Bibliographic record
Abstract
The positive link between international trade and productivity is well established. However, research on magnitude and consequences of internal trade barriers, which inhibit the efficient geographic distribution of production within a country, is limited. Unique data from Canada and China provide an ideal opportunity to measure the magnitude - and effect on productivity - of barriers to internal trade. Using a flexible, micro-founded approach, we find between-province trade costs average 30% in Canada and over 50% in China (net of distance-effects). These costs are even higher under other plausible parameter values. Internal trade costs in both countries are significantly higher in poor regions. We further adapt a new-trade model to estimate the productivity impact of these barriers. Eliminating inter-provincial trade barriers increases productivity by over 15% in the median province and by over 8% for Canada as a whole, accounting for nearly half the productivity gap with the United States. For comparison, we find these benefits are larger than lowering international trade barriers by 20%. Internal trade barriers also account for over 40% of the regional income inequality across provinces. The gains are even larger for China. Further work will investigate the extent to which high internal trade barriers in developing countries contributes to cross-country income and productivity differences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".