Trade policy in majoritarian systems: the case of the U.S.
Bibliographic record
Abstract
Abstract We provide a theory of trade policy determination that incorporates the protectionist bias inherent in majoritarian systems, suggested by Grossman and Helpman (2005). The prediction that emerges is that in majoritarian systems, the majority party favours industries located disproportionately in majority districts. We test this prediction using U.S. data on tariffs, Congressional campaign contributions, and industry location in districts represented by the majority party over the period 1989–97. We find evidence of a significant majority bias in trade policy: the benefit to being represented by the majority party appears at least as large in magnitude as the benefit to lobbying. On propose une théorie de la détermination de la politique commerciale qui incorpore le tendence protectionniste inhérent aux systèmes électoraux à scrutin majoritaire selon Grossman et Helpman (2005). La prédiction qui en ressort est que, dans un tel système, le parti de la majorité favorise les industries localisées de façon disproportionnée dans les circonscriptions détenues par la majorité. On évalue cette prédiction à l'aide des données sur les tarifs douaniers aux Etats-Unis, sur les contributions aux campagnes électorales au Congrès, et sur la localisation des industries dans les circonscriptions représentées par le parti de la majorité pour la période 1989–97. Les résultats révèlent un biais significatif dans la politique commerciale : l'avantage d'être représenté par le parti de la majorité s'avère au moins aussi important que l'avantage du lobbying. If they [politicians] are successful, they claim, as a matter of right, the advantages of success. They see nothing wrong in the rule, that to the victor belong the spoils of the enemy. (New York Senator William L. Marcy, referring to the victory of the Jackson Democrats in the election of 1828, in the U.S. Senate, 25 January 1832).1
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".