Fragile by design: The Political Origins of Banking Crises and Scarce Credit
Bibliographic record
Abstract
Why are banking systems unstable in so many countries--but not in others? The United States has had twelve systemic banking crises since 1840, while Canada has had none. The banking systems of Mexico and Brazil have not only been crisis prone but have provided miniscule amounts of credit to business enterprises and households. Analyzing the political and banking history of the United Kingdom, the United States, Canada, Mexico, and Brazil through several centuries, Fragile by Design demonstrates that chronic banking crises and scarce credit are not accidents due to unforeseen circumstances. Rather, these fluctuations result from the complex bargains made between politicians, bankers, bank shareholders, depositors, debtors, and taxpayers. The well-being of banking systems depends on the abilities of political institutions to balance and limit how coalitions of these various groups influence government regulations. Fragile by Design is a revealing exploration of the ways that politics inevitably intrudes into bank regulation. Charles Calomiris and Stephen Haber combine political history and economics to examine how coalitions of politicians, bankers, and other interest groups form, why some endure while others are undermined, and how they generate policies that determine who gets to be a banker, who has access to credit, and who pays for bank bailouts and rescues.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".