The Economic Effects of Special Purpose Entities on Corporate Tax Avoidance
Bibliographic record
Abstract
ABSTRACT This study provides the first large‐sample evidence on the economic tax effects of special purpose entities (SPEs). These increasingly common organizational structures facilitate corporate tax savings by enabling sponsor firms to increase tax‐advantaged activities and/or enhance their tax efficiency (i.e., relative tax savings of a given activity). Using path analysis, we find that SPEs facilitate greater tax avoidance such that an economically large amount of cash tax savings from research and development (R&D), depreciable assets, net operating loss carryforwards, intangible assets, foreign operations, and tax havens occur in conjunction with SPE use. We estimate that SPEs help generate over $330 billion of incremental cash tax savings, or roughly 6 percent of total U.S. federal corporate income tax collections during the sample period. Interaction analyses reveal that SPEs enhance the tax efficiency of intangibles and R&D by 61.5 percent to 87.5 percent. Overall, these findings provide economic insight into complex organizational structures supporting corporate tax avoidance.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".