General Information
Project description
Regulators require lenders to display a subset of credit card features in summary tables before customers finalize a credit card choice. Some jurisdictions require some features to be displayed more prominently than others to help ensure that consumers are made aware of them. This approach could lead to untoward effects on choice, such that relevant but nonprominent product features do not factor in as significantly. To test this possibility, we instructed a random sample of 1615 adults to choose between two hypothetical credit cards whose features were shown side by side in tables. The sample was instructed to select the card that would result in the lowest financial charges, given a hypothetical scenario. Critically, we randomly varied whether the annual interest rates and fees were made visually salient by making one, both, or neither brighter than other features. The findings show that even among credit-savvy individuals, choice tends strongly toward the product that outperforms the other on a salient feature. As a result, we encourage regulators to consider not only whether a key feature should be made more salient, but also the guidelines regarding when a key feature should be displayed prominently during credit card acquisition.
Detailed information
Final report: Is there a final report presenting the results and conclusions of this project?
Final report
Analyses
The main analysis involves running logistic regressions with annual fee salience and annual interest rate salience as two-level predictor variables of choice of the low annual fee card, which is a binary outcome variable. We plan to exclude participants who respond incorrectly to a catch trial, largely because we are unable to determine whether their responses are due to inattention, poor comprehension, defiance, or some combination (see the subsection ‘Descriptives and exclusions’ for more details). We compare goodness of fit between regression models with and without these predictor variables using likelihood-ratio tests. We also evaluate whether adding the relative location of the card feature in the information boxes (i.e., the annual interest rate appears above or below annual fee) can improve the fit. We subsequently build logistic regressions hierarchically to explore whether financial literacy score can influence credit card choice share, independent of or depending on the visual salience manipulations. Finally, we use analyses of variance (ANOVAs) to evaluate effects of salience on response time, which may attest to the extent to which information was processed. All analyses are carried out using ‘R’ statistical software (R Core Team, 2015).
Sample Size. How many observations will be collected or what will determine sample size?
1615 participants aged 18 years and older, living in Canada or the USA, were recruited through Amazon's Mechanism Turk platform between May 29, 2020 and June 24, 2020
Who is behind the project?
Project status:
Completed
Methods
What is the project about?
Date published:
17 January 2022