Bibliographic record
Abstract
Abstract The general anti‐avoidance rule, or GAAR, is an enforcement mechanism that gives a country's taxing authority broad power to deny a taxpayer tax benefits associated with any transaction. Although GAARs are becoming increasingly common, the presence of a GAAR is generally overlooked by researchers and thus has been left unstudied. In this paper, we provide an initial investigation by studying the effect of GAARs on firm‐level corporate tax avoidance behaviors. Using an indicator for the enactment or strengthening of a GAAR within a country in a stacked difference‐in‐differences design, we find GAAR enactment is associated with a statistically and economically significant decrease in firm‐level tax avoidance. Additional cross‐sectional analyses show that the decline in tax avoidance occurs for conventional GAARs and economic substance‐type rules, original and strengthened GAARs, and domestic and multinational firms. Results also show that the effect is strongest for firms with higher levels of pre‐GAAR‐enactment tax avoidance and for firms incorporated in countries where the burden of proof lies with the taxpayer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".