The Cost of Coronavirus Uncertainty: The High Returns to Clear Policy Plans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Policymakers face an extremely uncertain environment during COVID‐19. Using a nonlinear VAR estimate for the Euro Area, we argue that the benefit of reducing policy uncertainty at a time dominated by pessimistic expectations amounts to several points of GDP. The impact on the economy of uncertainty shocks is much larger during periods of negative outlook for the future. We estimate the impact on industrial production of the current COVID‐19 induced uncertainty to peak at a year‐over‐year growth loss of −15.4 per cent in September 2020, and to lead to a fall in CPI inflation between 1 per cent and 1.5 per cent. Policies providing state‐contingent scenarios ready to be adopted if the worst‐case outcomes materialise can reduce the impact of uncertainty .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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 it