COVID-19 Mortality and Economic Losses: The Role of Policies and Structural Conditions
Bibliographic record
Abstract
The response of governments to the COVID-19 outbreak was foremost oriented to two objectives: saving lives and limiting economic losses. However, the effectiveness and success factors of interventions were unknown ex-ante. This study aims to shed light on the drivers of countries’ performances during the first year of the COVID-19 pandemic. We measure performances by excess mortality and GDP growth adjusted for additional fiscal stimulus. We conduct an empirical analysis in two stages: first, using hierarchical clustering, we partition countries based on their similarity in health and economic outcomes. Second, we identify the key drivers of outcomes in each country cluster by regression analysis, which include linear, least absolute shrinkage and selection operator (LASSO), and logit models. We argue that differences in countries’ performances can be traced back both to policy responses to COVID-19 and structural conditions, the latter being immutable over the pandemic. Three relevant structural conditions emerge from the results: trade reliance on services, corruption, and the size of the vulnerable population (elderly, low-income, smoking, or cardiovascular-failing). Policies such as large-scale open public testing and additional fiscal stimulus in non-health could help reduce excess mortality, which might lead to lower economic losses.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".