Improving Data Collection of Debt and Financial Strain to Assess Health Impacts of Economic Insecurity

Project Summary/Abstract Individual and household debt plays an increasingly important role in the dynamics of inequality in the United States. However, current data limitations impede scientific understanding of the role of indebtedness in reducing or exacerbating economic insecurity. At best, existing social surveys collect

data on a subset of mainstream debts most often encountered by middle-class populations (mortgages, credit cards, auto loans, student loans), while debts common to low-income populations (payday loans, legal debts, past due bills, child support arrears, loans from employers, family, and friends) are less well represented. Without better data, scientific analyses of population health and

wellbeing may understate and misidentify crucial sources of economic insecurity and disadvantage, as well as reciprocal effects of debt with poverty, health, and family functioning. This project consists of a multi-pronged data collection and analysis effort to build a stronger data infrastructure, more

adequate and accurate measures of indebtedness, and best practices for analyzing various forms of indebtedness and their relation to economic hardship and financial strain for low-income families. The project has three Specific Aims: (1) To conduct qualitative cognitive and developmental interviews with low-income families on their experiences with debt and use these results to develop an

enhanced survey instrument that comprehensively and accurately captures low-income debt holding; (2) To field a two-wave pilot study designed to allow comparison of data quality and accuracy when collected via the enhanced instrument versus current ‘gold standard’ instruments (instruments will be

randomly assigned); and (3) To analyze linked pilot survey, high-quality administrative (employment, earnings, and benefit receipt), and individual credit report data to (a) document consistency of self- reported debt data collected via each instrument with credit report data; (b) examine whether consistency and differences by data collection instrument differ by respondent financial literacy and

sociodemographic characteristics (education, race); (c) estimate associations of types and amounts of debt using the enhanced module, existing modules, and credit report data with economic hardship and financial strain; and (d) investigate the causal order of associations of (the three measures of) debt with economic hardship and financial strain. The study will be the first to provide detailed data on

the full range of types and amounts of indebtedness among low-income populations and their associations with hardship and financial strain. By improving data collection and analysis, the study had the potential to identify policy levers that could reduce potential negative effects of debt on disadvantaged families and better target interventions to lessen economic insecurity and improve

well-being.