Bibliographic record
Abstract
This dissertation consists of three essays that study the economic impact of horizontal mergers.Chapters 1 and 2 address the merger paradox about merger profitability.Briefly put, we examine two oligopoly models of both price and quantity competition and demonstrate that mergers are profitable under certain conditions.To be more specific, we analyze in Chapter 1 the effects of mergers when every firm in the market faces a capacity constraint.We show that a merger has no effect on equilibrium prices and profits if pure-strategy equilibrium prevails both before and after the merger.Otherwise prices increase and the merger is profitable.Specifically if mixed-strategy equilibrium prevails both before and after the merger, the support of the price distribution will shift upward, and the post-merger price distribution of each firm will dominate its pre-merger price distribution.We show in Chapter 2 that product differentiation can also resolve the merger paradox associated with quantity competition by broadening the range of parameters over which mergers are profitable.To be more specific, we demonstrate that a merger is profitable if the number of competitors is small, and if the substitutability is high between the insiders but low between each insider and the outsiders.The post-merger prices of the insiders are higher when the substitutability is higher between the insiders or lower between each insider and the outsiders.Chapter 3 extends the model in Chapter 2 to study how efficiencies affect postmerger equilibrium.Specifically, we consider a situation where a merger reduces the marginal cost of the merging firms.We find that, depending on the degree of substitutability between the merging products, efficiencies can have opposite effects on iii post-merger prices.When the degree of substitutability is sufficiently high, efficiencies tend to reduce post-merger prices, as the conventional wisdom suggests.However, if the degree of substitutability is lower, efficiencies have the unconventional effect of raising the post-merger prices.In this case, prices rise with efficiencies and might be eventually pushed up above the pre-merger level as efficiencies grow.Our analysis suggests that measures such as pass-through rate and the Upward Pricing Pressure test might lead to misleading conclusions regarding the price effects of a merger.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".