Can subsidies for MARs be procompetitive?
Bibliographic record
Abstract
In contrast to recent literature, we show that market access requirements (MARs) can be implemented in a procompetitive manner even in the absence of threats in related markets. By focusing on subsidies that are paid only when the requirement is met, we show that a MAR can increase aggregate output relative to free trade provided that the right set of firms is targeted. In the context of a model with multiple Japanese and U.S. firms, we show that a MAR on U.S. imports is procompetitive as long as the U.S. firms are the ones targeted to receive the subsidy. JEL Classification: F13 Est‐ce que les subventions pour promouvoir la pénétration du marchéétranger peuvent promouvoir la concurrence? Contrairement à ce qui ressort de la littérature spécialisée récente, les auteurs montrent que l'on peut mettre en place des mesures pour promouvoir la pénétration du marchéétranger de telle manière que cela promeuve la concurrence, et ce même en l'absence de menaces dans des marchés reliés. En mettant l'accent sur les subventions payées quand les objectifs de pénétration sont remplis, on montre que de telles pratiques peuvent accroîre le niveau de production par rapport à ce qu'il serait dans un monde de libre‐échange pourvu que le bon ensemble de firmes soit ciblé. Dans un modèle où coexistent plusieurs firmes américaines et japonaises, on montre que de tels efforts quand ils portent sur les importations en provenance des Etats‐Unis promeuvent la concurrence tant que ce sont les firmes américaines qui reçoivent la subvention.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".