Putting Canada in the penalty box: Trade and welfare effects of eliminating North American Free Trade Agreement
Bibliographic record
Abstract
Abstract Three years ago, very few economists would have imagined that one of the newest and fastest growing research areas in international trade is the use of quantitative trade models to estimate the economic welfare losses from dissolutions of major countries' economic integration agreements (EIAs). In 2016, "Brexit" was passed in a UK referendum. Moreover, in 2019, the existence of the entire North American Free Trade Agreement (NAFTA) is at risk if the US withdraws—a threat President Trump has made if the proposed US–Mexico–Canada Agreement is not passed by the US Congress. We use state‐of‐the‐art econometric methodology to estimate the partial (average treatment) effects on international trade flows of the six major types of EIAs. Armed with precise estimates of the average treatment effect for a free trade agreement, we examine the general equilibrium trade and welfare effects of the elimination of NAFTA (and for robustness US withdrawal only). Although all the member countries' standards of living fall, surprisingly the smallest economy, Mexico, is not the biggest loser; Canada is the biggest loser. Canada's welfare (per capita income) loss of 2.11% is nearly two times that of Mexico's loss of 1.15% and is nearly eight times the US' loss of 0.27%. The simulations will illustrate the important influence of trade costs—international and intranational—in contributing to the gains (or losses) from an EIA's formation (or elimination).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".