RDP 2018-11: Consumer Credit Card Choice: Costs, Benefits and Behavioural Biases 8. Conclusion

Credit cards provide a convenience to consumers, acting as both a method of payment and a flexible credit instrument. We may expect then that most consumers would pay a modest net monetary cost to access this convenience. But the wide distribution of costs and benefits across cardholders instead suggests extensive variation in the credit card market, with some consumers paying relatively high net costs, and others receiving substantial net monetary benefits.

My estimates show that while around 40 per cent of credit card holders receive a net monetary benefit from their card, 30 per cent pay a net monetary cost, while the remaining cardholders break even. Consumers with lower levels of income and liquid wealth were more likely to incur net costs, partly, but not entirely, because they were more likely to use their card to borrow, and therefore incur interest charges. At the same time, higher-income respondents were more likely to hold rewards cards, and to receive substantial benefits from those rewards and from their cards' interest-free periods.

My analysis suggests that most consumers do not hold a credit card that is particularly well suited to their use patterns. Findings from the Consumer Payments Survey are consistent with consumers exhibiting certain behavioural biases when they first choose their credit card. In particular, I find suggestive evidence of optimism bias; that cardholders systematically underestimate their probability of incurring credit card interest charges. This would help to explain why the majority of consumers who regularly pay interest hold a card with a relatively high interest rate, which increases costs for these individuals.

In addition, I find some evidence that a small but substantial share of cardholders overestimate the net monetary value of their card, and believe they are making a gain when in fact they are likely making a loss. These cardholders are more likely to be motivated by rewards points, and less likely to have paid interest in the past year. This finding is consistent with the hypothesis that, while for respondents who regularly pay interest, the costs of their card may be highly salient, those who do not pay interest may experience bounded rationality; they may overweight benefits, such as rewards and the interest-free period, to form an inflated estimate of their card's value.

In contrast to evidence from the United States, I do not find that temporary credit card sign-up offers appeal to consumers' present bias; consumers who signed up to their card with a temporary offer were no worse off after the offer expired. If anything, these consumers were typically better off than other cardholders. This appears to reflect the mix of offers, with higher-income respondents – who are already more likely to make a net monetary benefit from their card – more likely to respond to, or to be targeted by, these offers in the Australian market.

Finally, I find suggestive evidence of cognitive barriers to switching cards. While I estimate that the vast majority of cardholders would be better off if they held a different card, less than 10 per cent had switched cards over the preceding year. Barriers to switching are likely to be both practical and cognitive, but I identify a group of around half of loss-making respondents who appear most likely to have been influenced by behavioural biases. The data do not allow me to identify the nature of these biases.

My findings represent a first estimate of the presence and impact of behavioural biases in the Australian credit card market. While the survey data do not allow me to rule out other potential explanations for the patterns we see, they provide suggestive evidence that behavioural biases do indeed influence consumer credit card choice in adverse ways. My findings also support earlier conjectures that these biases appear to explain an apparent lack of competition in credit card interest rates.

Future work, using more detailed survey or experimental data, would help to confirm my findings, and would enable a more precise estimate of the impact of these biases. For instance, surveys that collect more detailed information on personal circumstances, card choice and switching behaviour (such as asking respondents if they had experienced any adverse financial shocks since choosing their card, and asking for reasons that respondents do or do not choose to switch cards) would help to confirm (or otherwise) these patterns that I interpret as evidence of behavioural biases. Experimental evidence would allow a cleaner estimate of the effect of such biases.