2025-06-16-美联储-县级收入定义对低收入社区分析的适用性（英）_57页_1mb

Suitability of a County-Level Income Definition for Analysis of Lower-Income Communities

Introduction

• Examines the use of a simple, county-level income definition (population-weighted, bottom 25% of national household income quartile) for analyzing economic disparities.
• Compares performance across geography (e.g., commuting zones vs. census tracts) and cost-of-living adjustments (RPP), and applies it to COVID-19 recovery analysis.

Key Findings

1. Definition and Validation

• Method: Population-weighted county income groups (bottom 25%, middle 25%, rest top half).
• Data Sources: Uses American Community Survey (ACS) and Small Area Income Estimates (SAIPE).
• Validation:
  • Correlation with economic indicators: Higher unemployment, lower college grads/share prices, poorer credit access in bottom quartiles.
  • Geographic robustness: Broad trends align across scales (commuting zones > counties > tracts), with larger differences at finer scales.
  • Cost-of-living adjustment (RPP): Minimal impact on economic well-being metrics, but shifts urban/affordable counties into or out of low-income groups.

2. Geographic Sensitivity

• Income Disparities: Strongest in census tracts; counties act as a "middle ground" between neighborhoods and labor markets.
• Exceptions: Homeownership shows less variation at county level due to smaller-scale segregation effects.
• Demographics: Hispanic share differs by tract vs. county due to within-county heterogeneity.

3. COVID-19 Application

• Financial Distress: Higher utilization/delinquency rates persist in low-income counties despite relief measures.
• Recovery Trends: Smaller improvements in financial distress for low-income counties post-pandemic.

Conclusion

• The county-level income definition offers a practical, population-weighted approach to analyzing economic disparities.
• Cost-of-living adjustments provide limited improvement, and geographic scale choice depends on the research question.
• Caution needed for metrics sensitive to local variation (e.g., homeownership, neighborhood-specific factors).

Citation: Troland et al. (2025). Finance and Economics Discussion Series, 2025-039.