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Background: COVID-19 pandemic has affected all countries across the globe in varying intensity resulting in varied numbers for total cases and deaths. Objectives: The paper aims to understand if different socioeconomic factors have a role to play in determining the intensity of COVID-19 impact. Methods: The study uses a country-wise number of corona cases and deaths and analyse them in a cross-country multivariate regression framework. It uses gross domestic product per capita, average temperature, population density, and median age as independent variables. The study uses testing data as a control variable. Results: In absence of the testing variable, higher-income countries have experienced a higher number of COVID cases. The population density, median age, climate do not have significant impact. The countries with higher population density have lower deaths. Each region shows different patterns of correlation between socioeconomic factors and COVID intensity. Conclusion: The majority of the cross-country variation can be attributed to the number of tests done by a country. The countries with high population density would have applied strict lockdowns and proactive testing to curb the deaths. The study essentially refutes claims around corona being a high-income group disease, cold-climate disease, or a disease impacting old-age patients more.
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10.1101/2020.10.22.20217430
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document_parses/pdf_json/ea4a65dd7c5c075f772410cf31879f741d8ca6f0.json
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Covid-19 and Socioeconomic Factors: Cross-country Evidence
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