On the Relationship between Tariff Levels and the Nature of Mergers
Bibliographic record
Abstract
Abstract This paper employs an endogenous merger formation approach in a two-country oligopoly model of trade to examine the international linkages between the nature of mergers and tariff levels. Firms sell differentiated products and compete in a Bertrand fashion in product markets. Two effects play key roles in determining equilibrium market structure: the tariff saving effect and the protection gain effect. The balance between these two effects implies that, when foreign country practices free trade, low home tariffs yield international mergers irrespective of the substitutability levels. By contrast, when foreign tariffs are sufficiently high and products are close substitutes, national mergers obtain in the equilibrium. Unlike this asymmetric result of unilateral trade liberalization, we find that when bilateral tariffs are sufficiently low, international mergers arise. These results fit well with the fact that global trade liberalization has been accompanied by an increase in international merger activities. From a welfare perspective, we show that international mergers are preferable to national mergers and thus social and private merger incentives become aligned together as trade gets bilaterally liberalized. Finally, if countries can commit to a trade policy, they would optimally commit to a low tariff to induce international mergers when products are close substitutes while any tariff commitment is optimal when products are sufficiently differentiated.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".