Value-Added Taxes: Lessons Learned from Other Countries on Compliance Risks, Administrative Costs, Compliance Burden, and Transition
Bibliographic record
Abstract
A letter report 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 was asked to identify the lessons learned from other countries' experiences in administering a VAT. This report describes (1) how VAT design choices, such as exemptions and enforcement mechanisms, have affected compliance, administrative costs, and compliance burden; (2) how countries with federal systems administer a VAT; and (3) how countries that recently transitioned to a VAT implemented the new tax. GAO selected five countries to study--Australia, Canada, France, New Zealand, and the United Kingdom--that provided a range of VAT designs from relatively simple to more complex with multiple exemptions and tax rates. The study countries also included some with federal systems and some that recently implemented a VAT. GAO does not make any recommendations in this report."
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".