Collaborative Research: Debt and Insecurity Among Vulnerable Communities During the COVID-19 Crisis

合作研究：COVID-19 危机期间弱势群体的债务和不安全感

• 批准号: 2048408
• 负责人: Jason Houle
• 金额: $ 6.61万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048408&HistoricalAwards=false
• 关键词: Collaborative; Research; Debt; Insecurity; Among

In this project, two questions are investigated: first, how financial difficulties were distributed within low-income communities before and during the COVID-19 pandemic and, second, the extent to which social welfare decisions mitigated or exacerbated debt burdens and which of these decisions were most (or least) successful in protecting low-income families from serious financial difficulties. When faced with economic insecurity, many Americans take on debt. Although access to credit can provide families a resource in times of crisis, debt is often burdensome to repay for low-income families, and can exacerbate, rather than relieve, economic strain and insecurity. With the onset of the COVID-19 crisis, many Americans faced sudden and new sources of economic insecurity, and official responses to the pandemic were diverse. This study contributes to understanding the combined effects of debt and official responses in low-income communities when a crisis strikes. One important goal of this project is to inform decision-makers about low-income debt and economic insecurity during the COVID-19 crisis in order to increase the effectiveness of future interventions. The research questions are addressed through linkages of consumer credit, alternative financial services, and state data sets. Levels of indebtedness in low-income communities are captured with zip-code level quarterly data on consumer credit, such as mortgages and credit cards. Alternative financial service use—e.g., payday and title loans—is obtained from Q1 2019 through Q4 2020. These data are linked to state and zip-level data that include sociodemographic characteristics, COVID-related constraints, and state-level social safety net procedures. Analysis involves, first, description of debt patterns in vulnerable communities in the United States before and after the onset of the COVID-19 pandemic. Second, a variety of statistical models are used to investigate associations between debt problems and official responses to the pandemic in these communities.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.

在这个项目中，研究了两个问题：第一，在COVID-19大流行之前和期间，低收入社区的财务困难是如何分布的;第二，社会福利决策在多大程度上减轻或加重了债务负担，以及这些决策中哪些在保护低收入家庭免受严重财务困难方面最成功（或最不成功）。 当面临经济不安全时，许多美国人承担债务。 虽然获得信贷可以在危机时期为家庭提供资源，但债务往往是低收入家庭偿还的沉重负担，可能加剧而不是缓解经济紧张和不安全。 随着COVID-19危机爆发，许多美国人面临突如其来的新经济不安全因素，官方对疫情的反应各不相同。这项研究有助于了解债务和官方应对危机时低收入社区的综合影响。 该项目的一个重要目标是让决策者了解COVID-19危机期间的低收入债务和经济不安全状况，以提高未来干预措施的有效性。研究问题是通过消费信贷，替代金融服务和国家数据集的联系来解决的。 低收入社区的负债水平是通过邮政编码级别的消费信贷季度数据来获取的，如抵押贷款和信用卡。 替代金融服务用途-例如，发薪日和产权贷款-从2019年第一季度到2020年第四季度获得。 这些数据与州和邮政级别的数据相关联，其中包括社会人口特征、与新冠病毒相关的限制和州一级的社会安全网程序。 分析涉及，首先，描述美国弱势社区在COVID-19疫情爆发前后的债务模式。第二，使用各种统计模型来调查这些社区的债务问题和官方对流行病的反应之间的关联。该奖项反映了NSF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。