HNDS-R - Collaborative Research: An Integrated Analysis of the COVID-19 Crisis on Labor Market Outcomes and Mortality

这项研究通过评估失业保险(UI)计划在新冠肺炎大流行之前、期间和之后对社区工人的经济和健康影响来检验一个重要的劳动力市场理论。这项研究将为该项目的实证部分收集20年来来自几个来源的大量、详细和创新的数据集。研究的第一部分集中于不同群体对尿毒症的接受率，第二部分研究了艾滋病大流行前、中、后的慷慨程度对劳动力供给、工作稳定性和几个劳动力市场结果的影响，第三部分研究了艾滋病扩大对不同群体CODI-19死亡率的影响。这项研究的结果将大大增加我们对UI计划对个人、社区和州一级行为者影响的理解，并帮助政策制定者更好地应对未来的经济衰退和流行病。这一研究项目的成果将对劳动经济学做出重大贡献，并有助于更好地设计失业保险政策，从而有助于将美国确立为UI政策的全球领导者。这项研究项目结合了几个数据集，并将其与加州所有受访员工20年来的个人数据合并，这些数据涵盖了新冠肺炎大流行之前、期间和之后的一段时间。然后，这些数据基础设施将被用来调查有关失业保险(UI)计划的三个基本问题：工人、公司、邻里特征和工人互动如何影响不同群体对UI计划的接受率；UI对成本和收益对几个结果，尤其是对劳动力供应、就业稳定性、职业流动性和收入的因果影响；以及最后，UI对新冠肺炎死亡率的影响。通过回答关于失业保险计划的这些基本问题，本研究项目对劳动经济学做出了重大贡献，并为如何改进失业保险政策以使其更有效提供了指导。这一结果还可能有助于改善劳动力市场政策，并帮助美国在失业保险创新方面确立全球领先地位。这项研究的结果还将通过改进新研究设计的测量和实施，极大地促进经济学和统计学领域的文献。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research tests an important labor market theory by evaluating the economic and health impacts of unemployment insurance (UI) programs on workers across communities before, during, and after the COVID-19 pandemic. The study will assemble a large, detailed, and innovative data set over 20 years from several sources for the empirical part of the project. The first part of the study focuses on take-up rate of UI by various groups, the second part studies the impact of the generosity of UI on labor supply, job stability, and several labor market outcomes before, during and after the pandemic, while the third part studies the effects of UI expansion on CODI-19 mortality rates among different groups. The results of this research will substantially increase our understanding of the effects of the UI program on individual, neighborhood, and state-level actors, and help policymakers better respond to future recessions and pandemics. The results of this research project will make significant contributions to labor economics as well as contribute to better design of unemployment insurance policies, hence help to establish the US as a global leader in UI policies. This research project combines several data sets and merges it with individual-level data for all covered employees in California, over 20 years, that covers periods before, during, and after the COVID-19 pandemic. This data infrastructure will then be used to investigate three fundamental questions about unemployment insurance (UI) program: how does worker, firm, neighborhood characteristics, and worker interaction affect the take-up rate of UI programs by different groups; what is the causal impact of UI on the cost and benefits on several outcomes, especially on labor supply, employment stability, job mobility and earnings; and finally the effects of UI on COVID-19 mortality. By answering these fundamental questions about UI programs, this research project makes significant contributions to labor economics and provides guidance on how to improve unemployment insurance policies to make them more effective. The results could also help improve labor market policies as well as help establish the US a global leader in unemployment insurance innovation. The results of this study will also substantially advance the literature in the fields of economics and statistics, through improved measurement and implementation of new research designs.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.