Trade Liberalization and Heterogeneous Firm Models: An Evaluation Using the Canada - US Free Trade Agreement
Bibliographic record
Abstract
We examine the qualitative and quantitative predictions of a heterogeneous firm model à la Melitz (2003) in the context of the Canada- US Free Trade Agreement (CUSFTA) of 1989. We calibrate our model to the pre-trade liberalization stage, simulate the trade liberalization, and compute the resulting growth rates of Canadian industry productivity, exports and imports. We compare them with Trefler’s (2004) estimates of the effects of CUSFTA. Our results show that our model performs well in replicating the qualitative aspects of Trefler’s results. In particular, we correctly predict that US tariff cuts have smaller productivity enhancing effects than Canadian tariff reductions due to the entry of less efficient exporters. Quantitatively, the model tends to underpredict the impact of CUSFTA on growth rates of productivity, but overpredicts the increase in Canadian exports and imports. We discuss how liberalization-induced changes in the firm-level productivity distribution can reconcile the model with the evidence.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".