Assessing Anticipatory Effects in the Presence of Antidumping Duties: Canadian softwood lumber
Bibliographic record
Abstract
US antidumping (AD) policy can generate anticipatory effects on firms subject to AD duties because of a process called "administrative reviews" in which US government agencies determines refund rates based on exporters' most recent pricing behavior. The purpose of this paper is to assess the anticipatory effects from importers' and exporters' side by examining the US-Canada softwood lumber disputes. Using a demand estimation technique, we find evidence of the importers' anticipation: importers were less sensitive to tariff rates under the AD duties compared to standard tariffs, which indicates that the importers increased their volume of imports anticipating the future refund. We further show that the importers adjusted their anticipation adaptively, in the sense that the anticipated refund rate evolved according to the most recent revised rate of an AD duty released in the determination of an administrative review. On the other hand, using a pass-through regression, we find evidence of the exporters' anticipation: the pass-through of the AD duties into export prices (boarder prices) is larger than that of standard tariffs by about 41% after controlling for unobserved demand shocks. The result indicates that the exporters set their prices higher under the AD duties in order to raise the future refund, which in turn increase their future profits through the evolution of the importers' anticipation.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".