Searching for an Appropriate Ad Valorem Equivalent for TRQs: The Case of CETA
Bibliographic record
Abstract
Tariff-rate quotas have become an increasingly popular policy instrument in contemporary trade agreements; however, the real effect of this policy tool is often not very clear. One way to consider tariff-rate quotas in policy impact assessment is the calculation of ad valorem equivalents. Ad valorem equivalents can be used with little effort to compare different policies, summarize them or use them in large-scale modelling analyses. Such an ad valorem equivalent can be calculated with the help of the fill rate of the quota. For newly applied trade agreements that are phased in over a longer period of time, the fill rates of quotas are, however, not known. This makes a prefixed model necessary. We set up a demand driven model and compare different options for calculating ad valorem equivalents of tariff-rate quotas using the example of the trade agreement between Canada and the EU. We find that a marginal tariff can serve as a good ad valorem equivalent because it produces the same imports, welfare and prices as the quota. In our case study, it is also sufficiently robust in the sensitivity analysis, especially if a simplified version of it is being used.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".