Document Type
Article
Publication Date
1-23-2016
First Page
1
Last Page
42
Additional Publication URL
http://ssrn.com/abstract=2419035
Abstract
This paper evaluates the possible benefits and drawbacks of the formal formula learning of compound growth as it pertains to eliminating, or at least reducing, the exponential growth bias in various household savings and debt decisions. In our main experimental study, we determine if the ability to calculate the simple compound savings formula only assists in its direct area of application with an available calculator, or if this knowledge extends into similar exponentially-based savings and debt decisions when either a calculator is prohibited or when the formula is unknown. In the process of tackling this research question, we develop a measure for the exponential growth bias that naturally extends over different tasks and parameter settings. Our findings suggest that learning the compound savings formula does much more than eliminate the exponential growth bias for individuals in the savings domain with an available calculator. In fact, we find evidence that these individuals post less biased savings and debt estimates in the absence of a calculator, suggesting that the knowledge of this formula may aid in developing a more general, intuitive grasp of exponential effects. On the other hand, we find that too much dependence on these formulas can have adverse effects, as a number of participants who knew the compound savings formula mistakenly applied a variation of it in the debt domain leading to insensible answers well above the initial loan balance.
Rights
This is an electronic version of a working paper. Archived with permission.The author(s) reserves all rights.
Recommended Citation
Foltice, Bryan and Langer, Thomas, "In Equations We Trust? Formula Learning Effects on the Exponential Growth Bias" (2016). Scholarship and Professional Work - Business. 262.
https://digitalcommons.butler.edu/cob_papers/262
Included in
Finance and Financial Management Commons, Management Sciences and Quantitative Methods Commons