Are regulations achieving their objectives? Post Implementation Reviews – why they should be done, why they aren’t done and how to get them done
Bibliographic record
Abstract
Regulatory policy is often under-prioritised by governments, particularly when compared with the detailed focus associated with tax and spending measures. Even where a clear policy development process is adopted and applied for regulatory measures, it rarely has the same profile or attendant resources as applied to fiscal measures. This paper highlights one aspect of this regulatory policy deficit – the lack of priority given to evaluation and ex-post review of regulatory measures. Post implementation reviews (PIRs) are an essential part of the framework for ensuring best practice regulatory policy making by government and regulators. Ex post evaluation highlights whether regulations are achieving their objectives and operating as expected, or whether they are leading to unintended consequences or imposing disproportionately high costs. They inform decisions over whether to retain, revise or remove the regulation. However only 25% of OECD countries have formal requirements for PIRs and even then, an evaluation is often not undertaken for many regulatory measures. This paper reviews the different approaches to PIRs in the UK, Canada, Australia, the US and the European Union in terms of system governance, methodology and public transparency and capacity building. It highlights the methodological challenges in undertaking PIRs, in particular the importance of a well-designed monitoring and evaluation plan and the failure to feed the results of the PIR into subsequent modifications to the regulations which suggests a systematic failure in the policy making framework. The paper suggests that PIRs are not undertaken more comprehensively due to: limited political benefit, lack of prioritisation and concern over exposing previous policy failures. It proposes seven policy approaches that might lead to a more comprehensive approach to PIRs, however the common thread running through all of these approaches is the critical importance of high-level political support. Without high-level backing, statutory requirements will be variously disregarded, internal and external voices ignored and the pragmatic short-term pressures to focus on new and high profile policy measures will trump the longer-term benefits from a comprehensive approach to policy evaluation.
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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 | 0.177 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".