From Great Depression to Great Credit Crisis: similarities, differences and lessons
Bibliographic record
Abstract
The Great Depression of the 1930s and the Great Credit Crisis of the 2000s had similar causes but elicited strikingly different policy responses. While it remains too early to assess the effectiveness of current policy, it is possible to analyse monetary and fiscal responses in the 1930s as a natural experiment or counterfactual capable of shedding light on the impact of current policies. We employ vector autoregressions, instrumental variables, and qualitative evidence for 27 countries in the period 1925–39. The results suggest that monetary and fiscal stimulus was effective -- that where it did not make a difference it was not tried. They shed light on the debate over fiscal multipliers in episodes of financial crisis. They are consistent with multipliers at the higher end of those estimated in the recent literature, and with the argument that the impact of fiscal stimulus will be greater when banking systems are dysfunctional and monetary policy is constrained by the zero bound. — Miguel Almunia, Agustín Bénétrix, Barry Eichengreen, Kevin H. O’Rourke and Gisela Rua
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".