Economic Inequality and COVID-19\nDeaths and Cases in the First Wave: A\nCross-Country Analysis
Bibliographic record
Abstract
À partir d’un échantillon formé de différents pays à l’échelle de la planète, cette recherche étudie la relation entre d’une part les taux de cas d’infection et de décès liés à la maladie à coronavirus 2019 (COVID-19) lors de la première vague pandémique et d’autre part, l’inégalité des revenus et la pauvreté mesurées précédemment, en tenant compte d’autres facteurs sous-jacents. Si les associations estimées sont interprétées comme causales, le coefficient de Gini pour le revenu a un effet positif significatif sur les cas d’infection et de décès per capita pour les régressions utilisant l’échantillon complet et sur les cas d’infection, mais non sur les décès, lorsque les échantillons des pays membres et des pays non membres de l’Organisation de coopération et de développement économique (OCDE) sont traités séparément. Le coefficient de Gini pour la richesse a un effet positif significatif sur les cas d’infection et non sur les décès dans les deux sous-échantillons et dans l’échantillon complet. La pauvreté a, en général, de faibles effets positifs dans l’échantillon complet et dans celui des pays non membres de l’OCDE, mais la mesure de la pauvreté relative a un effet positif élevé sur les cas d’infection des pays membres de l’OCDE. L’analyse de l’écart entre les cas d’infection et de décès per capita dus à la COVID-19 lors de la première vague au Canada et les taux plus élevés aux États-Unis indique que 37 % de l’écart des cas et 28 % de l’écart des décès peuvent être attribués à un plus haut Gini des revenus aux États-Unis selon les régressions de l’échantillon complet. Abstract: The cross-country relationship of coronavirus disease 2019 (COVID-19) case and death rates with previously measured income inequality and poverty in the pandemic’s first wave is studied, controlling for other underlying factors, in a worldwide sample of countries. If the estimated associations are interpreted as causal, the Gini coefficient for income has a significant positive effect on both cases and deaths per capita in regressions using the full sample and for cases but not for deaths when Organisation for Economic Co-operation and Development (OECD) and non-OECD sub-samples are treated separately. The Gini coefficient for wealth has a significant positive effect on cases, but not on deaths, in both sub-samples and in the full sample. Poverty generally has weak positive effects in the full and non-OECD samples, but a relative poverty measure has a strong positive effect on cases in the OECD sample. Analysis of the gap between COVID-19 first-wave cases and deaths per capita in Canada and the higher rates in the United States indicates that 37 percent of the cases gap and 28 percent of the deaths gap could be attributed to the higher-income Gini in the United States according to the full-sample regressions.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".