Heterogeneity and Applied General Equilibrium Models of Trade: Lessons from the Canada-US Free Trade Agreement
Bibliographic record
Abstract
Applied General Equilibrium models of trade failed to predict the sec-toral changes in trade volumes following the Canada-US Free Trade Agreement. These models utilized a representative firm framework and used econometric estimates for the elasticities of substitution be-tween home and foreign goods. I take a different approach on both fronts, modeling plants as heterogeneous and calibrating the elastic-ities to match estimated markups in each sector. I introduce these features by adapting a Hopenhayn (1992) model of plant entry and exit and embed this in a multisector trade model. The resulting model is very similar to Melitz (2003), but I focus on quantifying the effects of trade liberalization on trade flows and industrial structure. I cal-ibrate the model using trade data between the United States and Canada before their Free Trade Agreement and evaluate the model’s performance using later data. By successively shutting down various features of the model, I isolate the contribution of each. I find that calibrating the elasticities to markups improves the fit between model predictions and data significantly, from weighted correlations which are negative to values of 0.36. Incorporating plant heterogeneity and industrial data improves the weighted correlation to 0.77. ∗I am grateful to Timothy J. Kehoe for his guidance. I also thank Tom Holmes, Ed Prescott, and Sam Kortum for their suggestions. I thank Ron Leung and participants in the Minnesota Macro Workshop for their help. All remaining errors are my own. 1 1
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".