Three essays on perception bias in the financial knowledge of American adults



Journal Title

Journal ISSN

Volume Title



Perception bias in financial knowledge (the disconnect between the actual level of financial knowledge and the perception of it) is prevalent among American adults. This study utilizes surrogate residuals within a two-step regression procedure to first estimate the perception bias in the financial knowledge of American adults and then identifies the predictors of this perception bias by employing mixed-effects linear regression models with variable intercepts in the first chapter. This study also investigates how well perception biases in financial knowledge can predict individuals’ credit card behaviors by applying machine-learning-based binary classification techniques in the second chapter and deep neural networks in the third chapter. For the analyses, this study utilizes data from the 2018 National Financial Capability Study (NFCS), funded by the Financial Industry Regulatory Authority’s (FINRA) Investor Education Foundation. The results from the mixed-effects linear regression model suggest that Individuals who have perception biases in other domains of life (e.g., math ability and risk-taking attitude) tend to have perception biases in financial knowledge as well. Those who participate in financial-education programs are found to show a higher magnitude of perception bias in financial knowledge than those who do not participate in financial education programs. Furthermore, the results from the machine-learning-based binary classifications and deep neural networks indicate that the inclusion of perception bias in financial knowledge variables as inputs improves the prediction accuracy of all credit-card management behaviors.

Embargo status: Restricted until 09/2172. To request the author grant access, click on the PDF link to the left.



Perception-Bias, Financial-Knowledge, Surrogate-Residuals, Mixed-Effect-Linear-Modeling, Machine-Learning, Binary-Classification, Deep-Neural-Network