CHAMPAIGN, Ill. — The practice of employers screening potential new hires by conducting pre-employment credit checks as a gauge of “character” or productivity is a controversial one that has drawn increased scrutiny in the years since the Great Recession. According to research from a University of Illinois expert in labor economics and workforce policy, credit screening in an employment situation is a flawed practice that can result in discrimination against low-income and minority applicants.
Although advocates insist that it improves the quality of job matches, a worker’s credit status doesn’t contain anything meaningful about the character-related components of employee productivity, says Andrew Weaver, a professor of labor and employment relations at Illinois.
“When the Great Recession hit, there were many news stories about employers screening potential new employees using their credit status as a condition of hiring,” Weaver said. “It turns out that there’s very little research out there that indicates whether that is a good practice.”
The paper, which was published in the journal ILR Review, analyzed data through 2010 from a nationally representative survey of individuals who were between 14-21 years old in 1979 to determine whether credit status contained information about a worker’s character that would be predictive of their productivity.
The results indicate that the portion of credit status that is related to an employee’s “time-invariant character” does not have a significant relationship to the employee’s productivity, according to the paper.
Although there are other potential rationales for the use of credit checks, the paper’s results call their widespread use into question, Weaver said.
“Is it really the case that someone who has more credit card debt is going be a worse forklift driver in a warehouse than someone else? These results indicate that in a lot of cases, credit checks may be used for situations where they don’t really have any validity,” he said.
An employer screening on credit status “isn’t picking up an actual job-relevant trait that’s germane to their productivity,” Weaver said. “An individual could have a terrible credit report and still be a very good worker.”
The practice would also have a disproportionately negative effect on low-income and minority workers.
“Since low-income and minority workers tend to have worse credit status, credit checks may end up discriminating against groups who have persistently worse credit than wealthier individuals,” he said. “So in addition to being unfair on an individual basis, the practice has some wider implications for discrimination.”
Credit problems can originate from many different sources, not just from, say, runaway credit card or gambling debts, Weaver noted.
“It could be that an employee gets laid off from their job, which causes a loss of income and, by extension, bad credit status,” he said. “They could have a costly medical problem – or a family member with a costly medical problem. It could also be the declining fortunes of the company or the industry that caused these things, not the irresponsibility of the individual.
“So the concern is that credit checks unfairly punish people who are trying to get back on their financial feet by applying for a job, resulting in rejections for something that's not job-relevant.”
The rise of similar screening practices that rely on correlations present in big data rather than a causal chain further increases the importance of the issue, Weaver said.
“One of the characteristics of big data analysis is that it relies heavily on correlations,” he said. “If we have enough data, we can make connections to just about any disparate thing. We could find that if you buy a red sweater, you’re more likely to default on your student loan. Therefore, lenders could charge people who wear red sweaters higher interest rates when they go to apply for a car loan.”
In some cases, it’s fine to make those types of correlations, Weaver said. In others, we should worry that there can be some discriminatory impact, he said.
“Just because something correlates with a negative outcome doesn’t mean much. Correlation does not equal causation,” Weaver said. “With an increasing number of employers running big data correlations between future employees and nonwork factors, we should be wary about screening based on factors that lack careful evidence of predictive validity.”