Bibliographic record
Abstract
In the traditional models of strategic trade policy pioneered by Brander and Spencer, exports of the domestic firm, engaged in a Cournot‐Nash competition with the foreign firm in a neutral market, must be subsidized to maximize national welfare. We demonstrate that when the firms play the Cournot‐Nash game in two stochastic and positively correlated markets, it may be optimal to tax exports to the more volatile market while subsidizing it in the other. The policy combination reduces the amplitude of aggregate profit and raises the utility of the risk‐averse firm in a manner similar to the theory of portfolio choice. JEL Classification: F12, D18 Marchés d'exportation co‐reliés et politique commerciale stratégique. Dans les modèles traditionnels de politique commerciale stratégique proposés par Brander et Spencer, les exportations de la firme nationale, qui est engagée dans une concurrence à la Cournot‐Nash avec une firme étrangère dans un marché neutre, doit être subventionnée si l'on veut maximiser le niveau national de bien‐être. On montre que, quand les entreprises jouent un jeu à la Cournot‐Nash dans deux marchés d'exportation stochastiques et positivement co‐reliés, il peut être optimal de taxer les exportations vers le marché le plus volatile et de subventionner les exportations vers l'autre marché. Cette combinaison de politiques réduit l'amplitude de variation des profits agrégés et augmente l'utilité de l'entreprise qui a une aversion au risque d'une manière qui ressemble à ce qui se passe dans la théorie des choix de portefeuilles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".