An Economic Investigation of the Import Licensing Methods and TRQs in Agriculture
Bibliographic record
Abstract
Tariff‐Rate Quotas (TRQs) were introduced at the outset of the Uruguay Round to support market access following the tariffication of non‐tariff barriers to trade in agriculture. TRQs created an administrative mess in which governments often discretionarily allocate import licenses to private and/or public firms. Numerous papers describe the arbitrarily chosen procedures used to allocate licenses in different countries and the resulting distorted trade patterns. However, few research efforts have formally studied the impacts of different administrative methods on welfare. Due to significant spreads between domestic and world prices, the administration of import licenses can have important strategic effects under imperfect competition. We propose a simple theoretical framework to highlight the various economic implications of two methods used by WTO members: the historical allocation and the first‐come‐first‐serve procedures. These two methods differ in their discretionary degree and, under imperfect competition, lead to different welfare implications depending on the structural parameters of an industry. Numerical simulations are provided to illustrate our findings
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".