Uncertainty, arbitrage and intra–industry trade
Bibliographic record
Abstract
When firms in the same industry located in different regions or countries experience shocks to production costs in their respective industries that are imperfectly correlated, arbitrage opportunities automatically lead to trade. Trade can either stabilize or destabilize the price faced by producers in a given country. Producers’ surplus is affected, owing to the ‘variance–covariance’ effect, while consumers’ surplus is more directly affected through the variance of the product price. We examine how consumers’ surplus, producers’ surplus, and social welfare are affected when the regions switch from autarky to free trade in the presence of industry and region–specific cost shocks. JEL Classification: F10, D80 Incertitude, arbitrage et commerce intra–industrie. Quand les entreprises d’une même industrie localisées dans diverses régions ou différents pays font face à des chocs, qui ne sont pas parfaitement co–reliés, dans leurs coûts de production, les possibilités d’arbitrage entraînent automatiquement un accroissement du commerce. Ce commerce inter–régional ou international peut soit stabiliser ou déstabiliser les prix auxquels les producteurs font face dans un pays donné. Le surplus aux producteurs est directement affecté via l’effet de «variance–covariance», alors que le surplus aux consommateurs est plus directement affecté via la variance du prix du produit. Ce mémoire examine comment le surplus aux producteurs, le surplus aux consommateurs, et le niveau de bien–être social sont affectés quand les régions passent de l’autarcie au libre échange dans le cas où existent des chocs dans les coûts qui sont spécifiques à l’industrie et à la région.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 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".