A gravity analysis of inter-provincial trade
Bibliographic record
Abstract
In this paper, we provide evidence of frictions associated with trade in goods and services among Canadian provinces. We examine empirical relationships between sector- and industry-level trade flows and trading frictions associated with intra-provincial trade, inter-provincial trade, and international trade. We also develop a novel method for estimating the magnitude of differences across provinces, industries, and time in relative inter-provincial trade frictions. We find that the ranking of these relative inter- provincial frictions across provinces and the degree of regional dispersion varies considerably across the sectors and industries we study. In addition, we find considerably more geographic dispersion in the frictions that provinces face as sellers of goods and services than those which they face in their roles as buyers. Finally, we evaluate quantitative associations between two Canadian inter-provincial regional trade agreements and inter-provincial trade flows for a variety of industries. We document considerable variation across sectors and manufacturing sub-industries in our estimates of the relationships between these provincial trade agreements and trade flows. For example, trade agreements signed among western provinces around 2010 are positively associated with trade flows in the mining sector, textiles, petroleum, and transportation equipment, but are negatively associated with trade flows in agricultural goods and manufactured food products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".