Marred By Bad Credit: The Social Distribution and Consequences of Subpar Credit Records

Friday, June 24, 2016: 10:45 AM-12:15 PM
83 Dwinelle (Dwinelle Hall)
Barbara Kiviat, Harvard University, Cambridge, MA
Rourke O'Brien, University of Wisconsin La Folette School of Public Affairs, Madison, WI
Marred by Bad Credit: The Social Distribution and Consequences of Subpar Credit Records

Barbara Kiviat, Harvard University

Rourke O’Brien, University of Wisconsin-Madison

Background and Motivation

            Having a black mark on one’s credit record is an increasingly significant problem for people in the U.S. and beyond. As the welfare state shrinks, people more frequently borrow to maintain standard of living—the so-called plastic safety net (Rajan 2010; Krippner 2011; Prasad 2012)—and to access mechanisms of social mobility, such student loans for attending college (Dwyer, McCloud, and Hodson 2012). Decision-makers also now consider credit history when allocating a broad range of other sorts of resources, including jobs, rental housing, and insurance policies (Thorne 2007; Fourcade and Healy 2013; Rona-Tas 2014). Credit records have never before been so important in determining who gets what.

            This growing importance of credit history leads us to ask two questions. First, how does having a bad credit record map onto other forms of economic standing such as income, wealth, family structure, education, and race? Second, what inferences do people make about individuals with bad credit reports, and how do those inferences change how people allocate resources?

            Existing literature on the first question—how credit problems align with other forms of advantage and disadvantage—is surprisingly sparse. In the U.S., credit history is proprietary information controlled by three large corporations, Equifax, Experian and TransUnion, which constrains even basic descriptive analysis. Researchers have found that about a fifth of credit reports show a serious delinquency (Avery et al. 2009), about a third include a debt in collections (Ratcliffe et al. 2014), and about an eighth have a public records item such as foreclosure or bankruptcy (Avery et al. 2004). Yet aligning these black marks with data on household-level income, wealth, family structure and other variables has largely remained elusive.

            Scholarship from economic sociology and anthropology sheds light on the second question, how people make inferences about individuals with debt. Timely loan repayment has long been seen as a sign of moral character (Carruthers 2009; Graeber 2012), although the type of debt a person carries can bring its own moral weight (Tach and Greene 2014; Polletta and Tufail 2014)—hence popular references to “good” debt like home mortgages and “bad” debt like credit card balances. What is generally missing from this literature, however, is quantitative specificity about both moral judgments and how those judgments translate into outcomes, such as differential access to resources.

Data and Methods

            This paper leverages two sorts of data to shed light on the questions of who has bad credit and how it affects access to resources. To gain purchase on the distribution of bad credit in the U.S. population, we use the Federal Reserve Board’s nationally representative Survey of Consumer Finances to align self-reported indications of bad credit—such as not applying for a loan because of credit report problems—with individual- and household-level measures of income, wealth, family structure, education, race and other categories of sociological interest.

            We then use an original vignette experiment with 1,625 respondents to gauge how character judgment and resource allocation changes according to the sort of unpaid debt an individual carries (medical vs. credit card vs. none) and how an individual explains having missed debt payments. In this experiment we measure a range of outcome variables, such as willingness to recommend a person for a job, rent the person an apartment, and lend him or her money, which allows us to look at differences across types of resources as well as types of debt. Our sample is nationally representative in terms of age, sex, income, race/ethnicity, and education, so we are also able to make claims about how responses systematically vary across those categories

Preliminary Results

            We have just recently begun to analyze our data, so all findings are tentative.

            From the Survey of Consumer Finances, we find that while asset wealth maps somewhat neatly onto missed loan payments and self-reported bad credit, with the rich showing lower incidences of both, income requires a more nuanced story. In line with what researchers using Census-tract-level data have found (Ratcliffe et al. 2014), we find a negative but small correlation between income and bad credit. Yet unlike other work, our data lets us consider annual income in a typical year (to capture economic class rather than short-term shock) and to look at income at the household level. Households in the second and third quintiles of the income distribution are hardest hit, with nearly 20% of households reporting some sort of credit problem. Bad credit is an economic black mark that most frequently goes with being middle class.

            From the Survey of Consumer Finances, we also find that credit problems are more common in households with children, irrespective of whether one or two parents are present. Additionally, we find that in terms of education, people with some college coursework but no degree have the highest rate of credit problems, although, surprisingly, people with associate’s degrees fare worse than those with only a high school degree. These are among the findings we will explore further.

            From the vignette experiment we find a strong ordering of unpaid debts, with respondents showing the least favorable assessments of individuals with unpaid credit card debt, then unpaid medical debt, and then no unpaid debt. These assessments—along dimensions such as trustworthiness, honesty, and morality—directly translate into willingness to engage in economic relationships, such as starting a business together, making a job recommendation, or renting an apartment. Our results consistently show statistically significant differences.

            This moral ordering does not, however, extend to non-economic relationships. When we ask respondents how likely they would be to let the individual described in the vignette watch their children or how likely they would be to recommend that he/she date a friend, our results show that having unpaid medical or credit card debt makes no statistically significant difference.

            From these findings, and the results of the first part of our study, we theorize the role that a marred credit history plays in perpetuating economic disadvantage.