Behavioural Remedies in Oligopolistic Markets under the Indian Merger Control Regime
Bibliographic record
Abstract
Competition authorities primarily make use of two types of remedies, namely, “structural” and “behavioural,” or a combination of the two1, before clearing mergers that are likely to cause substantial harm to competition. Of these, structural remedies have been the predominant choice. However, of late, in the wake of the digital revolution and greater emphasis on designing remedies on a case-by-case basis, behavioural remedies have witnessed increased use. To this end, this paper seeks to address the role of behavioural solutions in the oligopolistic market structure under Indian competition law, with a focus on the merger control regime. It also intends to understand and critically analyse the literature on the problem of oligopolistic markets and the approach adopted with respect to remedies employed by the competition authorities of various jurisdictions (including the European Union (EU), the United States of America (USA), Canada, South Korea, Brazil, and India) to address the problem. Furthermore, the paper aims to examine the scope and limitations of behavioural remedies and their potential role in the conditional clearance of mergers. We use the number and nature of merger control investigations in the aforementioned jurisdictions in which behavioural remedies were adopted during 2015–19 to examine the conditions under which these remedies were used. The findings indicate that there is no straitjacket rule in the design and implementation of remedies employed while assessing the potential competition harm of mergers. The incidence of the implementation of behavioural remedies varies according to, inter alia, the nature of the concerned industry, the nature of competition harm (unilateral/coordinated, vertical/horizontal concerns), and the specific facts of the case.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".