Changing the Rules of the Game: Offshore Financial Centers, Regulatory Competition & Financial Crises
Bibliographic record
Abstract
Politicians from New York District Attorney Robert Morgenthau to British Prime Minister Gordon Brown are blaming offshore financial centers (OFCs) for contributing to the current global financial crisis by failing to adequately regulate their financial industries. These criticisms are mistaken for three reasons. First, OFCs offer different rather than less regulation. Since most OFC financial products are aimed at sophisticated and institutional investors, OFC regulators are less concerned with protections for retail investors than are regulators in jurisdictions like the United States. This enables them to adopt less costly regulations that are still effective at preventing fraud and other financial crimes. Second, OFCs play a particularly important role in providing regulatory competition. OFCs have innovated in areas of law from captive insurance to trusts, pushing onshore jurisdictions like the United States to respond. For example, Vermont has innovated in captive insurance in response to competition from Bermuda and the Cayman Islands. Third, OFCs provide an important means for onshore jurisdictions to price discriminate in their taxation. Without OFCs, companies based in high tax jurisdictions like Canada, France, and the United States would find it more difficult to compete internationally with companies from lower tax jurisdictions. Preserving OFC-competition thus benefits onshore jurisdictions.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".