RAPID: Income and Poverty in the COVID-19 Pandemic

在新冠肺炎大流行开始的9周内，有3900万人申请失业保险，而2020年第一季度GDP下降了4.8%。联邦政府采取了前所未有的刺激立法，承诺投入2万亿多美元来应对疫情的影响。有效地定位和调整政府的财政应对措施，需要及时跟踪人口群体和地理位置的收入和贫困变化，而这方面的能力极其有限。2020年的官方收入和贫困估计要到2021年9月才能公布。本项目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，为将资源定向用于受影响最严重的人群，直接响应了开发新流程以帮助应对COVID-19大流行经济影响的需求。该项目旨在利用大量具有代表性的美国家庭样本的高频数据，构建仅滞后几周的收入分配和贫困的新指标。该项目将对促进经济学和相关社会科学领域的基础知识产生直接影响，并通过构建新的、接近实时的收入和贫困估计，为决策者提供工具。该项目将通过比较基于这些数据的历史估算值与更广泛使用的收入和消费数据估算值，验证新的、及时的收入衡量标准。重要的是，这些验证练习将利用将调查数据与个人层面的税收和计划数据联系起来的新数据。验证将有助于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点和局限性。该项目还将解决有关大型经济冲击和调查收入计量的影响的关键问题，这两个都是许多经济研究的关键要素。该奖项反映了美国国家科学基金会的法定使命，并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。

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.