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.
