Revisiting the Impact of Trade Liberalization and Mergers on the Malting Industry of North America
Bibliographic record
Abstract
This paper builds on the work of BUSCHENA and GRAY (1999) to look at the effects of mergers in the North American malting industry as ten firms in two separated markets merged into four firms in an integrated market. We explore the sensitivity of our results to the assumption of market power. We show that welfare gains from free trade are not generally lost but can face considerable redistribution under reasonable assumptions of market power. Varying the rival reactions did not change the result that total economic surplus with four firms after NAFTA is greater than when there were ten firms but no trade. Mergers reduce the trade gains of malt consumers and barley producers in both countries and reduce the welfare losses of malting plants in the U.S. According to our estimates, the second wave of mergers may have led to a positive total welfare effect for malting plants in Canada if the oligopoly is exercising significant market power.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".