Bibliographic record
Abstract
Although VATs applied simultaneously within the same country by different levels of government were long considered to be either undesirable or infeasible, two quite different types of sub-central VATs – regional consumption taxes and local business taxes -- now exist in a number of countries. Brazil, Canada, and India have introduced regional (state and provincial) VATs which, like national VATs, are general taxes on consumption administered through a transaction-based credit-invoice approach. Although these three countries are very different, and each has established such a tax for its own reasons in different ways and with varying degrees of success, as this paper discusses, on the whole such regional VATs appear to work fairly well, especially in Canada. The issues that arise with independent regional VATs are closely related to those arising with national VATs in a common market such as the EU. A number of problems such as ‘carousel’ (or ‘missing trader’) fraud have recently received considerable attention in the EU and a variety of alternative solutions to such problems have been suggested, some involving major structural changes in the VAT. Experience with regional VATs, however, suggests that what is needed to resolve most such problems is primarily a firmer ‘EU-wide’ framework for improving VAT administration. The second type of sub-central VAT that has recently emerged in Italy, Japan, and France (as well as in several U.S. states) takes the form of a revised form of local business tax which is generally imposed on an ‘income’ (origin) basis in contrast to the destination-based consumption VATs discussed earlier. These taxes seem superior in some important respects to other forms of local business taxation and appear to be compatible with both regional and national VATs. Although important economic and administrative aspects require careful consideration in designing and implementing ‘two-level’ (dual) VATs, such dual VATs (or even triple VATs, including an ‘income-type’ VAT at the local level) are evidently both feasible technically and acceptable politically. This conclusion does not mean that regional VATs are either inherently desirable or necessarily the best alternative for any country (or set of countries). But it does suggest that such taxes may work more satisfactorily in at least some countries than other forms of regional sales taxes or local business taxes. Indeed, both varieties of ‘decentralized VATs’ discussed here may become more important over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".