Value-Added Taxes: Potential Lessons for the United States from Other Countries' Experiences
Bibliographic record
Abstract
Testimony issued by the Government Accountability Office with an abstract that begins "Dissatisfaction with the federal tax system has led to a debate about U.S. tax reform, including proposals for a national consumption tax. One type of proposed consumption tax is a value-added tax (VAT), widely used around the world. A VAT is levied on the difference between a business's sales and its purchases of goods and services. Typically, a business calculates the tax due on its sales, subtracts a credit for taxes paid on its purchases, and remits the difference to the government. While the economic and distributional effects of a U.S. VAT type tax have been studied, GAO issued a report in 2008 that looked at lessons learned from VAT administration in Australia, Canada, France, New Zealand, and the United Kingdom. These countries provided a range of VAT designs from relatively simple to more complex. This statement, which is based on the 2008 report, focuses on (1) the effect VAT design choices, such as exemptions and enforcement mechanisms, have on compliance, administrative costs, and compliance burden; (2) Canada's experience with administering a VAT in conjunction with several different subnational consumption tax arrangements; and (3) the experience that some countries had transitioning to a VAT."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".