Optimum financial areas: Retooling the governance of global finance
Bibliographic record
Abstract
Abstract This article analyses the political economy of financial stability under conditions of deep cross‐border market integration, adapting the ‘joint products’ approach of Broz among others. Many argue that financial stability is a public good; we propose that it is inherently excludable and that particular conditions must obtain to ensure it is non‐diminishable for all. The difficulties of providing financial stability arise because of the ‘club goods’ nature of monetary and financial systems. We then propose six institutional preconditions that can stabilise a financial market that is integrated across multiple regulatory jurisdictions. We use case studies of Great Britain, the US and Canada to show how national governments have dealt with these political economy dilemmas to stumble towards similar arrangements to stabilise domestic financial market integration. Three criteria relate to the ‘technical substructure’ of markets, while three others focus on macro‐prudential considerations. Together they constitute necessary and sufficient conditions for the provision of financial stability. These criteria generate political economy obstacles both individually and as an interdependent package but can mitigate the costly dynamics of financial market disintegration in times of crisis. We argue that these criteria can be applied across national boundaries as well as across regulatory jurisdictions within them.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".