Bibliographic record
Abstract
Local VAT had been introduced in Japan in FY1997. Several problems became evident with the current system. The purpose of this paper is to identify these problems in detail and to suggest reform options in the light of international experience and the fiscal federalism literatures. We argue that contrary to traditional wisdom, it is possible to allow local governments to set their rate of VAT independently, within the framework of revenue-sharing arrangement on the basis of consumption statistics. To do so, however, requires some changes in the current systems;(1) when origin prefecture imposes local VAT on the 'final sales' rather than the 'central VAT liability', even if each prefecture levies the tax at variable rates, calculating tax under the input tax credit mechanism leads to an appropriate outcome; (2) our new allocation formula on the basis of 'revenue potentials' allows local governments to set their tax rate independently. In addition, we argue that in the view of exactness, Harmonized Sales Tax in Canada that derived consumption statistics from inter-regional Input-Output table is ideal for allocating tax revenue to the destination region. Until inter-regional input-output table will be designated by the government, some feasible reform package should be adopted: (1) deduction exempt final sales from designated statistics; (2) removal of employees' share; (3) increase in the weight assigned to population share and decrease in the weight assigned to designated statistics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".