Bibliographic record
Abstract
While there has been a proliferation of Free Trade Agreements (FTAs) and Preferential Tariff Agreements (PTAs) across the world, very few of them are implemented as agreed upon. In many cases, even after agreements and initial implementation, the preferential tariffs are just not utilized enough. We employ a unique transaction-level data, for the United States, Canada and European Union on the utilization of preferential tariffs in this paper to illustrate the importance of taking into account the utilization of preferential tariffs. Based on this dataset, we calculate the tariffs implied by the lack of complete utilization of preferential tariffs first at the HS6 level and then extend it to the GTAP sectoral level. We undertake some comparisons between the preferential tariffs with complete and incomplete utilization of preferences at the GTAP sectoral level. Then, we run counterfactual simulations of completely eliminating tariffs from the complete utilization level and partial utilization level of the preferential tariff rates in the sectors and countries where we find a difference between the two. Economy-wide results are analyzed and conclusions are made on the welfare implications of incomplete vis-a-vis complete utilization of preferential tariffs. We find that not accounting for utilisation of preferences over-emphasizes the welfare losses arising from unilateral tariff elimination for the countries with incomplete utilization of these preferences and the welfare gains for their partner countries. The welfare loss differences are as high as US$ 3 billion in France and welfare gain differences are twice as high for all the partner countries put together.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".