RESEARCH ARTICLE Why Money Matters: A Fourth Natural Experiment
Bibliographic record
Abstract
November 17, 2006:A20) compared the behavior of money supply, nominal income and stock prices in the United States during the course of the 1920s and early 1930s with behavior in two other historical episodes, Japan in the 1980s and early 1990s and the United States in the 1990s and early 2000s. The three episodes, he argued, provided a natural experiment to test his and Anna J. Schwartz’s explanation of the Great Depression of the 1930s. I use similar data for the U.S. recession that began in the fourth quarter of 2007 as a fourth such natural experiment. What makes this episode particularly interesting are the continuing comparisons between it and the Great Depression that have been made as events unfolded. The results are clear-cut. In the recent recession, like the U.S recessions at the start of this century and the Japanese recession in the 1990s, there were no severe monetary shocks of the sort experienced in the 1930s. This recession, again like the other two, has been very much milder, and very likely will prove very much shorter than the Great Depression. This, in turn, is exactly what the Friedman and Schwartz hypothesis predicts.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".