Directors’ and Officers’ Liability Insurance and Aggressive Tax‐Reporting Activities: Evidence from Canada
Bibliographic record
Abstract
Abstract This paper examines the relationship between directors’ and officers’ liability insurance (D&O insurance) and firms’ aggressive tax reporting. Using large Canadian public companies listed on the TSX300 and relying on several measures to capture aggressive tax‐reporting activities, including GAAP effective tax rates, cash effective tax rates, and the total and residual book‐tax differences, I find that D&O insurance exhibits a strong negative relationship with the GAAP effective tax rates and a strong positive relationship with both the total and residual book‐tax differences. However, there is generally no evidence showing that D&O insurance is associated with the cash effective tax rates. I interpret these results as indicating that D&O insurance reduces the tax expenses reported in the financial statements but not the actual tax paid. In other words, D&O insurance contributes to financial tax management but not to cash tax savings. Further tests in this study reveal that firms with fluctuating D&O coverage limits engage in more aggressive tax reporting than other firms, suggesting that managers may consider the level of D&O insurance that they purchase when they make aggressive tax‐reporting decisions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".