Bibliographic record
Abstract
Borders affect the composition, not only the level, of interregional trade. In disaggregated U.S. Commodity Flow data, border effects vary substantially across commodities. Substantial border–induced compositional change suggests the possibility that standard estimates suffer from aggregation bias arising from endogenous industry location patterns and the presence of zero observations in commodity–level trade. Adjusting for these effects reduces the estimate of the aggregate border effect from 20.9 to 5.7. JEL Classification: F14, F15 Biais d’agrégation, changement de composition, et effet de frontières. Les frontières affectent la composition et pas seulement le volume de commerce inter‐régional. Une analyse des données désagrégées des flux de commerce de biens des Etats‐Unis montre que l’effet de frontières varie substantiellement selon les biens. Le changement de composition du commerce engendré par les frontières suggère la possibilité que les estimations usuelles souffrent d’un biais d’agrégation résultant des patterns de localisation industrielle endogènes et de la présence d’observations nulles dans le commerce de certains biens. Un ajustement pour tenir compte de ces effets suggère que les effets de frontières passent de 20.9 à 5.7.
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 | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".