Recommended Reads

Update on student loan borrowers during payment suspension

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Updating a previous CFPB report, this blog documents increasing signs of financial strain through September 2022 among student loan borrowers who are expected to face monthly payments when federal forbearances end. It also tracks changes in other risk factors for which consumers are most likely to experience financial difficulties when the relief ends, depending on implementation of new debt cancellation proposals.

Thomas Conkling and Christa Gibbs, Office of Research Blog, Consumer Financial Protection Bureau

Let the rich be flooded: The distribution of financial aid and distress after hurricane harvey

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Comparing areas with and without flood insurance after Hurricane Harvey, this study finds evidence that mandatory flood insurance programs mitigated financial shocks where applicable. However, despite lender forbearances, federal disaster grants, and small business disaster loans, credit-constrained homeowners who lived in areas not subject to flood insurance mandates experienced a 20% increase in bankruptcies and a 13% increase in the share of debt in severe delinquency in flooded blocks relative to non-flooded areas.

Stephen B. Billings et al., Journal of Financial Economics

Wage Garnishment in the United States: New Facts from Administrative Payroll Records

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In the first comprehensive academic analysis of court-ordered wage garnishment, this study finds that garnishment rates nearly doubled from 2014 to 2019 due largely to increases in student loan collections. On average, 11 percent of gross earnings were remitted to creditors, raising concerns about whether unexpected garnishments may perpetuate a cycle of debt. Garnishments are most frequent in neighborhoods with higher percentages of Black residents and fewer college-educated workers, even controlling for income.

Anthony A. DeFusco, Brandon M. Enriquez, & Margaret B. Yellen

Accuracy of Explanations of Machine Learning Models for Credit Decisions

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This white paper creates a framework for using synthetic data sets to assess the accuracy of interpretability techniques as applied to machine learning models in finance. The authors controlled actual feature importance using a synthetic data set and then compared the outputs of two popular interpretability techniques to determine which was better at identifying relevant features, finding variation in results.

Andrés Alonso and José Manuel Carbó, Banco de España

Reducing the Black-White Homeownership Gap through Underwriting Innovations

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This study updates mortgage market developments in the use of cash-flow information from bank accounts and utility, telecommunications, and rental payments history. The report highlights issues concerning data collection, standardization, and consumer protection regulation when using non-traditional financial data sources, as well as the impact of pricing, servicing, and regulation in determining whether the use of such data sources enhances racial equity.

Jung Hyun Choi et al., Urban Institute

Credit Counseling and Long-Term Credit Outcomes: Evidence from the National Foundation for Credit Counseling’s Sharpen Your Financial Focus Program

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This study analyzes five-year credit outcomes for consumers who participate in credit counseling and in some cases enroll in debt management plans (DMPs) using data from a national initiative by the National Foundation for Credit Counseling.

Adrienne DiTommaso and Stephanie Moulton, The Ohio State University

Explaining Racial Disparities in Personal Bankruptcy Outcomes

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This paper examines the role of racial bias in contributing to disparities in consumer bankruptcy outcomes. Using a deep learning model trained on voter registration data to impute race in their analysis of bankruptcy filings between 1990 and 2022, the authors find that Black filers are more likely to have their cases dismissed without any debt relief under both Chapter 13 and Chapter 7 than White filers.

Bronson Argyle et al.

Credit Scores Since the COVID-19 Outbreak

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This paper analyzes the effect of forbearance programs and related credit reporting practices on consumers’ credit scores during early stages of the pandemic, using data from March through September 2020. Focusing mainly on mortgage forbearances, it finds evidence of a positive effect on consumers’ credit scores but concludes that broader improvements in credit card utilization rates particularly among consumers with low credit scores contributed more to general credit score improvements during the downturn.

Michal Kowalik, Lily Y. Liu, and Xiyu Wang; Federal Reserve Bank of Boston Supervisory Research and Analysis Working Papers 21-4

Lessons Learned from Mortgage Borrower Policies and Outcomes during the COVID-19 Pandemic

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This study analyzes the aggregate impacts of mortgage forbearances and the Federal Reserve’s large-scale asset purchase program through Q3 2021 with a particular eye toward effects on different racial and ethnic groups.

Kristopher Gerardi, Lauren Lambie-Hanson, and Paul Willen; Federal Reserve Bank of Boston Current Policy Perspectives

Designing Inherently Interpretable Machine Learning Models

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The authors argue that machine learning models in use cases that are highly sensitive and/or sectors that are highly regulated require inherent interpretability. The paper provides an approach for qualitatively assessing the interpretability of models based on feature effects and model architecture constraints.

Agus Sudjianto and Aijun Zhang; Cornell University

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