Bibliographic record
Abstract
An intense debate has erupted over whether the unprecedented size of the US fiscal stimulus will cause the US economy to overheat and generate high inflation. To date, the debate has focused primarily on the United States, even though many other developed economies responded to the COVID-19 crisis with unprecedented economic stimulus packages. By some measures, Japan stands out: The total amount of its three consecutive stimulus packages is estimated to exceed 50 percent of its GDP, about twice as high as the US fiscal packages (about 26 percent of US GDP). However, overheating concerns are not being actively raised for Japan. This Policy Brief finds that although Japan’s headline number looks astonishingly high, the actual size of its discretionary fiscal measures is about 16 percent of GDP, substantially smaller than the total size of the US packages. US fiscal stimulus is the largest among Group of Seven (G7) countries relative to GDP, justifying the attention economists have given it. The United Kingdom is estimated to spend more than Japan as a proportion of GDP, but even the UK stimulus program markedly lags behind that of the United States. If additional stimulus measures making their way through the legislative process in Canada are counted, Japan’s fiscal stimulus looks even smaller and would amount to being only average in size among G7 countries. Given this and the lackluster performance of its economy in the first quarter of 2021, it is unlikely that Japan will find itself in overheating territory any time soon.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".