Zrušení NAFTA: Dopady na mezinárodní obchod se zaměřením na Mexiko
Bibliographic record
Abstract
The North American Free Trade Agreement came into force in 1994 after long and emotive discussions. When Donald Trump became the US president in 2016, its future became uncertain, which motivates this paper to attempt to quantify the impact of its repeal. To do that, it uses a standard GTAP general equilibrium model and models an increase of intra-NAFTA tariffs to the derived MFN rates. It finds that NAFTA repeal would notably reduce intra-NAFTA trade and have a modest but negative impact on countries' welfare. NAFTA repeal is estimated to decrease Canadian GDP by 0.48%, US GDP by 0.39% and Mexican GDP by 0.06%. It would severely damage US-Mexico value chains and increase income inequality in Mexico by hurting unskilled workers more. Additional simulations are performed to control for variation in sectoral MFN rates and to observe the sensitivity of results to the choice of closure. The only positive of NAFTA repeal is that it might mitigate regional economic disparities in Mexico by damaging sectors concentrating their production near the US-Mexico border. 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".