Biases in calculating dumping Margins: The case of cyclical products
Bibliographic record
Abstract
A dumping investigation involves comparing export prices with a “normal value” loosely defined as the price in the exporter’s domestic market observed in the course of normal trade. However, domestic sales with prices below production costs are excluded from the computation of a normal value; thus increasing the probability products with cyclical prices will get caught with positive dumping margins although there are no intentions to dump. The objective of the paper is to illustrate how price cycles impact the magnitude of estimated dumping margins. The empirical analysis focuses on Canadian hog exports to the U.S. and U.S. potato exports to Canada. The period and amplitude of each price cycles are estimated. The analysis starts with the assumption that export and domestic prices are equal so no true dumping occurs. Margins are then calculated based on rules that exclude below cost sales. The resulting average dumping margins for Canadian hogs and U.S. potato exports are respectively 11.5 and 5.9 percent. Biases in dumping margins depend on the nature of the cycle, the period of investigations, and the estimate of the cost of production.
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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 | 0.018 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.001 | 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".