Effects of Dumping vs. Anti-dumping Measures: The US Trade Remedy Laws Applied to Wheat Imports from Canada
Bibliographic record
Abstract
Empirical estimation of the effects of dumping (and/or subsidization) usually has been assessed by estimating it after the imposition of an anti-dumping duty (and/or countervailing duty) order. This article examines, theoretically and empirically, the difference between the economic effects of dumping and anti-dumping measures, using US trade remedy law against hard red spring (HRS) wheat imports from Canada. An econometric model is developed and used to estimate the effects of the decline in HRS wheat imports from Canada after the imposition of anti-dumping/countervailing duties by US authorities. This study found that the anti-dumping/countervailing duties on Canadian HRS wheat imports resulted in an increase in HRS wheat price by $0.14/bushel. Although, in theory, the economic effects of dumping are seemingly identical to those of an anti-dumping measure, they are not equal in practice. In fact, the volume of dumped imports entering the US market on the strength of dumping and the volume of dumped imports driven from the US market after the imposition of an anti-dumping measure are not identical. Implementation of the trade remedy laws is certainly not designed to equate, scientifically, the two effects.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".