The Impact of Canceling Tax Rebates on Export of Steel Products: The Case of Sino-ROK Trade
Bibliographic record
Abstract
In this paper,we examine the impact on the amount and structure of China's steel export after cancelling tax rebate policy using a new method,difference on difference,and 6-bit export data(Chap.72 under HS classification).The empirical study shows that the cancelling of tax rebate policy in July 2010 significantly suppressed the export of concerned products.Among the four categories of products,the export growth appears significantly different.The export growth of control group(eliminated tax rebates products) appears 54% to 98% lower than the compared group when we study China's world trade,while 30% to 69% lower when we take Sino-ROK trade as an example.Considering the stable influence on the demand of a certain country,such as trade policy,exchange rate and other factors,we think the empirical results of Sino-ROK can reflect the actual effects of tax rebate policy more objectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".