RAPID: Income and Poverty in the COVID-19 Pandemic

RAPID：COVID-19 大流行期间的收入和贫困

• 批准号: 2033893
• 负责人: Bruce Meyer
• 金额: $ 19.97万
• 依托单位: University of Chicago
• 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: 2020-06-15 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033893&HistoricalAwards=false
• 关键词: RAPID; Income; Poverty; COVID; 19

AbstractIn nine weeks at the start of the COVID-19 pandemic 39 million people filed for unemployment insurance, while GDP for the first quarter of 2020 was down 4.8%. The federal government responded with unprecedented stimulus legislation that committed more than two trillion dollars to countering the effects of the pandemic. Effectively targeting and calibrating the government's fiscal response requires tracking changes in income and poverty by demographic group and geography in a timely way, which there is extremely limited ability to do. Official estimates of income and poverty for 2020 will not be available until September of 2021. This project responds directly to this need to develop new processes to help address the economic impact of the COVID-19 pandemic by providing timely and accurate information on the impact of the current pandemic on income and poverty to inform the targeting of resources to those most affected. The project aims to construct new measures of the income distribution and poverty with a lag of only a few weeks using high frequency data for a large, representative sample of U.S. families. This project will have immediate impact in advancing basic knowledge in the fields of economics and related social sciences and providing tools for policy makers by constructing new, near real-time estimates of income and poverty. The project will validate new, timely measure of income by comparing historical estimates that rely on these data to estimates from data on income and consumption that have been used much more broadly. Importantly, these validation exercises will utilize new data that links survey data to tax and program data at the individual level. The validation will help researchers understand the advantages and limitations of using more timely income data to study changes in economic well-being. This project will also address key questions regarding the impacts of large economic shocks and survey income measurement, both critical elements of much economic research.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.

摘要新冠肺炎疫情爆发后的9周内，3900万人申请失业保险，而2020年第一季度的国内生产总值下降了4. 8%。联邦政府以前所未有的刺激立法作出回应，承诺投入2万多亿美元来对抗大流行病的影响。要有效地确定和调整政府的财政对策，就需要及时按人口群体和地理位置跟踪收入和贫困的变化，而这方面的能力极其有限。官方对2020年收入和贫困的估计要到2021年9月才能公布。该项目直接回应了开发新流程的需求，通过提供有关当前疫情对收入和贫困影响的及时准确信息，帮助应对COVID-19疫情的经济影响，从而将资源定向用于受影响最严重的人群。该项目的目的是利用大量具有代表性的美国家庭样本的高频数据，在几周内建立新的收入分配和贫困衡量标准。该项目将立即产生影响，通过构建新的、近实时的收入和贫困估计数，促进经济学和相关社会科学领域的基本知识，并为决策者提供工具。该项目将通过将依赖这些数据的历史估计数与更广泛使用的收入和消费数据估计数进行比较，验证新的、及时的收入计量方法。重要的是，这些验证工作将利用新的数据，将调查数据与个人层面的税收和计划数据联系起来。验证将帮助研究人员了解使用更及时的收入数据来研究经济福祉变化的优势和局限性。该项目还将解决有关重大经济冲击和调查收入衡量的影响的关键问题，这两个关键因素的许多经济研究。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

项目成果

Income and Poverty in the COVID-19 Pandemic

• DOI: 10.1353/eca.2020.0007
• 发表时间: 2020-06-01
• 期刊: BROOKINGS PAPERS ON ECONOMIC ACTIVITY
• 影响因子: 5.9
• 作者: Han, Jeehoon;Meyer, Bruce D.;Sullivan, James X.
• 通讯作者: Sullivan, James X.