Outcomes for Implemented Macroeconomic Policy Responses and Multilateral Collaboration Strategies for Economic Recovery After a Crisis: A Rapid Scoping Review
Bibliographic record
Abstract
To promote postpandemic recovery, many countries have adopted economic packages that include fiscal, monetary, and financial policy measures; however, the effects of these policies may not be known for several years or more. There is an opportunity for decision makers to learn from past policies that facilitated recovery from other disease outbreaks, crises, and natural disasters that have had a devastating effect on economies around the world. To support the development of the United Nations Research Roadmap for COVID-19 Recovery, this review examined and synthesized peer-reviewed studies and gray literature that focused on macroeconomic policy responses and multilateral coalition strategies from past pandemics and crises to provide a map of the existing evidence. We conducted a systematic search of academic and gray literature databases. After screening, we found 22 records that were eligible for this review. The evidence found demonstrates that macroeconomic and multilateral coalition strategies have various impacts on a diverse set of countries and populations. Although the studies were heterogeneous in nature, most did find positive results for macroeconomic intervention policies that addressed investments to strengthen health and social protection systems, specifically cash and unconventional/nonstandard monetary measures, in-kind transfers, social security financing, and measures geared toward certain population groups.
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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 | 0.026 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".