The Determinants of Fiscal and Monetary Policies During the Covid-19 Crisis
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
As countries around the world grapple with Covid-19, their economies are grinding to a halt. For the first time since the Great Depression both advanced economies and developing economies are in recession. Governments and central banks have responded to the pandemic and the economic crisis using both fiscal and monetary tools on a scale that the world has not witnessed before. This paper analyzes the determinants of fiscal and monetary policies during the Covid-19 crisis. We find that high-income countries announced larger fiscal policies than lower-income countries. We also find that a country's credit rating is the most important determinant of its fiscal spending during the pandemic. High-income countries entered the crisis with historically low interest rates and as a result were more likely to use nonconventional monetary policy tools. These findings raise the concern that countries with poor credit histories -those with lower credit ratings and, in particular, lower-income countries -will not be able to deploy fiscal policy tools effectively during economic crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it