Evaluating the Impure Chinese VAT Relative to a Pure Form in a Simple Monetary Trade Model with an Endogenous Trade Surplus
Bibliographic record
Abstract
China's VAT while seemingly conventional has two major impurities.One is that a separate export rebate system exists where rebate rates are linked from rates paid on creditable inputs.The other is the use of an income base for which there is no crediting of taxes on capital good, rather than the more conventional consumption base with expensing of investment expenditures.Here we argue that in a conventional competitive model both impurities would typically involve a welfare loss, but if we use a numerical calibrated equilibrium model with a monetary structure capturing by these Chinese features in which the trade surplus is endogenously determined and the exchange rate is exogenously set, things are different.These impurities effectively act as added taxes on domestic production (lowed export rebate rates, taxes on a larger VAT base) and tax exporting.Tax exporting reduces exports which lowers the surplus and accumulation of foreign currency.In a static model, a reduced surplus is welfare improving.Using 2002 data, we thus find that China's impure VAT system yields welfare gains in contrast to what a conventional model would show.These results are important since there are arguments being made inside and outside China for changes to be made and move closer to a pure VAT.Our results suggest that unless there are wider changes first in macro-structure, such changes may not be welfare preferred.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".