The symbolic politics of delegation: macroprudential policy and independent regulatory authorities
Bibliographic record
Abstract
This paper investigates the motivations that led policy-makers to delegate macroprudential authorities to newly created independent systemic regulatory authorities (SRAs). Three case studies are examined: the US Financial Stability Oversight Council, the European Systemic Risk Board and the UK’s Financial Policy Committee. Policy-makers’ motivations are captured by examining the specific institutional features of the newly created SRAs and by tracing the legislative debates that surrounded their creation. The findings of this empirical analysis call into question several of the conventional claims that are used to justify delegation to technocratic agencies from the functionalist and ideational scholarship. Given the limitations of the explanations based on efficiency considerations and socialisation of welfare losses, this paper suggests that the delegation of powers to SRAs was ultimately motivated by what is referred to as the ‘logic of symbolic politics.’ It is argued that the main motivation that emerges from the legislative debates for delegating this important task is that the SRAs provided a quick institutional ‘fix’ to signal to the public that in the wake of the international crisis of 2007–2009, policy-makers were redressing regulatory mistakes made prior to and during the crisis that had caused a severe deterioration of public’s wealth.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".