Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of market competition on the relation between CEO inside debt and corporate risk-taking. Design/methodology/approach Ordinary least squares regressions are used to estimate the relation between CEO inside debt and firm risk. Additionally, instrumental variable (IV-GMM) regressions are used to check the robustness of the results. Findings The results of this paper indicate that CEO inside debt is negatively associated with the measures of future risk. However, this negative association is influenced by market competition. Specifically, CEO inside debt results in lower levels of firm risk when market competition is high. When market competition is low, inside debt has no effect on firm risk. Additional results show that CEOs with large inside debt tend to decrease R&D investments and financial leverage and increase firm cash holdings and working capital only when market competition is high. Overall, these results suggest that market competition significantly influences the effect of CEO inside debt on corporate risk-taking by changing the strength of incentives from inside debt. Practical implications CEO inside debt could be used to provide incentives to CEOs to manage corporate risk-taking. Social implications The empirical results in this paper provide a practical tool to the boards of corporations to manage corporate risk-taking. The results suggest that boards can reduce excessive risk-taking by increasing the level of debt type compensation incentives. However, this strategy is effective only when market competition is high because in such markets inside debt provides the strongest incentives to reduce corporate risk. When competition is low, incentives from inside debt are ineffective in managing corporate risk-taking. Originality/value This is the first study that shows that the negative association between CEO inside debt and corporate risk-taking critically depends on the intensity of market competition.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".