Toward a more robust competition policy regime for Hong Kong
Bibliographic record
Abstract
Abstract After years of debate, Hong Kong’s new competition law, the Competition Ordinance (CO), took effect in December 2015. Laying out rules to support competitive markets and creating the institutions to administer and enforce those rules, the CO is a modern competition law in many respects, following many best-practices and respecting recent learning in competition economics. This article argues, however, that—at least from an economist’s perspective—in its drafting a series of decisions were made that weaken the law. None is that unusual or critical on its own, however collectively they leave the law less powerful than competition enthusiasts might desire in a modern market economy. We discuss the implications of these decisions and go on to consider some other more unique aspects of the law that might need reconsideration at some point. Finally, we document and discuss the early activities of the Competition Commission of Hong Kong. We conclude that Hong Kong is off to a good start with its new law and its enforcement but that several reforms have the potential to bring a more robust competition policy regime.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".