CC BY
73
https ://doi.org/10.29013/EJEMS-19-4-66-71
Yichi Zhang, 500 Christ School Road, Arden, North Carolina, United States E-mail: [email protected]
FINANCIAL WORRIES OVER CREDIT CARD PAYMENTS AMONG ADULTS IN 2017
Abstract
Aim: This study aims to:
1. examine the predictors of adults' financial worries over Credit Card Payments in 2017;
2. build a predictive model for adults' financial worries over Credit Card Payments using a logistic regression model.
Method: The National Health Interview Survey (NHIS) in 2017 was used for data analysis. All the participants who were eligible were randomly assigned into 2 groups: training sample and testing sample. A logistic regression model was built using the training sample data. Receiver operating characteristic (ROC) was calculated.
Results: A total of 2789 (14.30%) participants out of 19508 had worried about the credit card payments. About 15.95% of female participants and 12.30% male participants had worried.
According to the logistic regression, the younger population were less likely to worry about credit card payment than the elderly population. Male is 27.0% less likely to worried about housing cost. The non-Hispanic population was 64.1% less likely to worry. Compared to the unmarried, married people were 29.2% less likely to worry. Compared to other races while the black population was 71.0% more likely to worry. Compared to the people in the northeast region, people in the Midwest were 24.1% less likely to worry. Compared to people who were looking for a job, the employed and the one not looking for a job were 45.0%, 43.4% less likely to worry about housing cost, respectively.
The area under the curve was 0.6407. The optional cutoff time is 0.62. The misclassification error was 0.144. the sensitivity rate is about 0.07% and the specificity is 100%.
Conclusions: In this study, we identified several important predictors for financial worries over credit card payments in 2017 e.g., age, gender, regions, working status. The findings can help identify people at higher risk of having the financial worries over credit card payments.
Keywords:
Introduction:
In modern society, credit card has become one of the most pervasive ways to pay. A credit card works as a card that issued to the cardholder in order to pay for his or her goods based on the promise that he
or she pays back the money to the card provider. In this way, the cardholders can purchase the items they can not afford now but will have to pay the money back later. Such convenience, more and more people choose to use the credit card. Bankruptcy Protection
Act of 2005 made people harder to file bankruptcy, so more people turned to use a credit card to pay their bills. Herman stated, accordng to the American Banking Association, the population of using credit cards has grown to a staggering number: there were 364 million open credit card accounts in the United States as of the end of 2017 (Herman [5]). According to the Boston Federal Reserve, 75.7 percent of consumers had at least one credit card. The staggering number will continue to increase in the future (Jamie Gonzalez-Garcia [1]).
However, the credit cards also brought concerns: the amount owed by the consumers is rising. Consumer debt is what the cardholders owe to the bank. With higher debt, people start to concern about the devastating consequences the credit card will bring: losing their job and ruining their credit. Now, Americans are drowning in debt. Once the economic recession come, people with high debt will be sunk.
"Consumer debt in total hit a little more than $4 trillion - the largest amount ever - as of December 2018." according to the latest data from the Federal Reserve. That includes auto loans, student loans, personal loans, credit cards but not mortgages (Samuel Stebbins [3]).
Stebbins noted, according to the Federal Reserve, "Americans owe a record $1.04 trillion in credit card debt - up from less than $854 billion five years ago." According to Ted Rossman, "an industry analyst at research group creditcards.com, about 40 percent of Americans have enough income to pay off their balance - and do so in full every month." Leonhardt has argued. For them, a high credit card balance is not a problem. For the remaining 60 percent, however, maintaining a high credit card balance can mean hundreds of dollars in interest payments a year and possibly a low credit score (Samuel Stebbins [3]). With these concerns, people's financial worries have become unprecedentedly high. To delve into these concerns, I did this study.
This study is to examine the financial worries of people. It aims to examine the predictors of adults'
financial worries over credit card Payments in 2017 and build a predictive model for adults' financial worries over credit card Payments using a logistic regression model.
2. Data and Methods:
Data:
The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC).
The National Health Interview Survey (NHIS) Data 2017 was used in this study.
https://www.cdc.gov/nchs/nhis/about_nhis. htm
Optimal Cutoff for Binary Classification maximizes the accuracy.
Mis-Classification Error is the proportion of all events that were incorrectly classified, for a given probability cutoff score.
Sensitivity: the probability that a test result will be positive when the disease is present (true positive rate.
Specificity: the probability that a test result will be negative when the disease is not present (true negative rate, expressed as a percentage). e expressed as a percentage).
Models:
We used logistic regression models to calculate the predicted risk. Logistic regression is a part of a category of statistical models called generalized linear models, and it allows one to predict a discrete outcome from a set of variables that may be continuous, discrete, dichotomous, or a combination of these. Typically, the dependent variable is dichoto-mous and the independent variables are either categorical or continuous.
The logistic regression model can be expressed with the formula:
ln(P/P-1) = j60 + ^1*X1 + p2*X2 + ... .+ pn*Xn
Variables:
The outcome variable is the percentage of How worried are you about ...credit card payments (ASICCMP).
Table 1.- Variables used in this study
SEX 1: male 2: female
ORIGIN_I Hispanic Ethnicity: 1: yes; 2: no
RACRECI3 1: White 2: Black 3: Asian 4: All other race groups*
AGE_P Age <18 years old 0-17
1 Northeast
Region 2 Midwest
3 South
4 West
3. Results
A total of 2789 (14.30%) participants out of 19508 had worried about the credit card payments. About 15.95% of female participants and 12.30% male participants had worried.
Figure 1. matrix of correlations between variables Table 2.- Logistic Regression for Credit Card Payments
Estimate Std. Error z value Pr(>|z )
1 2 3 4 5 6
(Intercept) 0.330 0.187 1.765 0.078 .
AGE P -0.440 0.127 -3.474 0.001 ***
Male -0.314 0.061 -5.148 0.000 ***
HISPAN NO -1.024 0.082 -12.523 < 2e-16 ***
MARRIED -0.346 0.061 -5.686 0.000 ***
White -0.135 0.115 -1.174 0.240
Black 0.536 0.138 3.884 0.000 ***
Midwest -0.276 0.095 -2.897 0.004 **
South -0.115 0.085 -1.348 0.178
1 2 3 4 5 6
West -0.162 0.092 -1.751 0.080
Working -0.597 0.116 -5.149 0.000 ***
NWorNLor -0.569 0.125 -4.566 0.000 ***
According to the logistic regression, the younger population were less likely to worry about credit card payment than the elderly population. Male is 27.0% less likely to worried about housing cost. The non-Hispanic population was 64.1% less likely to worry. Compared to the unmarried, married people were 29.2% less likely to worry. Compared to other race
while the black population was 71.0% more likely to worry. Compared to the people in the northeast region, people in the Midwest were 24.1% less likely to worry. Compared to people who were looking for a job, the employed and the one not looking for a job were 45.0%, 43.4% less likely to worry about housing cost, respectively.
0,000 0,500 1,000
I Risk increase BOR
Figure 2. Odds Ratio (blue) and Risk Increase (red) According to Logistic Regression
Figure 3. ROC in the testing sample for Logistic Regression
Table 3.- Odds Ratio According to Logistic Regression
OR Risk increase
AGE P 0.644 -35.6%
Male 0.730 -27.0%
HISPAN NO 0.359 -64.1%
MARRIED 0.708 -29.2%
White 0.874 -12.6%
Black 1.710 71.0%
Midwest 0.759 -24.1%
South 0.892 -10.8%
West 0.851 -14.9%
Working 0.550 -45.0%
NWorNLor 0.566 -43.4%
The area under the curve was 0.6407. The optional cutoff time is 0.62. The misclassification error was 0.144. the sensitivity rate is about 0.07% and the specificity is 100%.
4. Discussion
A total of 2789 (14.30%) participants out of 19508 had worried about the credit card payments. About 15.95% of female participants and 12.30% male participants had worried.
According to the logistic regression, the younger population were less likely to worry about credit card payment than the elderly population. Male is 27.0% less likely to worried about housing cost. The non-Hispanic population was 64.1% less likely to worry. Compared to the unmarried, married people were 29.2% less likely to worry. Compared to other race while the black population was 71.0% more likely to worry. Compared to the people in the northeast region, people in the Midwest were 24.1% less likely to worry. Compared to people who were looking for
a job, the employed and the one not looking for a job were 45.0%, 43.4% less likely to worry about housing cost, respectively.
It shows, that about one person among ten is worried about credit card payments; the proportion is high. Female tends to be more worried about the housing-cost, and married people are less likely to worry. The students in college are likely to worry about it, because they don't yet have a stable job, and they are not married. It showed that the black population and Hispanic population will be more likely to worry, indicating that there are still racial inequalities when finding jobs. With these problems, people who have high debt will be sunk when the wave ofeconomic recession crushes the United States. Overall, there are still plentiful problems left for Americans to solve, such as job opportunities and cheaper housing cost for students just graduated. This study warned us that it is time for us to pay more attention to the credit card problem.
There are still some limitations that are left to fix in this study. For example, we did not include the income and other debts and mortgage in this study when examing the factors of the financial worries of the credit card payments. These will make the results of the experiment deviate from the true results.
In this study, we identified several important predictors for financial worries over credit card payments in 2017 e.g., age, gender, regions and working status. As the data of the financial worries ofAmeri-cans demonstrates, problems like job opportunities, ethnic groups, and the ability to pay the housing cost make people worried. The prosper of the country will not be able to sustain without making people less worried about credit card payment.
References:
1. Gonzalez-Garcia J. (2019, July 15). Credit Card Ownership Statistics. Retrieved October 19, 2019. From URL:https://www.creditcards.com/credit-card-news/ownership-statistics.php
2. Stebbins S. (2019, April 26). Where credit card debt is the worst in the US: States with the highest average balances. Retrieved from URL:https://www.usatoday.com/story/money/personalfinance/2019/03/07/ credit-card-debt-where-average-balance-highest-across-us/39129001
3. NHIS - About the National Health Interview Survey. (2019, January 16). Retrieved August 28, 2019. From URL:https://www.cdc.gov/nchs/nhis/about_nhis.htm
4. Herman J. (2019, September 10). Average U. S. Credit Card Debt Statistics 2019. Retrieved August 28, 2019. From URL:https://www.creditcards.com/credit-card-news/credit-card-debt-statistics-1276.php
5. Leonhardt M. (2019, September 19). Americans have $29.000 in debt-and many say they'll be paying it off forever. Retrieved August 29, 2019. From URL:https://www.cnbc.com/2019/09/19/americans-dont-know-when-theyll-pay-off-their-debt.html
