The Impact of Vertical Theories of Harm on Investor Returns: An Event Study of US Vertical Mergers
Bibliographic record
Abstract
The welfare implications of vertical mergers have been a subject of disagreement for decades. Similar to horizontal mergers, economists need to weigh the efficiency gains relative to the market power concerns when considering the competitive effects of vertical mergers. However, in vertical mergers, regulators are also concerned with other potential harmful effects, such as input and customer foreclosure. Using an event style technique, this paper explores these vertical theories of harm by comparing the abnormal returns of acquirers, targets, and the two combined in vertical and horizontal mergers that were challenged by regulators as potentially anticompetitive. Our results indicate that abnormal returns to targets were similar between vertical and horizontal mergers, but the gains to targets relative to acquirers were far higher in vertical versus horizontal mergers (53.6% versus 39.5%). In addition, we found that exclusionary effects have a positive impact (0.24% of the dollar abnormal return) on the bargaining position of targets. In contrast, acquirers gain 0.45% and 0.39% of the dollar abnormal return relative to targets when the antitrust concern entails collusive effects or elimination of potential competition, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".