How Quickly Do Firms Adjust to Optimal Levels of Tax Avoidance?
Bibliographic record
Abstract
ABSTRACT The trade‐off literature asserts that managers weigh the direct benefits of tax avoidance against the associated nontax costs. This literature implies each firm has a unique optimal level of tax avoidance that balances these costs and benefits. Our study is the first to document how quickly the average firm moves toward its optimal level of tax avoidance. We find that the typical firm converges toward its optimum at a rate that ranges from approximately 69 to 84 percent over a three‐year period, depending upon model specifications. Consistent with asymmetric levels of frictions across the tax avoidance distribution, we find the speed of adjustment is greater for firms below their optimal level of tax avoidance than for firms above. We perform additional cross‐sectional analyses to provide insight into some of the frictions that prevent firms from adjusting completely to their optimal level of tax avoidance. We generally find growth firms exhibit slower adjustment speeds and provide limited evidence that both multinational firms and income‐mobile firms exhibit faster adjustment speeds.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".