Local Conditions and Debt in Collections

• Published date: 01 December 2019
• Author: Breno Braga,Signe‐Mary Mckernan,Caroline Ratcliffe,Brett Theodos,John Chalekian,Christopher Trepel
• Date: 01 December 2019
• DOI: http://doi.org/10.1111/joca.12283

2058 THE JOURNAL OF CONSUMER AFFAIRS

BRENO BRAGA, SIGNE-MARY MCKERNAN,

CAROLINE RATCLIFFE, BRETT THEODOS, JOHN CHALEKIAN,

AND CHRISTOPHER TREPEL

Local Conditions and Debt in Collections

Using credit reporting rm records on 7 million individuals in the

United States, we rst demonstrate that debt in collections varies signif-

icantly across the country; specically,the South and West regions have

higher shares of individuals with debt in collections than other regions.

Second, we identify local factors that are strongly related to debt in col-

lections. Results from our regression models show that neighborhoods

with higher rates of debt in collections are more likely to have(1) lower

health insurance coverage; (2) lower home values and homeownership

rates, (3) a higher share of delinquent and underwater mortgages, (4)

lower household incomes, and (5) a higher share of African Americans

and Latinos. While our analysis does not identify the causal mechanisms

that determine nancial distress, the analyses developed here can facil-

itate research on such mechanisms.

Delinquent debt in the United States is an important concern for both

researchers and policymakers, yet little is known about which localities

in the country are most affected by nancial distress, and which local

Breno Braga (bbraga@urban.org)is a Senior Research Associate at the Urban Institute. Signe-Mary

McKernan (smckernan@urban.org) is a Vice President at the Urban Institute. Caroline Ratcliffe

(caroline.ratcliffe@cfpb.gov) is a Senior Economist at the Consumer Financial Protection Bureau.

Brett Theodos (btheodos@urban.org) is a Senior Fellow at the Urban Institute. John Chalekian

(john.chalekian@mcmcg.com) is a Co-Founder and Chief Analytics Ofcer at Jump Start Finance.

Christopher Trepel (christopher.trepel@lowellgroup.co.uk) is Group Chief Science Ofcer at Low-

ell Group and an Operating Partner at Fenway Summer Ventures. Support for research on con-

sumer indebtedness was provided by the Consumer Credit Research Institute (CCRI; Encore Capital

Group). This article is the result of Caroline Ratcliffe’s independent research while at the Urban

Institute and does not necessarily represent the views of the Consumer Financial Protection Bureau

or the United States. Additionally, the views expressed are those of the authors and should not

be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research

ndings or the insights and recommendations of Urban experts. Further information on the Urban

Institute’s funding principles is available at www.urban.org/support. We are grateful to participants

at the Association for Public Policy Analysis and Management Fall 2014 Research Conference,

the Society of Government Economists seminar, National Collections and Credit Risk 2015 Con-

ference, Consumer Credit in America Symposium, and Urban Institute seminar participants for

helpful comments. We thank Bing Bai, Bowen Garrett, and Kyle Caswell for helpful suggestions,

Emma Kalish for excellent research assistance, and Peifang Guo for initial data preparation and

review.

The Journal of Consumer Affairs, Winter 2019: 2058–2085

DOI: 10.1111/joca.12283

Copyright 2019 by The American Council on Consumer Interests

WINTER 2019 VOLUME 53, NUMBER 4 2059

characteristics are most associated with it.1This paper begins to bridge this

gap by using unique credit reporting rm data to examine the geographic

distribution of debt in collections in the United States and, merged with

local-level data, identify the characteristics of neighborhoods (census

tracts) with high debt in collections. In doing so, we answer the following

research questions:

1. How is debt in collections geographically distributed across the

United States, and which areas have the highest concentration of

debt in collections?

2. Which neighborhood characteristics are associated with debt in

collections? How do local-area health insurance coverage, housing

market conditions, labor market conditions, and socioeconomic

characteristics relate to local nancial distress, as measured by debt

in collections?

These questions are important because debt in collections can decrease

nancial opportunities. Credit report information, including debt in col-

lections, can be used to determine eligibility for jobs, access to rental

housing and mortgages, and insurance premiums (FTC 2013; Traub 2013).

Debt in collections can lower credit scores, thereby affecting future

access to credit and the price of credit, and reducing debt in collec-

tions and its associated nancial stress can improve consumer health

and well-being (APA 2015; Arber, Fenn, and Meadows 2014; Berger,

Collins, and Cuesta 2015). Further, research has underscored the impor-

tance of debt recovery for expanding access to credit (Bae and Goyal

2009; Berkowitz and White 2004; Fedasyeu 2014; Jappelli, Pagano, and

Bianco 2005) and reducing the price of credit (Bae and Goyal 2009;

Berkowitz and White 2004).

Our ndings highlight how debt in collection is geographically con-

centrated; specically, consumers in southern and western states are more

likely to have debt in collections. And the average amount of debt in col-

lections is high in states that were most affected by the 2010 foreclosure

crisis (i.e., Nevada, Arizona, and Florida).

We nd that neighborhoods with higher rates of debt in collections have

lower health insurance coverage, lowerhousing values and homeownership

rates, more delinquent and underwater mortgages, and lower household

incomes. Debt in collections is also more prevalent in areas with more

African Americans and Latinos.

1. See for example Besharat, Carrillat, and Ladik (2014); CFPB (2014); Dobbie and Song (2015);

McCarthy (1997); and Zinman (2014).