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What Factors Influence Credit Card Debt, Percent of Consumers with Personal Loans, and Bankruptcy Filings?

June 15, 2020

This post looks at what demographic factors impact credit card debt, percent of consumers with personal loans, and bankruptcy filings. This analysis occurs at the state level. I analyze the impact of credit scores, percent female, median age, median household income, number of people in each race category (e.g. Hispanic, White, Black, Asian, American Indian), educational attainment (e.g. percent high-school or higher, percent bachelor’s degree or higher, and percentage advanced degree), and marriage rate on credit card debt, percent of consumers with personal loans, and bankruptcy filings.

Turning first to credit card debt, I run a regression of credit card balance on the above mentioned variables and find several statistically significant factors. Specifically, a one point increase in credit scores leads to a $34.32 decrease in credit card balance. Furthermore, a one percent increase in percent female leads to a $29,043.45 decrease in outstanding credit card balance. When it comes to median income, a one dollar increase leads to a $0.06 increase in credit card balance. And, the impacts of numbers of Blacks and American Indians on credit card balance are statistically significant, but the magnitude of the impacts are effectively zero.

Figure 1, below, summarizes the results of the regression. The r-squared for this regression, which measures the proportion of the variance in credit card balance that is explained by the independent variables, is 0.75. This suggests that 75% of the variation in credit card balance is explained by the statistical model.

Figure 1.  Regression Results of Average Credit Card Debt on Demographic Variables
Independent VariablesCoefficientP-ValueSignficiant?
Average Fico Credit Score-34.320.005Yes
Percent Female-29,043.450.039Yes
Median Age24.230.500No
Median Household Income0.060.000Yes
Number of Hispanics0.000.538No
Number of Whites 0.000.152No
Number of Blacks0.000.048Yes
Number of Asians0.000.407No
Number of American Indians0.000.062Yes
Percent High School or Higher3,101.560.547No
Percent Bachelor’s Degree or Higher-3,530.940.596No
Percent Advanced Degree10,332.220.293No
Marriage Rate-6.710.749No

Turning next to percent of consumers with personal loans, Figure 2, below, shows that median age and median household income are both significant predictors, but the magnitude of the impact is effectively zero. The same goes for the number of Whites and the number of Blacks. What is noteworthy is that a one percent increase in the percent with a high school degree leads to an 88% increase in consumers with a personal loan. This impact is very large and is also statistically significant.

The r-squared for this regression, which measures the proportion of the variance in percent of consumers with personal loans that is explained by the independent variables, is 0.73. This suggests that 73% of the variation in percent of consumers with personal loans is explained by the statistical model.

Figure 2.  Regression Results of Percentage of Consumers with a Personal Loan on Demographic Variables
Independent VariablesCoefficientP-ValueSignficiant?
Average Fico Credit Score0.000.970No
Percent Female-0.700.597No
Median Age-0.010.043Yes
Median Household Income0.000.049Yes
Number of Hispanics0.000.174No
Number of Whites 0.000.001Yes
Number of Blacks0.000.076Yes
Number of Asians0.000.829No
Number of American Indians0.000.670No
Percent High School or Higher0.880.087Yes
Percent Bachelor’s Degree or Higher-0.670.308No
Percent Advanced Degree0.270.775No
Marriage Rate0.000.198No

Finally, turning to the regression results of bankruptcy filings on demographic variables, I find that the number of Whites and the number of Asians are statistically significant, but the magnitude of the effects are basically zero. Nevertheless, the r-squared for this regression, which measures the proportion of the variance in bankruptcy filings that is explained by the independent variables, is 0.85. This suggests that 85% of the variation in bankruptcy filings is explained by the statistical model, which is high.

Figure 3.  Regression Results of Bankruptcy Filings on Demographic Variables
Independent VariablesCoefficientP-ValueSignficiant?
Average Fico Credit Score-161.730.401No
Percent Female15,792.670.944No
Median Age-153.040.796No
Median Household Income0.030.885No
Number of Hispanics0.000.113No
Number of Whites 0.000.003Yes
Number of Blacks0.000.261No
Number of Asians0.010.018Yes
Number of American Indians-0.010.538No
Percent High School or Higher-16,627.770.845No
Percent Bachelor’s Degree or Higher45,698.330.678No
Percent Advanced Degree-97,323.310.547No
Marriage Rate-52.100.881No

This short analysis examines what demographic variables explain credit card debt, percent of consumers with personal loans, and bankruptcy filings. I find that credit scores, gender, race, and educational attainment are all significant predictors, albeit in different statistical models. An analysis of debt, loans, and bankruptcy should include these independent demographic variables.

Source: https://www.experian.com/blogs/ask-experian/consumer-credit-review/, https://www.abi.org/newsroom/bankruptcy-statistics, https://www.chamberofcommerce.org/credit-card-debt-by-state, https://www.experian.com/blogs/ask-experian/research/personal-loan-balance-amount-by-state/, https://www.cdc.gov/nchs/data/dvs/state-marriage-rates-90-95-99-18.pdf, https://en.m.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_median_age, https://www.governing.com/gov-data/census/state-minority-population-data-estimates.html, https://en.m.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_educational_attainment

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