Credit card debt is a serious issue that affects the lives of millions of Americans. While we all have an idea in our head of why people carry credit card debt, the reality is that we need more answers as to what prevents people from paying off credit card debt even when they have enough income to do so in the long run–and how we can help people make the decisions that will lead them to pay off their credit card debt.
That’s why I was so excited when I found the opportunity to work with the team at ReadyForZero. With ReadyForZero’s focus on creating better tools for paying off debt (designed specifically with the user experience in mind) and with their extensive anonymized data, they were a perfect partner for this research.
The principal question I wanted to address with my research was as follows: What prevents people from paying off credit card balances even when they have an income that would make it possible to pay off the balances over time? In addition to standard motives for borrowing, such as liquidity shortages or a lack of resources to reduce debt levels, economists have suspected that another factor may contribute to these substantial balances held by households: Some households may be “present biased.”
The idea of “present bias” means that people are overly impatient in the short-run and value immediate gratification even if not in their long-term interest. Bluntly speaking this means that many of us like to go out for dinner, buy a new pair of winter boots or spend a weekend skiing, even though in the long-term we would prefer to have saved the money for a more long-term goal, such as paying off credit card debt or saving for the down payment for an apartment. Moreover, we often tell ourselves that we can make up for our indulgences by not going out and saving the next week, weekend, or month. But really: How often does that happen?
The idea that such present bias may also explain why some consumers struggle to pay off their credit card debt despite earlier plans to do so is intriguing, but there has been very little evidence for it in social science research so far. Of course, as a researcher, I want actual evidence before I believe a theory; after all, there are many plausible theories, but only looking at real data can tell which ones help us understand best what’s going on in real life.
So that is what we did.
We looked at some anonymous data from the ReadyForZero database. It’s important to emphasize that at no point in this research was any identifying information attached to the data. That means we don’t know who the users in our sample are. We also made sure that we only looked at aggregated output results. This means, for instance, that we estimated and looked at whether users spend more in weeks they received a paycheck than in non-paycheck weeks. But this aggregated information does not allow us to look into how much or on what someone spent in any given week.
To test whether present bias could help us understand better why some users struggle to reduce their debt, we first classified all users in the sample along two dimensions: First, we estimated for each user how present biased he or she appeared. The idea is that individuals with a stronger present bias spend more on consumption goods–such as going out to dinner or to a bar or club–when they have just received their paycheck. A user who consistently spends more in payweeks than in non-payweeks is likely to be more present biased than another user whose spending depends less on his payday. After all, the best parties don’t always happen the weekend you get paid, so it is often a good idea to save your money for special events rather than blowing it immediately.
Second, we wanted to know whether a user was aware of his or her own tendency to repeatedly spend on consumption goods rather than save for a long-term goal. To get at this, we used the fact that users who are aware of this behavior adapt their spending depending on how much money they have left.
For instance, imagine you get a bonus payment with your paycheck so that you can finally pay off one of your credit cards. If you think you won’t overspend in the following weeks, you may use some of the extra money to go out and celebrate the bonus payment this weekend.
However, if you know about your own tendency to overspend, you may rethink that decision and save some of the money to make sure you’ll have enough to pay off that card even if you inevitably end up overspending a little in the next weeks. In this case, the extra money is an additional motivation to save a little extra instead of spending immediately today. We therefore divided users into two groups: those users who save a little extra and spend less when they have additional resources (who we thought were more aware of their own tendency to overspend) and those who didn’t adjust their behavior this way.
Once we had these two characteristics for each user, we checked how they did with reducing their debt relative to what they had planned to do.
In line with the theoretical predictions of present bias we found that users who exhibited a stronger present bias reduced their debt less than users with a less strong bias. Moreover, users who appeared to be aware of their own behavior managed to stick with their original plans much better than those who appeared to repeatedly tell themselves they would save more for debt paydown in the future.
The findings therefore support the notion that present bias plays a substantial role in explaining why some consumers have trouble sticking with their debt repayment plan. We also checked some other possible explanation, such as credit constraints, but found that each only explains part of the data, not the whole picture we observe.
First, our results have important policy implications for the regulation of credit markets. Common features in credit card contracts, such as teaser rates or the backloading of fees in subprime credit cards, disproportionately hurt consumers with present bias. Our results therefore provide additional evidence to convince policy makers that they should continue pursuing regulation like the Credit CARD Act of 2009 which, amongst other things, restricts the backloading of fees.
Finally, these findings have important implications for how we can help consumers avoid the debt trap. If we can help people understand how this bias toward their short-term desires keeps them in debt, and give them tools to fight their own present bias, we should be able to increase their likelihood of achieving financial stability.
Further innovation should therefore focus on new and creative ways to inform and persuade consumers of their short-term bias and help them combat it. One way of doing this, for example, are technologies which allow people to willingly “lock themselves” into their own debt payoff plan to avoid later temptation.