{"title":"Pro-Trump Partisanship and COVID-19 Mortality: A Model-Based Counterfactual Analysis","authors":"Dominik Liebl, U. Schüwer","doi":"10.2139/ssrn.3924620","DOIUrl":null,"url":null,"abstract":"We show that a higher share of Trump voters (who are less likely to comply with COVID-19 public health guidelines than Democratic voters) in a U.S. county leads to significantly more COVID-19 deaths during times of high regional infection risk. Our model-based counterfactual analysis finds that about 15 percent of the cumulative death rates in pro-Trump counties after the first year of the pandemic can be explained by a pro-Trump partisanship effect. The analysis considers demographic and socioeconomic differences between counties, unobserved heterogeneity on county and interacted week x state level, and non-linear effects due to spatiotemporal differences in infection risks.","PeriodicalId":412621,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Microeconomics - Microeconometric Studies of Health","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Econometrics: Applied Econometric Modeling in Microeconomics - Microeconometric Studies of Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3924620","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We show that a higher share of Trump voters (who are less likely to comply with COVID-19 public health guidelines than Democratic voters) in a U.S. county leads to significantly more COVID-19 deaths during times of high regional infection risk. Our model-based counterfactual analysis finds that about 15 percent of the cumulative death rates in pro-Trump counties after the first year of the pandemic can be explained by a pro-Trump partisanship effect. The analysis considers demographic and socioeconomic differences between counties, unobserved heterogeneity on county and interacted week x state level, and non-linear effects due to spatiotemporal differences in infection risks.