Bibliographic record
Abstract
In this paper we examine the issue of optimal tariffs for a small economy that trades with a large economy. We define ‘small’ and ‘large’ in the sense that the world prices are determined solely by the large country, and, therefore, the small country faces exogenously given world prices. Within this framework it is shown that there exist situations in which the small country has an incentive to behave as a Stackelberg leader by committing itself to a non‐zero optimal tariff. Although the small country is unable to directly affect world prices, by pre‐committing to a non‐zero trade tax it may induce a reduction of the large country's optimal trade tax, thereby indirectly improving its terms of trade and welfare. JEL Classification: F13, F35 Stratégies de droits de douane et petites économies ouvertes. Ce mémoire examine le problème des droits de douane optimaux pour une petite économie qui commerce avec une grande. On définit ‘petit’ et ‘grand’ en un sens économique: les prix mondiaux sont déterminés seulement par le grand pays et le petit pays fait face à des prix mondiaux exogènes. A l'intérieur de ce cadre d'analyse, les auteurs montrent qu'il existe des situations dans lesquelles le petit pays est incitéà se conduire en leader à la Stackelberg en s'engageant fermement dans une politique de droit de douane optimal différent de zéro. Même si le petit pays ne peut pas influencer directement les prix mondiaux, en adoptant une politique ferme de droits de douane positifs, il peut amener le grand pays à réduire son niveau de droit de douane optimal, et, ce faisant, améliorer ses propres termes d'échange et son propre niveau de bien‐être.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".