Bibliographic record
Abstract
How large are the gains from trade? Do all trade models have the ‘same old gains’? Arkolakis, Costinot, and Rodriguez-Clare (2012) show that many quantitative trade models that summarize trade responses via a single elasticity have the same welfare implications. I develop a flexible approach to estimating trade responses using a translog expenditure function, and find welfare results that differ starkly from conventional trade models. In my model, trade responses can vary bilaterally, and the link between own- and cross-price elasticities of trade to trade cost is broken. I apply my approach to inter-regional trade flows in North America and international trade flows between OECD and BRICS countries. I structurally estimate the parameters and conduct counterfactual analyses. Canada’s border effect is at least three times smaller than estimates in previous literature. Compared to those implied by the formula in Arkolakis et al., welfare responses are larger and more heterogeneous. The welfare losses from raising trade barriers are underestimated by eight times for China, France, India, and the United Kingdom, and underestimated by more than ten times for Australia, Brazil, Canada, and Russia.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".