use of Cash Flows in Setting CEO Compensation and the Cost of Bank Loans
Bibliographic record
Abstract
This study examines whether the use of cash flow metrics (CFM) in setting CEO compensation affects the cost of borrowing from banks. Cash-flow-based performance evaluation can motivate managers to improve cash flow generation, which enhances the firms’ debt repayment ability and reduces credit risk. We thus hypothesize that banks, anticipating this incentive effect of cash-flow-based performance evaluation, offer lower loan spreads for firms using CFM in setting CEO pay. Consistently, we find a negative relation between the use of CFM in setting CEO pay and loan spreads. This negative association is robust to controlling for endogeneity. Moreover, this negative association is concentrated among firms facing higher default risk or higher risk of cash flow shortfalls, suggesting that lenders consider internally generated cash flows as more valuable when borrowers face higher external financing costs or greater liquidity concerns. Further, we find that the use of CFM is associated with improvements in cash flow generation and reductions in credit risk, reinforcing the notion that the use of CFM serves as an effective incentive mechanism. The overall evidence suggests that lenders consider the incentive effect of cash-flow-based performance evaluation in the debt contracting process.
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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 | 0.003 | 0.035 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".