Bibliographic record
Abstract
We explore how outcomes of trade policy retaliation (Nash tariff games) are affected when trade simultaneously takes places geographically across countries and through time via financial intermediation. In such models, deficits and surpluses in goods trade are endogenously determined, and retaliatory trade policy towards goods can affect these and monetary trade models show different retaliatory trade outcomes from conventional goods only models. We use a general equilibrium goods trade model, which also captures trade through time in the form of inside money as used in macro literature on one good overlapping generations models. In this model, the deficit or surplus of any country in goods trade is endogenous determined. Optimal trade policy differs from that in a conventional goods only trade model in that countries which run trade deficits in goods will have more strategic power through tariff policy (and surplus countries less) than in models with balanced trade. We calibrate such a model to China's trade with the rest of the world and explore two country tariff games using 2005 data. Results show the significant impacts on Nash outcomes of endogenizing the Chinese trade surplus in the model in this way.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".