The Optimum Discriminatory Tariffs under the Cournot-Nash Strategy in International Trade
Bibliographic record
Abstract
This paper investigates the optimum ad valorem tariffs under the Cournot competition. There are three situations that exceptions to most-favored-nation (MFN) principle are made within the GATT framework: free trade agreement, ‘safeguard’ actions and escape clause. Hence, the issue of discriminatory tariffs has important policy implications. Most of the literature concerning the discriminatory tariffs assumes that the objective of the government is to maximize their country’s welfare by choosing the appropriate trade policy. We expand welfare-maximizing to loss-minimization model in order to comparing two types of optimum discriminatory tariff ratios. In the loss-minimization model, we assume that the objective of the government is to minimize loss in consumers’ surplus while subject to a minimum target level of tariff revenue. The aim of this paper is to show that the optimum ad valorem tariff ratio between two exporting countries can be unambiguously derived with a linear demand curve and constant marginal costs. We conclude that the welfare-maximizing tariff ratio differs from that of the loss-minimization model or a quasi-Ramsey rule. The Ramsey-like tariff ratio does not depend on the size of the intercept of market demand since its objective function is to minimize the loss in consumers’ surplus. On the contrary, the welfare-maximizing tariff ratio is dependent on the intercept since it is used to measure the total consumers’ surplus. Only when the two foreign producers have the identical marginal cost will they coincide.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".