The EU Digital Markets Act: A report from a Panel of Economic Experts
Bibliographic record
Abstract
Over the last years, several reports highlighted the market power of very large online platforms that are gatekeeping intermediaries between businesses and consumers, and the difficulty for classic competition policy tools to deal effectively with anti-competitive practices in these platforms. In response to this, the European Commission recently published a proposal for a Digital Markets Act (DMA) to complement existing competition policy tools by means of ex-ante obligations for platforms. This report presents an independent economic opinion on the DMA, from a high-level Panel of Economic Experts, established by the JRC and based on existing economic research and evidence. The Panel endorses the vision encapsulated in the DMA, including the designation of large gatekeeper platforms and a series of ex-ante obligations they should comply with. The Panel points out the challenge of striking a balance between the benefits from network effects of large platforms and the potential negative effects from anti-competitive behaviour and winner-takes-all market forces in online services. While some types of anti-competitive behaviour are well-known from classic competition cases, data-driven multi-sided platforms have found new ways of tying, bundling and self-preferencing that present new challenges. The report explores these behaviours in specific settings, including in online advertising and mobile ecosystems. It discusses ways to use valuable data gathered by platforms for pro-competitive purposes and the wider benefit of society in order to achieve a higher standard of fairness in the distribution of the social value generated by large platforms. Information asymmetry between platforms and regulators remains an issue in the effective implementation of the obligations.
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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 | 0.042 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.029 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".