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Inequality in high-cost borrowing and unemployment insurance generosity in US states during the COVID-19 pandemic

Nature Human Behaviour (2024)Cite this article

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Abstract

US consumers may turn to the private market for credit when income and government benefits fall short. The most vulnerable consumers have access only to the highest-cost loans. Prior research on trade-offs of credit with government welfare support cannot distinguish between distinct forms of unsecured credit due to data limitations. Here we provide insight on credit–welfare state trade-offs vis-à-vis unemployment insurance generosity by leveraging a large sample of credit data that allow us to separate credit cards, personal loans and alternative financial services loans and to analyse heterogeneity in credit use by household income. We find that more generous state unemployment insurance benefits were associated with a lower probability of high-cost credit use during the first seven quarters of the coronavirus disease 2019 (COVID-19) pandemic. This inverse association was concentrated among consumers living in low-income households. Our results support theories that public benefits are inversely associated with the use of costly credit.

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Fig. 1: State variation in maximum UI benefits, average across states during Q2 2020–Q4 2021 (N = 50 US states).
Fig. 2: Temporal variation in maximum UI benefits, Q1 2018–Q4 2021 (N = 50 US states).
Fig. 3: State-level bivariate correlations between state UI generosity and new credit card, personal finance loan and AFS loan, Q2 2020–Q4 2021 (N = 50 US states).
Fig. 4: Associations of state UI generosity and new credit card, personal finance loan and AFS loan by Q4 2019 household income, Q2 2020–Q4 2021 (N = 16,697,611 consumer-quarter observations).

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Data availability

The credit panel data that support the findings of this study are proprietary data of the Experian Corporation and used under license for the current study and thus are not publicly available. Other scholars can obtain (for a fee) the dataset we used in this study by contacting Cathy Kelmar at Experian (Cathleen.Kelmar@experian.com). We draw UI measures from publicly available data from the US Department of Labor Office of Unemployment Insurance, ‘Significant Provisions of State UI Laws’ collection, effective January 2022 (https://oui.doleta.gov/unemploy/DataDashboard.asp)65. State and ZIP Code control variables derive from the publicly available 2019 5-year estimates from the ACS (https://www.census.gov/data/developers/data-sets/acs-5year.html)66, the publicly available University of Kentucky Center for Poverty Research National Welfare Database (https://cpr.uky.edu/resources/national-welfare-data)67, the Bonfer and Koehler Eviction Moratoria and Housing Policy data (https://www.openicpsr.org/openicpsr/project/157201/version/V2/view)62, and measures created from publicly available National Consumer Law Center Small dollar loan products reports (https://www.nclc.org/resources/predatory-installment-lending-in-the-states-2021/)63 and Center for Responsible Lending reports (https://www.responsiblelending.org/sites/default/files/nodes/files/research-publication/crl-red-alert-rates-payday-ratecap-map-jun2023.pdf)64. Benefit level measures for the supplemental analysis of Supplemental Nutrition Assistance Program come from the University of Kentucky Center for Poverty Research National Welfare Database (https://cpr.uky.edu/resources/national-welfare-data)67 and the United States Department of Agriculture (https://www.fns.usda.gov/snap/covid-19-emergency-allotments-guidance)68. UI replacement ratios come from United States Department of Labor data available on the Century Foundation’s website (https://tcf.org/content/data/unemployment-insurance-data-dashboard/)69. State and ZIP Code datasets including UI measures are available at https://github.com/OSU-UW-CCP/IneqBorrowUICOVID.

Code availability

We used Stata MP Version 15 on the Ohio Supercomputer to analyse the data available in this study. Our code is available at https://github.com/OSU-UW-CCP/IneqBorrowUICOVID.

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Acknowledgements

Support for this study was provided by the Russell Sage Foundation (L.M.B., M.B., J.M.C., R.E.D., J.H. and S.M.), the National Institute of Child Health and Human Development (R01HD103356) (L.M.B., R.E.D. and J.H.), National Science Foundation (GR122989) (R.E.D. and J.H.), The Ohio State University Institute for Population Research through a grant from the Eunice Kennedy Shriver National Institute for Child Health and Human Development of the National Institutes of Health (P2CHD058484) (M.B., R.E.D. and S.M.), the Institute for Research on Poverty at the University of Wisconsin–Madison through a grant from the US Department of Health and Human Services, the Office of the Assistant Secretary for Planning and Evaluation (1H79AE000058-01) (L.B. and J.M.C.) and the US Social Security Administration’s Retirement and Disability Research Consortium, through the University of Wisconsin–Madison Center for Financial Security (RDRC WI20-Q2) (M.B., J.M.C. and S.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the organizers and audiences of prior presentations of this work at the 2021 Annual Meeting of the Association for Public Policy Analysis and Management, the 2022 Annual Meeting of the Population Association of America, as well as seminar participants at the West Coast Poverty Center at the University of Washington. We thank V. Coan for excellent research assistance. The content is solely the responsibility of the authors and does not necessarily represent the official views or policies of the funders.

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Authors and Affiliations

  1. Sandra Rosenbaum School of Social Work and Institute for Research on Poverty, University of Wisconsin–Madison, Madison, WI, USA

    Lawrence M. Berger

  2. Department of Economics, The Ohio State University, Columbus, OH, USA

    Meta Brown

  3. LaFollette School of Public Affairs, University of Wisconsin–Madison, Madison, WI, USA

    J. Michael Collins

  4. Department of Sociology, The Ohio State University, Columbus, OH, USA

    Rachel E. Dwyer

  5. Department of Sociology, Dartmouth College, Hanover, NH, USA

    Jason N. Houle

  6. John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, USA

    Stephanie Moulton

  7. Department of Organizational Studies, University of Michigan, Ann Arbor, MI, USA

    Davon Norris

  8. Institute for Research on Poverty, University of Wisconsin–Madison, Madison, WI, USA

    Alec P. Rhodes

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Contributions

L.M.B., M.B., J.M.C., R.E.D., J.H. and S.M. designed the research. L.M.B, R.E.D., S.M., D.N. and A.P.R. performed the research. M.B., R.E.D., S.M., D.N. and A.P.R. managed the dataset construction. D.N. and A.P.R. analysed data. L.M.B., R.E.D., J.H., S.M., D.N. and A.P.R. wrote the paper.

Corresponding authors

Correspondence to Rachel E. Dwyer or Stephanie Moulton.

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Berger, L.M., Brown, M., Collins, J.M. et al. Inequality in high-cost borrowing and unemployment insurance generosity in US states during the COVID-19 pandemic. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01922-8

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