Trade Liberalization and Industrial Restructuring through Mergers and Acquisitions (JOB MARKET PAPER)
Bibliographic record
Abstract
This paper analyses mergers and acquisitions (M&A) as a previously neglected channel of industrial restructuring in the face of trade liberalization. Using the Canada-United States Free Trade Agreement of 1989 as a natural experiment, I provide empirical evidence that trade liberalization leads to signi…cant increases in M&A activity. I also show that resources are reallocated from less to more productive …rms in the process and that the amount of reallocation is quantitatively important. Taken together, these results suggest that M&A is an important …rm-level alternative to the previously studied adjustment channels of establishment exit and contraction. This has strong implications for the cost-bene…t analysis of trade liberalization episodes since M&A may o¤er a more e ¢ cient way of reallocating resources than establishment contraction and closure by low productivity …rms combined with internal growth of more e ¢ cient …rms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".