Bibliographic record
Abstract
We examine how corporate tax outcomes, consisting of tax avoidance and tax risk, relate to the career outcomes of employees who work in the tax department. Using tax employee data obtained from the professional networking website LinkedIn, we find that both tax avoidance and tax risk are linked to tax employee career outcomes. Specifically, we find that tax employees’ turnover is positively associated with adverse tax outcomes, evidenced through lower tax avoidance or higher tax risk. Moreover, we find that the employment gap for tax employees after exiting the firm is positively associated with these adverse tax outcomes. Lastly, we find that the probability of an external promotion for a tax employee upon joining a new firm is negatively associated with the adverse tax outcomes faced by the previous employer. Collectively, these results suggest that tax employees may experience negative career outcomes when their firms face adverse tax performance. Our study highlights the consequences that tax avoidance and tax risk may have on the individuals who produce these outcomes. Our study also sheds light on the incentives that drive tax employees to cooperate with their firm’s tax-related objectives.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".