Directors’ and Officers’ Liability Insurance: Reducing Litigation Risk While Encouraging Risk-Taking
Bibliographic record
Abstract
To retain directors and officers (D&O) and to protect them from potential litigation risk rooted in their business decisions, public listed companies often purchase liability insurance coverage for them. However, this may cause an unintended moral hazard problem, since insuring D&O “misbehaviors” reduces the disciplining effect of stakeholder litigation but in the meantime encourages other categories of risk-taking behaviors. Yet little has been done in the literature on investigating the association between D&O liability insurance coverage and unethical risk-taking behaviors in tax reporting practices. Using a sample of 225 Canadian publicly listed companies from 2002 to 2018, we find D&O insurance results in more tax avoidance which is unethical. We also find that higher insurance coverage only encourages tax avoidance in non-crisis periods, but not during financial crisis. In addition, the findings document that the association between D&O liability insurance coverage and tax avoidance is weakened in firms with high profitability and lower default risk.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".