Do generalist CEOs engage in more tax avoidance than specialist CEOs?
Bibliographic record
Abstract
Existing research suggests that generalist CEOs, who possess managerial skills that are transferrable across firms and industries, are more able to bear downside risk than specialist CEOs with non-transferrable managerial expertise. In this paper, we examine whether generalist CEOs are better positioned to engage in risky tax avoidance strategies than specialist CEOs. Our empirical results support this prediction and show that firms with generalist CEOs tend to engage in more tax avoidance than those with specialist CEOs. Our identification strategy includes an instrumental variable method and a difference-in-differences test using CEO turnover as a quasi-natural experiment to correct for endogeneities. A battery of robustness checks and cross-sectional tests strengthens our findings. Taken together, our findings imply that general managerial skills of CEOs matter more for tax planning than do specific managerial skills.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".