A Reconsideration of Minsky's Financial Instability Hypothesis
Bibliographic record
Abstract
The worst and longest depressions have tended to occur after periods of prolonged, and reasonably stable, prosperity. This results in part from agents rationally updating their expectations during good times and hence becoming more optimistic about future economic prospects. Investors then increase their leverage and shift their portfolios toward projects that would previously have been considered too risky. So, when a downturn does eventually occur, the financial crisis and the extent of default become more severe. Whereas a general appreciation of this syndrome dates back to Minsky (1992) and even beyond, to Irving Fisher ( ), we model it formally. In addition, endogenous default introduces a pecuniary externality since investors do not factor in the impact of their decision to take risk and default on the borrowing cost. We explore the relative advantages of alternative regulations in reducing financial fragility and suggest a novel criterion for improvement of aggregate welfare.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".