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COVID-19大流行之前，期间和之后对社区工人的经济和健康影响来测试重要的劳动力市场理论。该研究将从多个来源收集20多年来大量、详细和创新的数据集，用于该项目的实证部分。研究的第一部分重点关注不同群体的失业保险接受率，第二部分研究失业保险的慷慨性对劳动力供应、工作稳定性以及疫情之前、期间和之后的几个劳动力市场结果的影响，而第三部分研究失业保险扩大对不同群体的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的影响的理解，并帮助政策制定者更好地应对未来的经济衰退和流行病。 该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，从而有助于建立美国作为失业保险政策的全球领导者。 该研究项目结合了多个数据集，并将其与加州20多年来所有覆盖员工的个人层面数据合并，涵盖COVID-19大流行之前、期间和之后的时期。 然后，这个数据基础设施将被用来调查失业保险（UI）计划的三个基本问题：工人，企业，邻里特征，以及工人的互动如何影响失业保险计划的不同群体的接受率;失业保险计划的成本和收益对几个结果的因果影响是什么，特别是对劳动力供应，就业稳定性，工作流动性和收入;以及UI对COVID-19死亡率的影响。 通过回答这些关于失业保险计划的基本问题，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加有效提供了指导。 研究结果还有助于改善劳动力市场政策，并帮助美国成为失业保险创新的全球领导者。这项研究的结果也将大大推进在经济学和统计学领域的文献，通过改进的测量和实施新的研究设计。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.