2018
DOI: 10.3386/w24881
|View full text |Cite
|
Sign up to set email alerts
|

Sticking to Your Plan: The Role of Present Bias for Credit Card Paydown

Abstract: We are grateful to the team at ReadyForZero for providing the data and making this research possible, especially to Rod Ebrahimi and Ignacio Thayer. Financial support to Theresa Kuchler from the B.F. Haley and E.S. Shaw Fellowship through SIEPR is gratefully acknowledged. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

2
18
1

Year Published

2018
2018
2023
2023

Publication Types

Select...
6
3
1

Relationship

1
9

Authors

Journals

citations
Cited by 38 publications
(25 citation statements)
references
References 40 publications
(47 reference statements)
2
18
1
Order By: Relevance
“…For the subsample of credit card debt holders, our results complement the studies of Tan et al (), Telyukova (), and Kuchler (), which focus on individuals with credit card balances. We find that, even in this subsample, most of the time, households are aware of their credit card debt and its costs.…”
supporting
confidence: 81%
“…For the subsample of credit card debt holders, our results complement the studies of Tan et al (), Telyukova (), and Kuchler (), which focus on individuals with credit card balances. We find that, even in this subsample, most of the time, households are aware of their credit card debt and its costs.…”
supporting
confidence: 81%
“…We follow Gelman et al (2014), Baker (2013), Kuchler (2015), and Kueng (2015) in using data from a financial aggregation and service application (app), which overcomes the accuracy, scope, and frequency limitations of the existing data sources of consumption and income. Gelman et al (2014) were the first to advance the measurement of income and spending with this high-frequency app data, which is derived from the actual transactions and account balances of individuals.…”
Section: Literature Review and Theoretical Backgroundmentioning
confidence: 99%
“…This tension has been explained with buffer stock models augmented with an additional assumption: either discounting with present bias 10 ( Laibson et al, 2003Laibson et al, , 2017 or illiquid assets with very high rates of return and credit cards with counterfactually low interest rates . Present bias has also been used to explain willingness to hold high-interest debt (Ausubel, 1991), suboptimal debt-repayment trajectories (Kuchler and Pagel, 2017), and heterogeneity in debt levels. Individuals who exhibit present bias in laboratory tasks are 15 percentage points more likely to have credit card debt, and conditional on borrowing, have about 25 percent more debt (Meier and Sprenger, 2010).…”
Section: Credit Cardsmentioning
confidence: 99%