Comparing the Role of Economics/Effects-Based in Antitrust Enforcement and Its Relation to the Judicial Review in the EC to Other Countries*
Bibliographic record
Abstract
The last 25 or so years are widely considered as witnessing, in many jurisdictions throughout the world, a substantial increase in the role of economists and, though this view has not relied on much formal empirical backing, even in the extent and sophistication of economic analysis applied in the assessment of cases and in reaching decisions in competition law (CL) enforcement. A few countries, such as the USA and Canada, are generally thought of as leading the way in this regard. But whilst this view, or, better, hypothesis, can be thought of as uncontroversial for merger control, it is far from uncontroversial for antitrust enforcement in many jurisdictions.1 This paper contributes a comparative empirical investigation of the role of economics in the antitrust enforcement decisions of the EC, France, Greece, and Russia.2 The extent to which the assessment of conducts in specific antitrust cases relies on economic analysis depends on the legal standard (LS) (or decision rule) that is used to make the assessment. The theoretical analysis of the choice of LSs by Competition Authorities (abbreviated, henceforth, to CAs) and courts has been to a large extent normative analysis, looking at the determination of optimal LSs, either from the point of view of error-cost minimisation3 or welfare maximisation.4 More recently, there have also been contributions in the positive analysis of utility maximising CAs making LS choices that reflect their reputational and operational cost-minimisation concerns.5
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".