Comparative Analysis and Legality of Anti-poaching Agreements in the Indian Context
Bibliographic record
Abstract
Anti-poaching and wage-fixing agreements in labour markets represent a significant, yet largely unaddressed, antitrust concern in India. This paper argues that the Competition Commission of India (CCI) possesses both the legislative mandate under the Competition Act, 2002, and a compelling jurisprudential basis to proactively investigate and penalise such collusive practices. Drawing on a comparative analysis of enforcement trends in jurisdictions like the US, EU, and Canada, and a doctrinal review of Indian contract and competition law, this paper contends that the CCI’s current reluctance to engage with these issues, often deferring them to employment law, is a critical lacuna. It demonstrates that existing provisions, particularly section 3 of the Competition Act, are sufficient to address these anti-competitive agreements. The paper concludes by offering specific policy and enforcement recommendations for the CCI, urging a shift towards robust scrutiny to safeguard labour mobility, to ensure fair wages, and promote overall economic efficiency in India’s rapidly evolving labour markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".