Bibliographic record
Abstract
Abstract This paper investigates whether economies of scale exist for tax planning. In particular, do larger, more profitable, multinational corporations avoid more taxes than other firms, resulting in lower effective tax rates? While the empirical results indicate that, ceteris paribus, larger corporations have higher effective tax rates, firms with greater pre‐tax income have lower effective tax rates. The negative relation between effective tax rates (ETRs) and pretax income is consistent with firms with greater pre‐tax income having more incentives and resources to engage in tax planning. Consistent with multinational corporations being able to avoid income taxes that domestic‐only companies cannot, I find that multinational corporations in general, and multinational corporations with more extensive foreign operations, have lower worldwide ETRs than other firms. Finally, in a sample of multinational corporations only, I find that higher levels of U.S. pre‐tax income are associated with lower U.S. and foreign ETRs, while higher levels of foreign pre‐tax income are associated with higher U.S. and foreign ETRs. Thus, large amounts of foreign income are associated with higher corporate tax burdens. Overall, I find substantial evidence of economies of scale to tax planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".