Mobile Apps and Financial Decision Making
Bruce Carlin, Arna Olafsson, Michaela Pagel
Review of Finance, Volume 27, Issue 3, May 2023, Pages 977–996, https://doi.org/10.1093/rof/rfac040
Does the availability of new technology equip consumers to make better financial decisions? Ostensibly yes, if people have easier access to information, they should be able to avoid mistakes. However, measuring the economic impact of technology adoption is challenging. It is difficult to find settings, where it is possible to draw a causal link between the use of technology and people’s behavior and their outcomes.
In this paper, we exploit the release of a mobile application for a financial aggregation platform in Iceland to analyze how technology adoption changes consumer financial decision making. Before November 2014, access to the personal financial management software was possible only via an Internet browser. However, on November 14, 2014, a mobile application was released, which gave users easier and remote access to their bank account information. This caused a discontinuous jump in the propensity for users to log in to the platform.
To evaluate the economic impact of this improved access to information we use individual, transaction-level data from the aforementioned financial aggregation platform. The platform allows users to link all of their checking, savings, and credit card accounts, and view all spending, income transactions, and account balances in one place, providing us with a a detailed pictures of the financial lives of users of the platform as well as a good measure of how much attention they pay to their personal finances.
Our primary object of interest is the frequency of non-sufficient funds (NSF) fees. When a consumer attempts to make a purchase with a debit card and exceeds her overdraft limit, she incurs an NSF charge, but the purchase is denied. This represents an unambiguous mistake because the consumer suffers a cost with no benefit.
The release of the app was associated with a drop in NSF fees. Using 12-, 18-, and 24- month windows around the app release, we document a 14.1%, 26.8%, and 38.4% decrease in NSF fees per individual. These imply an average annual decline of $4.46 – $4.79 in NSF fees at 12-months following the app release. The results regarding overdraft interest and late fees are mixed. However, our analysis regarding these types of costs are limited by the fact that we did not have information about individual overdraft interest rates in the data, and interest rates were known to be changing during our sample period. As such, we cannot make unambiguous conclusions about these types of costs.
Our regression estimates compare average outcomes in the post-period to average out- comes in the pre-period. To investigate the dynamics, we can look at estimates for each month from the mobile app release. We plot the year-month coefficients for the various financial outcomes in Figure 1. By inspection, it is straightforward to see that the frequency and amount of NSF fees dropped following the app introduction. However, the change in overdraft usage and late fees is equivocal.
Because the incurrence of NSF fees does not bring any benefit for consumers, the decision to try to make a payment that results in a NSF charge is always dominated by the decision to avoid making the payment. Therefore, the drop in NSF fees represents a welfare benefit to consumers and implies that the new technology helped consumers avoid making mistakes.
Figure 1: Logins and Bank Fees Around the Release of the Mobile App
The top graph shows the propensity to use the aggregation platform for early adopters of the app (at least one mobile app login within the 12 months after its release) and inactive users of the platform (individuals that have all their accounts linked to the platform but never log in via the mobile app). The other graphs show the eff ect of the mobile app release on late fees, non-sufficient funds charges, number of non-sufficient funds charges, and overdraft interests incurred by users of the fi nancial aggregation platform. The e ffects are estimated with a regression where the outcomes are regressed on an indicator for the number of months from the mobile app release, individual fixed e ffects, and month fixed eff ects to control for seasonality. Each dot represents the estimated coefficient of the month-by-year dummy that corresponds to the number of months from the mobile app release that is shown on the x-axes. Error bars represent the 95% con fidence intervals. Standard errors are clustered at the individual level. All individuals included in the sample passed activity and completeness-of-records checks.