Is Tax Avoidance Associated with Economically Significant Rent Extraction among U.S. Firms?
Bibliographic record
Abstract
Abstract Two influential papers in the tax‐avoidance literature (Desai and Dharmapala ; Desai, Dyck, and Zingales ) argue that aggressive forms of tax avoidance employ technologies that complement managerial rent extraction, and provide supporting evidence from firms in Russia. Several papers rely on this theory to motivate and interpret tests in a U.S. setting, but these tests are open to multiple interpretations. This paper investigates the extent to which shareholders of U.S. companies are affected by any such rent extraction. The evidence is inconsistent with the tax‐avoidance technologies employed by U.S. firms allowing managers to extract sufficient rents to negatively affect future performance. Additional tests on poorly governed U.S. firms find no evidence that tax‐avoidance activities relate positively to either overinvestment or higher executive compensation, and no evidence that either complexity or the Sarbanes‐Oxley Act moderates the relation between future performance and tax avoidance. The evidence suggests that caution is warranted in interpreting evidence according to this theory in a U.S. setting.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".