Going the distance: Estimating the effect of provincial borders on trade when geography (and everything else) matters
Bibliographic record
Abstract
Abstract. In the presence of often‐cited provincial non‐tariff trade barriers, one should observe provincial border effects in Canada. However, using provincial trade data leads to upward biased estimates of the border effect, because intraprovincial trade is skewed towards short distance flows that are poorly estimated by gravity models. We overcome this bias by using sub‐provincial trade flows generated from a transaction‐level transportation dataset. The results show that border effects fall as geographies are more fine‐grained and uniform. In contrast to the US, where state border effects were eliminated using similar approaches, provincial border effects remain, with an implied 6.9% tariff equivalent. Résumé. Tenir la distance: estimation de l’effet frontalier provincial sur le commerce en tenant compte de la géographie. Lorsque l’on aborde la question très discutée des obstacles non‐tarifaires aux échanges commerciaux, il convient d’observer l’effet des frontières provinciales au Canada. L’utilisation de données commerciales interprovinciales se traduit néanmoins par des biais par excès au niveau de l’effet frontalier, les flux de courtes distances étant surreprésentés et mal évalués par les modèles gravitaires. Afin de résoudre ce problème d’écart systématique par excès, nous utilisons des données commerciales infraprovinciales issues d’un ensemble de données transactionnelles relatives au transport. Nos résultats montrent que l’effet frontalier diminue dès lors que les unités géographiques sont plus petites et uniformes. En utilisant les mêmes approches, on constate que l’effet frontalier disparaît aux États‐Unis. En revanche, au Canada, l’effet frontalier persiste avec un équivalent tarifaire implicite de 6,9 %.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".