A Tale of Two Countries and Two Booms, Canada and the United States in the 1920s and the 2000s
Bibliographic record
Abstract
The paper examines the experience of Canada and the United States in the run-up to the two biggest financial crises in global history, in the 1920s and 2000s, and the roles of their monetary and financial stability policies.Comparing the Canadian and the U.S. experiences over the two periods is instructive because Canadian monetary policy was somewhat more conservative than U.S. monetary policy and there were important institutional differences in the two periods: Canada did not have a central bank in the 1920's and followed different financial stability policies in the 2000's.We present evidence that suggests two conclusions.Firstly, a more moderate Canadian monetary policy in the two booms affected Canada's relative macroeconomic performance during the booms; in particular, the extent of the economic expansion was less.Secondly, this difference, however, by itself, does not explain why Canada fared better in the recent crisis, but not in the Great Depression.Indeed, the comparative evidence suggests that it was the difference in the effectiveness of financial stability policies, primarily financial regulation supervision with respect to banks and housing finance, that explains the better Canadian performance during the recent crisis.In contrast, in the 1920s, both countries lacked the financial policies to control excess credit growth and both suffered as a consequence.In addition, both countries made policy mistakes in aftermath of the stock market crash and credit collapses; in particular, Canada pursued inflexible interest and exchange rate policies that aggravated the economic downturn.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".