Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards
According to the Federal Reserve of the United States, in the second quarter of 2024, American credit card debt reached USD 1.14 trillion, the highest balance ever recorded. In an age of high-interest, complex credit cards, how does financial literacy affect credit card debt repayment? Also, how cou...
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MDPI AG
2025-02-01
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| Series: | International Journal of Financial Studies |
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| Online Access: | https://www.mdpi.com/2227-7072/13/1/22 |
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| author | Christos I. Giannikos Efstathia D. Korkou |
| author_facet | Christos I. Giannikos Efstathia D. Korkou |
| author_sort | Christos I. Giannikos |
| collection | DOAJ |
| description | According to the Federal Reserve of the United States, in the second quarter of 2024, American credit card debt reached USD 1.14 trillion, the highest balance ever recorded. In an age of high-interest, complex credit cards, how does financial literacy affect credit card debt repayment? Also, how could financial literacy and education stop the rise in credit card debt in America? To answer these questions, we use microdata from the latest wave of the Survey of Consumer Finances for 2022. We aim to capture the likelihood of credit card repayment behaviors related to the monthly balances owed by 3865 credit card holders. We consider three categories of self-reported credit card payoff behavior: hardly ever, sometimes, and always or almost always. Given the ordinal nature of our outcome variable, we perform a series of likelihood-ratio and Brant tests to assess the assumption of the proportionality of odds across response categories. Following the failure of the tests, we conclude with the selection of a generalized ordered logit/partial proportional odds model that allows us to relax the parallel lines constraint for those variables for which it is not justified. In our logistic regressions, we account for a comprehensive set of demographic characteristics, and from our results, we highlight the following: For credit card holders with low financial literacy, we find that the odds of moving to a higher category of payoff behavior are 21% and significantly lower than those of high financial literacy respondents. Further, for college-educated card holders, the odds of paying off always or almost always versus sometimes and hardly ever are 2.49 times and significantly greater than the odds for credit card holders without a college education. Credit card holders who are minority group members, female, under 45, have dependents, or earn less than USD 50,000 demonstrate a tendency for poor credit card payoff behavior. In our conclusion, we discuss how to improve credit card repayments. We stress the importance of monitoring people closely. We also aim to provide better financial advice to certain groups. Lastly, we present a more realistic approach to building and sustaining financial literacy. |
| format | Article |
| id | doaj-art-4697c79e31be4b0a94100e7012904c9c |
| institution | DOAJ |
| issn | 2227-7072 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Financial Studies |
| spelling | doaj-art-4697c79e31be4b0a94100e7012904c9c2025-08-20T02:42:34ZengMDPI AGInternational Journal of Financial Studies2227-70722025-02-011312210.3390/ijfs13010022Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit CardsChristos I. Giannikos0Efstathia D. Korkou1Bert Wasserman Department of Economics & Finance, Zicklin School of Business, Baruch College, The City University of New York, New York, NY 10010, USADepartment of Business and Economics, School of Business and Information Systems, York College, The City University of New York, 94-20 Guy R. Brewer Blvd, Jamaica, NY 11451, USAAccording to the Federal Reserve of the United States, in the second quarter of 2024, American credit card debt reached USD 1.14 trillion, the highest balance ever recorded. In an age of high-interest, complex credit cards, how does financial literacy affect credit card debt repayment? Also, how could financial literacy and education stop the rise in credit card debt in America? To answer these questions, we use microdata from the latest wave of the Survey of Consumer Finances for 2022. We aim to capture the likelihood of credit card repayment behaviors related to the monthly balances owed by 3865 credit card holders. We consider three categories of self-reported credit card payoff behavior: hardly ever, sometimes, and always or almost always. Given the ordinal nature of our outcome variable, we perform a series of likelihood-ratio and Brant tests to assess the assumption of the proportionality of odds across response categories. Following the failure of the tests, we conclude with the selection of a generalized ordered logit/partial proportional odds model that allows us to relax the parallel lines constraint for those variables for which it is not justified. In our logistic regressions, we account for a comprehensive set of demographic characteristics, and from our results, we highlight the following: For credit card holders with low financial literacy, we find that the odds of moving to a higher category of payoff behavior are 21% and significantly lower than those of high financial literacy respondents. Further, for college-educated card holders, the odds of paying off always or almost always versus sometimes and hardly ever are 2.49 times and significantly greater than the odds for credit card holders without a college education. Credit card holders who are minority group members, female, under 45, have dependents, or earn less than USD 50,000 demonstrate a tendency for poor credit card payoff behavior. In our conclusion, we discuss how to improve credit card repayments. We stress the importance of monitoring people closely. We also aim to provide better financial advice to certain groups. Lastly, we present a more realistic approach to building and sustaining financial literacy.https://www.mdpi.com/2227-7072/13/1/22financial literacyfinancial decision-makingcredit card repaymenthousehold financesordinal datageneralized ordered logit model |
| spellingShingle | Christos I. Giannikos Efstathia D. Korkou Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards International Journal of Financial Studies financial literacy financial decision-making credit card repayment household finances ordinal data generalized ordered logit model |
| title | Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards |
| title_full | Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards |
| title_fullStr | Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards |
| title_full_unstemmed | Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards |
| title_short | Financial Literacy and Credit Card Payoff Behaviors: Using Generalized Ordered Logit and Partial Proportional Odds Models to Measure American Credit Card Holders’ Likelihood of Repaying Their Credit Cards |
| title_sort | financial literacy and credit card payoff behaviors using generalized ordered logit and partial proportional odds models to measure american credit card holders likelihood of repaying their credit cards |
| topic | financial literacy financial decision-making credit card repayment household finances ordinal data generalized ordered logit model |
| url | https://www.mdpi.com/2227-7072/13/1/22 |
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