Using the gravity equation to differentiate among alternative theories of trade
Bibliographic record
Abstract
The simple gravity equation explains a great deal about the data on bilateral trade flows and is consistent with several theoretical models of trade. We argue that alternative theories nevertheless predict subtle differences in key parameter values, depending on whether goods are homogeneous or differentiated and whether or not there are barriers to entry. Our empirical work for differentiated goods delivers results consistent with the theoretical predictions of the monopolistic‐competition model, or a reciprocal‐dumping model with free entry. Homogeneous goods are described by a model with national (Armington) product differentiation or by a reciprocal‐dumping model with barriers to entry. JEL Classification: F10, F12 Equation de gravité et différenciation entre diverses théories du commerce international. La simple équation de gravité fournit une bonne part d'explication des flux de commerce bilatéraux et donne des résultats compatibles avec plusieurs modèles de commerce international. Les auteurs suggèrent que les diverses théories prédisent néanmoins des différences subtiles dans les valeurs de paramètres clés, selon que les biens sont homogènes ou différenciés, et qu'il y a barrières ou non à l'entrée. Le travail empirique des auteurs livre des résultats compatibles avec les prévisions théoriques du modèle de concurrence monopolistique ou du modèle de dumping réciproque avec entrée libre. On décrit les flux de biens homogènes à l'aide d'un modèle de différenciation nationale de produit à la Armington ou par un modèle de dumping réciproque avec barrières à l'entrée.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".