RAPID: Understanding the Disparate Impact of COVID-19

RAPID：了解 COVID-19 的不同影响

• 批准号: 2039851
• 负责人: William Darity
• 金额: $ 19.99万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039851&HistoricalAwards=false
• 关键词: RAPID; Understanding; Disparate; Impact; COVID

There is anecdotal evidence that the COVID-19 pandemic has disproportionately affected communities of color and poor communities than relatively affluent communities. This research will use two projects to systematically study the causes and consequences of disparate effects of the COVID-19 pandemic on different socioeconomic groups. The first project uses new cross state survey data to investigate whether access to public health insurance for the poor reduces the chances that individuals contract the COVID-19 disease. The unique way the researchers collect the data and the methodology they use allows them to establish a causal relationship between public health insurance access and COVID-19 spread. The second project will use a different cross-state survey data to investigate the effects of COVID-19 on political and ideological preferences. The results of this research project will provide important inputs into policies to reduce the disparate effects of the pandemic on different groups and indirectly reduce the rate of spread and its negative impact in the aggregate.This research project investigates the disparate effects of COVID-19 on different communities. The project will collect data from adjoining counties across states that expanded Medicaid under the Affordable Care Act (ACA) and those that did not and use regression discontinuity (RD) design to estimate the causal effects of access to public health insurance on the probability of getting COVID-19. The second project will use panel survey data to investigate the effects of the COVID-19 pandemic on political ideology, beliefs, and participation. This project is based on panel data collected from 1000 individuals in 8 states. The sampling frame, and details of the data collected allows the PIs to establish a causal relationship while controlling for a wide variety of socioeconomic and policy variables. By studying the causes and consequences of the disparate effects of COVID-19, this research provides a broader and more nuanced effects of COVID-19 than had hitherto been provided. The results of this research project will provide important inputs into policies to reduce the disparate effects of the pandemic on different groups and indirectly reduce the rate of spread and its negative impact in the aggregate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

有传闻证据表明，COVID-19大流行对有色人种社区和贫困社区的影响比对相对富裕社区的影响更大。 该研究将使用两个项目系统地研究COVID-19大流行对不同社会经济群体的不同影响的原因和后果。 第一个项目使用新的跨州调查数据来调查穷人获得公共医疗保险是否会降低个人感染COVID-19疾病的机会。 研究人员收集数据的独特方式和他们使用的方法使他们能够在公共医疗保险的获得和COVID-19传播之间建立因果关系。 第二个项目将使用不同的跨州调查数据来调查COVID-19对政治和意识形态偏好的影响。该研究项目的结果将为政策提供重要投入，以减少疫情对不同群体的不同影响，并间接降低传播速度及其总体负面影响。该研究项目调查了COVID-19对不同社区的不同影响。 该项目将收集各州毗邻县的数据，这些县根据《平价医疗法案》（ACA）扩大了医疗补助计划，而那些县没有扩大医疗补助计划，并使用回归不连续性（RD）设计来估计获得公共医疗保险对感染COVID-19概率的因果影响。第二个项目将使用小组调查数据来调查COVID-19大流行对政治意识形态、信仰和参与的影响。 该项目基于从8个州的1000名个人收集的面板数据。 抽样框架和收集到的数据细节使PI能够在控制各种社会经济和政策变量的同时建立因果关系。 通过研究COVID-19不同影响的原因和后果，这项研究提供了比迄今为止提供的更广泛和更细微的COVID-19影响。 该研究项目的成果将为减少大流行对不同群体的不同影响的政策提供重要投入，并间接降低传播速度及其总体负面影响。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。