Growing out of Crises and Recessions: Regulating Systemic Financial Institutions and Redefining Government Responsibilities
Bibliographic record
Abstract
I characterize and discuss the challenges and pitfalls we must face to grow out for good of recent and future financial crises and economic recessions. I propose a brief history of the 2008 crisis and insist on the loss of confidence within the banking and financial sector, which propagated later to the real sector. I discuss the factors underlying this loss of confidence: the failure of the Federal Reserve Board to abide by its mission; the ill-advised political interventions in mortgage markets; the leniency of (captured) financial regulators; the faulty risk management mechanisms in the banking sector; and the omnipresence of poorly designed compensation systems in the banking and financial sector. I also discuss the ways to rebuild confidence and move out of a bad and stable economic equilibrium. Considering data on gross job creation and loss in the US private sector, I challenge the sorcerer’s apprentices’ plan for reforming capitalism and I recall the key role played by creative destruction. I suggest that government deficits and economic growth are not good friends, offering a reference to the Canadian experience of the two decades 1985-2005. Finally, I discuss fiscal and regulatory reforms and propose a set of redefined roles for public/governmental and competitive/private sectors in generating a more prosperous economy. Je caractérise les défis et écueils auxquels nous devons faire face pour sortir pour de bon des crises financières et récessions économiques. Je propose une brève histoire de la crise de 2008 et insiste sur la perte de confiance au sein du secteur bancaire et financier, qui s’est propagée plus tard au secteur réel. Je présente les facteurs-clés de la crise : la défaillance du Federal Reserve Board dans la poursuite de sa mission; les interventions politiciennes dans le marché hypothécaire; la complaisance des régulateurs; la faiblesse des mécanismes de gestion de risques au sein du système bancaire; et l’omniprésence dans le secteur bancaire et financier de systèmes de rémunération mal conçus. Je discute des moyens de rétablir la confiance et de se sortir d'un équilibre économique mauvais mais stable. Considérant les données brutes sur la création et la perte d'emplois dans le secteur privé américain, je nous mets en garde contre les apprentis-sorciers en mal de réformer le capitalisme et je rappelle le rôle-clé de la destruction créatrice. Je suggère que les déficits publics et la croissance économique ne sont pas de bons comparses, donnant en référence l'expérience canadienne de la période 1985-2005. Enfin, je discute des réformes fiscales et réglementaires et je propose des rôles renouvelés des secteurs public/gouvernemental et privé/concurrentiel dans le façonnement d’une économie plus prospère.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".