The Impact of Uncertainty on International Trade: A Case Study of The United States and Some Selected Trade Partners
Bibliographic record
Abstract
In this paper, we researched the impact of uncertainty on international trade for the US and some selected countries of its top trade partners during the period of 1985-2020. The Bloom 2009 model was used with some changes, like using the Economic Policy Uncertainty index (EPU) as the uncertainty shock in a Vector Autoregression model (VAR). \nWe used the VAR model for the United States with some of its top trade partners that are involved in a free trade agreement like Canada, Mexico, Singapore, and South Korea. Another VAR model for the United States with some Asian trade partners like China, India, and Japan, some European trade partners like France, Germany, Ireland, Italy, and the United Kingdom, and finally Brazil as the only South American trade partner in this study. \nOur findings are that trading under a trade agreement as well as without a trade agreement led to the same trend of the impact of the uncertainty for both countries on the US imports from these countries and the US exports to these countries, but the impact on the US IPM has the opposite trend compared to the US EPU. However, the US EPU has the strongest magnitude on all variables in each case of study. It is worth mentioning that the impact of the Chinese EPU on the US IPM is stronger and more significant than the US EPU.\nIn addition, the US EPU had an insignificant impact on the degree of openness, but there were some significant periods for the US IPM as a result of an increase in the US EPU.\nIn a conclusion, trading under a free trade agreement or without a free trade agreement had an insignificant impact on the direction and the magnitude of the impact on the partners’ mutual trade.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".