Getting Financial Regulations Right: Avoiding Unintended Effects
Bibliographic record
Abstract
Canada’s financial system made it through the 2008 global financial crisis better than many other economies did, but Canadian regulators nonetheless hastened to introduce a spate of new regulations to increase financial stability. However, all new regulations create effects, intended and unintended, and the process in Canada for assessing the impact of new regulations is not as useful as it could be. This could lead to regulations having unforeseen and unwanted effects on efficiency and investor protection, even if stability is improved. Such unwelcome effects might be minimized with a system that better assesses impacts both before and after new regulations are enacted. While Canada’s regulatory-impact analysis system is relatively less burdensome than that of the EU, which has adopted an elaborate process to ensure full transparency in its consultation with stakeholders, with that lack of burden comes a much higher risk of unnecessarily increased compliance and administrative costs. Since regulatory changes necessarily lead to uncertainty in markets, the lack of transparency in the Canadian system is also cause for concern. Chief risk officers tend to rank inconsistent regulation as the most prominent risk factor, far higher than any other, yet Canada does not rank very well in an OECD evaluation of regulatory-impact analysis systems compared to, for instance, the U.K. In addition, the lack of transparency, particularly at the federal level, heightens the risk of “regulatory capture,” which can harm individual and business consumers of financial services as well as smaller firms. While the sort of lengthy and costly regulatory reviews required by the U.S. and EU could prove counterproductive, there are issues in the Canadian system that could be improved without going that far. For instance, the Canadian system offers a much shorter time frame for stakeholder consultations, which could feasibly be remedied without adding undue cost or complexity to the system. Canada’s system tends also to focus on the effects of regulation on large institutions, without enough regard for smaller firms, where costs of compliance and administration are usually higher, potentially decreasing competition in the financial sector. At the provincial level there tends to be less accommodation for public input before new regulations are implemented. Once regulations are in place, the Canadian system does not require a thorough assessment of the subsequent (or ex post) impacts. A better regulatory-impact assessment system in Canada is achievable without becoming overly burdensome. Beginning with a statement of clear objectives and outcomes for a proposed regulation, an improved system would then provide a clear set of measurements by which the regulation’s success will be evaluated, both before and after it is enacted. A white paper with regulatory proposals (rather than draft rules) issued to stakeholders followed by consultation with all parties to determine whether there are other options, besides the proposal, and to better assess the costs of the changed regulation, would minimize the possibility of implementing a suboptimal (or worse, damaging) regulatory change. Finally, meaningful transparency throughout the entire process so that all parties can see the entire process as well as its results will help protect against regulatory capture, would encourage participation from all those potentially affected by a rule change, and would help everyone involved appreciate the intended benefits of the proposed regulation. It is also important for purposes of transparency to republish after a new regulation is implemented the original objectives, the measurements and the evaluation research. Canada’s reputation for having a stable financial system is not enough; it is important, given the post-crisis pressures for regulatory reform, that the system also maintains efficiency and fairness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".