Defining Relevant Markets in the Digital Era: Lessons from Merger Control in Brazil, Chile and Mexico
Bibliographic record
Abstract
Abstract Relevant market definition methodology has been a thorny issue over the years for antitrust doctrine and practice. This is especially true when it comes to digital markets, in which prices may not be a significant variable for defining relevant markets. Traditional or static views of competition may result in improper market definitions, which might result in the clearance of mergers in digital markets because antitrust authorities define market shares according to traditional methods of measurement. However, this seems like a chicken-and-egg problem, because it is only by appropriately defining relevant markets that it is possible to establish if market shares are high or low. This article discusses practical implications for antitrust enforcement derived from the relevant market definitions adopted in high-profile digital market mergers in Brazil, Chile and Mexico. Many of these cases have elements of conglomerate or vertical integration that go beyond traditional demand substitution measurement and test the authorities’ ability to foresee future competitive scenarios in which digital competition and brick-and-mortar competition have become increasingly complementary. These cases also show an increasingly large role of data concentration as a driver in the attractiveness of the acquisitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".