Rebalancing and the Chinese VAT: Some Numerical Simulation Results
Bibliographic record
Abstract
This paper presents numerical simulation results that suggest that China can both reduce its trade imbalance and receive welfare benefits by switching the value added tax (VAT) regime from the current destination principle to an origin principle.With the tax on exports exceeding that no longer collected on imports, revenues rise and exports fall.VAT regime switching is thus a possibility for China to receive a double benefit, rebalancing trade with a welfare gain.This has implications for present G20 discussions on finding ways to adjust global trade imbalances.Under a destination principle, imports are taxed but input taxes are rebated on exports (as currently).Under an origin basis imports are not taxed, but no export rebates are given.Previous VAT literature stresses the neutrality of tax basis switches, which simply reflect moving between consumption and production taxes, but neutrality only holds when trade is balanced.In the unbalanced trade case for countries with a trade surplus, such as China, an origin basis offers a lower tax rate on an equal yield basis and reduced exports.We use a two country endogenous trade imbalance general equilibrium global trade model with endogenous factor supply, a fixed exchange rate and a non-accommodative monetary policy structure which supports the Chinese trade imbalance.We calibrate model parameters to 2008 data and simulate counterfactual equilibria for VAT tax basis switches in which the trade imbalance changes.Our results suggest that given China's trade surplus VAT regime switching to an origin can decrease China's trade surplus by over 50%, and additionally increase Chinese and world welfare.The rest of the world's production and welfare improves simultaneously.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".