Lender Monitoring and the Efficacy of Managerial Risk-Taking Incentives
Bibliographic record
Abstract
ABSTRACT Firms provide convexity in managers' compensation plans (vega) to induce risk-averse managers to pursue risky, positive net present value projects. The resulting alignment of managers' and shareholders' incentives creates conflicts with lenders, who face an increased risk of default when managers pursue risky investments. We hypothesize that lenders would respond by stepping up their monitoring and threatening foreclosure to inhibit managers from acting on their vega incentives. Strong lender monitoring should, thus, reduce the efficacy of vega incentives. We test this hypothesis in a unique setting, where lenders purchase credit insurance, reduce their exposure to downside risk, and lower their monitoring. Afterward, we find a stronger association between vega incentives and the firms' risky investments. We contribute to the literature by showing that strong lender monitoring reduces the effectiveness of vega incentives and, thus, of the compensation mechanisms that boards of directors put in place to resolve manager-shareholder conflicts. JEL Classifications: G32; G33; M41; M48.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".