Tax Uncertainty and Incremental Tax Avoidance
Bibliographic record
Abstract
ABSTRACT We investigate whether tax avoidance becomes more uncertain as the rate of tax avoidance increases. We estimate a system of equations to demonstrate that as firms' pretax income increases, each additional dollar of potential tax results, on average, in 32.8 cents of tax avoided, which we refer to as incremental tax avoidance. Of the incremental tax avoided, 1.4 cents represent additions to the reserve for uncertain tax benefits (UTB reserve), or 4.3 percent of the total incremental tax avoided. We then partition sample firms into groups that prior research suggests engage in higher rates of tax avoidance, and examine the amount of incremental tax avoidance that results in additions to the UTB reserve. Results demonstrate that the percentage of incremental tax avoidance reflecting additions to UTB reserve is not larger for groups engaging in higher rates of tax avoidance, suggesting higher rates of tax avoidance may not be more uncertain. JEL Classifications: H26; M41; M48.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".