Publication type
Journal Article
Authors
Publication date
November 4, 2026
Summary:
Self-reported and administratively recorded debt measures are susceptible to measurement error, while selection bias may arise if individuals who consent to data linkage differ systematically from those who do not. This study evaluates the quality of self-reported and administrative debt measures by assessing selection into linkage, agreement between data sources, and the implications of measurement differences for associations with mental health. Using Wave 4 of the UK Household Longitudinal Study linked to Credit Reference Agency (CRA) records, we compared 8 debt types across 3 analytical samples: the full analytical sample, nonconsenters sample, and linked consenters sample. Agreement between self-reported and CRA-recorded debt was assessed using misclassification, sensitivity, specificity, positive predictive value, and negative predictive value. Mental health associations were estimated using Bayesian hierarchical regression models. Respondents who consented to linkage were more likely to be older, partnered, employed, homeowners, and UK-born. Misclassification varied across debt types and was lowest for overdrafts, personal loans, and retail finance. Substantial discrepancies were observed between self-reported and CRA-recorded debt prevalence, particularly for credit cards and other running debts. Differences between data sources also influenced estimated debt-mental health associations. These findings underscore the need to account for selection and measurement error in linked debt data.
Published in
Journal of the Royal Statistical Society Series A: Statistics in Society
DOI
https://doi.org/10.1093/jrsssa/qnag123
ISSN
09641998
Subjects
Notes
© The Royal Statistical Society 2026. Published by Oxford University Press on behalf of The Royal Statistical Society.
Open Access
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Online Early
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