Cross-Border Price Eects of Mergers and Acquisitions -A Quantitative Framework for Competition Policy
Bibliographic record
Abstract
Decisions of national competition authorities have important eects on other ju- risdictions. We provide a framework to quantify the domestic and cross-border eects of mergers, and to draw conclusions for the coordination of national merger policies. We develop a two-country model with many sectors. In each sector, producers vary in terms of their marginal costs, and are engaged in Cournot competition. We allow for pro…table mergers to take place subject to the non-violation of a given national competition policy. Because of trade costs and perceived dierences in qualities be- tween domestic and foreign products, mergers may have dierent consumer surplus eects in the home and the foreign country. We calibrate the model using data for the year 2002 for 167 manufacturing sectors in the U.S. and Canada. We choose parame- ters to match relevant moments in the data, including industry sales, concentration ratios and trade ‡ows. We …nd that in the majority of industries a merger approval policy based on domestic consumer surplus is too restrictive from the viewpoint of the neighboring country. We also show that adopting a supra-national policy that approves a merger if and only if it increases the sum of consumer surplus in the two countries would lead to signi…cant gains for U.S. consumers but hurt consumers in Canada. These results highlight the di¢ culties in coordinating national competition policies in a way acceptable to all participating countries.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".