Heterogeneous Firm-Level Responses to Trade Liberalisation: A Test Using Stock Price Reactions
Bibliographic record
Abstract
This paper presents novel empirical evidence on key predictions of heterogeneous firm models by examining stock market reactions to the Canada-United States Free Trade Agreement of 1989 (CUSFTA). Using the uncertainty surrounding the agreement’s ratification, I show that the pattern of abnormal returns of Canadian manufacturing …firms was broadly consistent with the predictions of a class of models based on Melitz (2003). Increases in the likelihood of ratification led to stock market gains of exporting firms relative to non-exporters. Moreover, gains were higher in sectors with larger cuts in U.S. import tariffs. Decreases in the likelihood of ratification led to opposite stock market reactions. Results for the impact of Canadian tariff reductions are less conclusive but most specifications suggest that exporters also gained relative to non-exporters in response to such reductions. Translating stock market gains into implied profit changes, I find that CUSFTA increased expected per-period profits of exporters by around 6-7 % relative to non-exporters. Keywords: Heterogeneous firm models, stock market event studies, Canada-U.S. free trade agreement JEL Classifications: F12; F14; G14 This paper was produced as part of the Centre’s Globalisation Programme. The Centre for Economic Performance is financed by the Economic and Social Research Council.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".