Trade Liberalization and the Profitability of Mergers: a Global Analysis*
Bibliographic record
Abstract
Abstract We analyze the effects of bilateral tariff reductions on the profitability of cost‐reducing horizontal mergers. Given Cournot competition in a two‐country world, for any positive tariff below a certain threshold, marginal trade liberalization is shown to encourage only those domestic mergers with sufficiently large cost‐savings and to discourage the rest. For tariffs close to, but smaller than, the prohibitive tariff, however, marginal trade liberalization necessarily encourages all domestic mergers. Moreover, we show that for a given level of cost‐savings, the impact of marginal trade liberalization may not reliably predict that of nonmarginal liberalization. Although at high tariffs, domestic mergers are shown to be unambiguously more profitable than cross‐border mergers, near free trade, mergers which yield the most cost‐savings become the most profitable. Thus, when comparing domestic and cross‐border mergers, trade liberalization encourages the type which yields the most cost‐savings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".