Modelling and predicting the competitive effects of vertical mergers: The bargaining leverage over rivals effect
Bibliographic record
Abstract
Abstract A new competitive effect of vertical mergers, based on the Nash bargaining model, has begun to play an important role in antitrust authorities’ evaluations of vertical mergers in the United States, Canada and abroad. The key idea is that a vertical merger will increase the bargaining leverage of the merged firm over its downstream rivals. Its bargaining leverage increases because it now takes into account the additional profit that its own downstream division will earn if it withholds inputs from downstream rivals, which changes its threat point in the bargaining game with downstream rivals. Consequently, the merged firm can increase the price that it charges rival downstream firms for inputs. One strong appeal of this theory is that it provides a simple and very intuitive formula to measure the upward pricing pressure caused by a vertical merger due to changes in bargaining leverage, based on variables whose values can generally be estimated using available data. This article describes this new competitive effect, which will be called the bargaining leverage over rivals (BLR) effect, and derives the upward pricing pressure formula. It also explains why this new competitive effect is distinct from the older raising rivals’ costs (RRC) effect that has been widely discussed in the economics literature, and discusses the relationship between the two different effects.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".