S-Curve Dynamics of Trade: Evidence from US- Canada Commodity Trade
Bibliographic record
Abstract
The J-Curve effect is a concept used to describe the short-run effects of currency depreciation on the trade balance, i.e., an initial deterioration of the trade balance followed by an improvement. A concept close to the J-Curve is the S-Curve introduced by Backus, et al (1994) who found that the cross-correlation function between current terms of trade and future values of the trade balance is positive, but between current terms of trade and past values of the trade balance it is negative. The S-curve, however, did not receive strong support in the cases of Canada and the US Suspecting that the lack of an S-Curve pattern for each of the two countries could be to the result of using aggregate trade data, we disaggregate the trade data between the two countries by commodity and provide overwhelming support for the S-Curve in 41 out of 60 industries, that account for more than 80 per cent of the bilateral trade between the two countries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".