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Discussion Paper
Improving Housing Payment Projections during the COVID-19 Pandemic
COVID-19 caused widespread job loss, raising concerns about whether people can still cover their rent or mortgage expenses. Policymakers and researchers are working to understand the effects of the pandemic on housing payments and predict future payment rates.
Working Paper
Targeted Relief: Geography and Timing of Emergency Rental Assistance
In response to the COVID-19 pandemic, Congress established the Emergency Rental Assistance (ERA) program, which provided nearly $45 billion to prevent evictions and increase housing stability. We provide new evidence on the implementation of ERA by examining the fine-grained geographic distribution of ERA funds and the timing of ERA expenditures by state and local governments. Using administrative data on ERA transactions, we find that ERA sent more funds per renting household to census tracts with higher pre-pandemic eviction filing rates, higher poverty rates, higher shares of Black ...
Working Paper
Place-Based Labor Market Inequality
This paper presents an overview of how various labor market indicators differ across geography. While many indicators are often discussed in terms of national aggregates, such discussions obscure the large degree of variation that exists across localities. We primarily use counties as a geographic unit, and document both structural differences that persist over time as well as differences in the past two business cycles. The racial composition of communities plays a large role in explaining geographic differences in labor market indicators, in some cases even more so than income. We ...
Working Paper
Suitability of a County-Level Income Definition for Analysis of Lower-Income Communities
This paper examines the costs and benefits of using a straightforward county-level income definition in the classification and study of lower-income communities. A definition based on population-weighted distribution of county-level median household incomes does a good job of identifying the most economically disadvantaged communities across a wide range of indicators. We show robustness to the use of different thresholds, levels of geography, and cost-of-living adjustments.