Asymmetric distance and business cycles (ΑDBC): A new understanding of distance in international trade models through the example of Iran's trade corridors
Bibliographic record
Abstract
We introduce a new concept of distance, and the way this could affect gravity-based trade modeling. Our motivation is twofold: a) global uncertainty in trade relations allows us to treat distance as an asymmetric shock in economic modeling; b) economies of scale in seaborne trade make geographical distance less relevant in trade models, substituted by economic distance, as this can be proxied by ocean freight rates. This, for instance, allows China to import iron ore from Brazil, at three times the distance compared to Australia. We enhance the New Keynesian Dynamic Stochastic General Equilibrium Model (DSGE) by incorporating a distance shock parameter into the transaction costs function. We test this on Iran's participation in the Shanghai Cooperation Organization as well as in the International North-South Transport Corridor. We conclude that longer physical distances do not necessarily have a negative impact on trade.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".