Boulder Summer Conference on Consumer Financial Decision Making: 4 Studies and What They Mean for Behavioral Design

May 22, 2018  |  By: Kristen Berman

We attended the Boulder Summer Conference on Consumer Financial Decision Making, and while all the presentations were great, we especially appreciated these four. A big thank you to John Lynch for hosting and bringing together these people.

1. Access to Credit and Labor Market Outcomes: Evidence from Credit Lotteries

By: Janis Skrastins, Armando Gomes, Bernadus Doornik

When people get access to mobility (a motorcycle) through a popular lottery based lending scheme in Brazil (Consorcio), their wages increase as does their formal employment. This was thought to be driven by people’s ability to travel farther to get to work and thus get to a better job. The design of the loan (a lottery) was such that people either got their motorcycle immediately or at some point throughout the loan period. This had the benefit of comparing time effects across borrowers. People who got the motorcycle sooner got the wage benefits faster.

Implication: Debt can increase wages. Transportation (especially in places with lower access to jobs) is one mechanism to do this.

2. Categorizing Spending as ‘Exceptional’ Decreases Consumer Motivation to Repay Debt

By: Keri Kettle, Remi Trudel, Simon Blanchard

This study finds that not all expenses are created equal. In a field study with Hello Wallet’s data, people are found to treat ordinary (common and frequent) expenses different that exceptional (uncommon and infrequent) expenses. Consumers make greater credit card payments when a greater proportion of of their spending is labeled as ordinary. The hypothesis is that consumer view this debt as less acceptable and thus are motivated to repay.

Implication: All debt is not all the same. We can increase repayment rates on credit cards by calling people’s attention to their ordinary (normal!) expenses that don’t fall into the category people typically assign ‘credit card’ expenses.

3. How do Consumers Respond when Retirement Nudges Push the Envelope?

By: Richard Mason, Shlomo Benartzi, John Beshears, Katherine Milkman

Employers who offer retirement savings are, on average, offering a 3% suggestion contribution. 20% of sponsors have pushed this up to 6%. Only 2.4% of plan sponsors have suggested rates higher than 6%. What happens when employers change the form and employees are shown a higher suggested retirement contribution? In a field study of over 10,000 people, users were randomly assigned to see a suggested contribution rate ranging from 6% to 11%. Relative to the 6% suggestion, higher rates increased savings by 20–50 basis points, as measured over the first 60 days. People had some increased likelihood of moving away from the suggestion when it was set between 7–10%, but only at the suggested rate of 11% did the likelihood of not participating go up.

Implication: We have a retirement crisis in America, but it may not be because we don’t want to save more. Small changes to the suggested rates can increase contributions without turning people off of saving completely. Note the paper did not mention any effects of retirement leakage. We believe this will be measured over time.

4. Leveraging Temporal Asymmetry to Improve Consumer Financial Predictions

By: Chuck Howard, David Hardisty, Abigail Sussman, Melissa Knoll

How good are we at predicting future expenses? Not good. We are good at under-predicting our expenses. In a clever field study, participants were asked to record their expenses from the last week and then make a prediction the future week’s expenses. In every period people consistently underestimated their future expenses, despite learning that they were wrong the previous week. Why is this? The research concluded that the more typical someone thinks their expenses are, the more likely they are to underestimate their future expenses. People forget to account for all of the atypical expenses in their prediction. However, the bias can be eliminated by decreasing the perceived typicalness of their expenses through asking people to think about past and future atypical expenses. This intervention consistently increased prediction accuracy.

Implication: It’s not enough to call someone’s attention to their expenses. In order to make accurate predictions for future budgets, we must help people change how they think about their expenses.

by Kristen Berman

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