Nontax Use of Tax Havens: Evidence From Captive Insurance
Bibliographic record
Abstract
ABSTRACT Corporate tax avoidance is a recurring focus of policy‐makers, the media, activist groups, and researchers. This focus often centers on multinational enterprises' (MNEs) use of tax havens, with a wide body of research utilizing MNEs' tax haven use as evidence of corporate tax avoidance activities. However, the common assumption that MNEs operate in tax havens only for tax avoidance purposes overlooks the role tax havens play as homes for captive insurance entities, which allow firms to secure “self” insurance coverage but do not provide obvious differential federal tax benefits. When we remove the effect of captives on tax haven–based measures, we observe a roughly threefold increase in the magnitude of tax savings specifically associated with haven noncaptive activity. We document that nonfinancial firms' use of captive insurance occurs in approximately 11% of firm‐years and spans nearly all Fama–French 49 industries. We construct a haven captive use determinants model, with strong discriminatory power and compelling out‐of‐sample corroboration tests, that future research can employ to account for firms' use of haven captives. Our findings underscore the importance of separating captive and noncaptive‐related haven activities.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".