8. Model
8.6 Unemployment and Credit Card Debt After COVID-19 Outbreak
It is crucial to focus on actions that can be undertaken to ensure that this market is functioning smoothly and to avoid any potential market failures. One measure that can be implemented is the investment in the financial literacy of the population. Banks can be ordered to keep consumers updated about the health of their credit card accounts via issuing regular credit card balances statements. In case a consumer starts to be lagging behind regular payments, banks can create incentives for them to start repaying the debt by informing them about potential accumulating losses. On a more general level, supplementary educational courses can be established to introduce consumers to the credit card market and explain which payment strategies are the best to avoid accumulating debt. These programs can be promoted in colleges and universities to target young families, who greatly rely on credit cards. Additionally, more rigorous policies to issuing credit cards can be applied to the banks to avoid the spread of the credit card industry to the vulnerable part of the population: consumers can be asked to provide banks with more detailed income statements, thus proving their reliability as debtors.
As per April 3, 2020, in the US “A staggering 6,6 million people applied for unemployment benefits”, NY Times reports. By May 2, 2020, this figure stood at appalling 30 million people (Reinicke 2020, p. 1). The Wall Street Journal is reporting that credit card issuers are already preparing for the great loan losses. Their research concluded that credit cards will be “less profitable and riskier for the next two years or so” (Andriotis and McCaffrey 2020, p. 1).
American consumers were allowed to skip April payments, whereas tightening of spending and unwillingness to pay outstanding debts alongside with deterioration of
Source: Federal Reserve Bank of St. Louis, Federal Reserve Bank of New York, 2020, own calculations Figure 12 – Credit Card Debt and Unemployment Rate in the US: Actual versus Predicted
capital of credit card issuers can lead to a burst of credit card debt bubble as a result of coronavirus pandemic, CBS News report concludes (Schlesinger 2020).
Figure 12 compares the quarterly data for the total outstanding credit card debt (right axis) and unemployment rate (left axis) since 2003. The upper chart shows the historical values up until the fourth quarter of 2019 with the forecast with 95% percent significance intervals for 2020. As we can see, the unemployment rate raised drastically after the last recession and kept going down after peaking in 2010. It has fallen below a 4% rate by 2018, which is a historical low for this time frame. The total credit card debt in the US increased in the period from 2006 to 2009, then plunged moderately until 2013 and kept rising afterward.
Based on the historical data, the unemployment rate should have kept falling down throughout 2020, eventually approaching the benchmark of 3 percent. The total credit card debt should have kept increasing moderately as it did in the last years.
However, the most recent statistics for the unemployment rate for the beginning of 2020 already reflect the dramatic impact of the COVID-19 outbreak. As per the beginning of April 2020 the unemployment rate is peaking at a 4,4 percent rate, which already surpassed the upper 95% confidence interval based on the historical data. We can anticipate the response of households to this shock by altering the level of debt.
Eventually, we cannot derive a significant conclusion due to the novelty of the data. The true magnitude of change will unfold with time. However, according to James X. Sullivan (2008) households in the second and third deciles based on assets distribution increase their borrowings as a response to income shortfalls, whereas the poorest households and the wealthier ones do not increase their borrowings. The current situation imposes a serious threat to the credit card market. We can only speculate about the steps we could have undertaken to mitigate the negative effects of the crisis, but understanding the key factors driving the credit card debt would definitely be incredibly valuable knowledge in hands of policymakers when it comes to the adjustments of the credit card market, which were the main topic of this thesis.
Conclusion
The aim of this thesis was to identify the key determinants of the credit card debt for the US families. The framework of the analysis was inspired by the work of Chien and Devaney (2001) and substantially improved based on the most recent data available and novel financial literacy variables. The Survey of Consumer Finances was employed as the main data source for the analysis. Two-step procedure which consists of stepwise regression and Tobit model empowered this analysis with accurate estimates of main factors driving the debt and directions of impact. We hypothesized that demographic (age, years of education, ethnicity, occupation, and marriage), economic (income and property possession status) and attitudinal (financial literacy and attitude towards credit) characteristics jointly affect the level of the credit card debt for US households.
Based on the model results, we reject the null hypothesis that demographic, economic, and attitudinal factors jointly do not affect the level of credit card debt in the US. Age, marital status, income, real estate ownership status, favorable and neutral attitude towards credit, and high self-reported financial knowledge were selected as the statistically significant determinants of credit card debt level.
The credit card debt is decreasing with age and increasing for married families. The relationship of age and credit card debt is consistent with the life-cycle hypothesis (Modigliani 1986), as well as numerous empirical findings in this area (Mandell (1973);
Lown and Ju (1992); Bertaut and Haliassos (2004)). The effect of age and marital status is also consistent with findings of Bird et. al (1997) and Chien and Devaney (2001).
The real estate possession is associated with the boost in credit card debt, whereas increase of income is negatively affecting the level of credit card debt. These findings align with ones of Chien and Devaney (2001). The relationship between credit card debt and income was also depicted by Bird et. al (1997), Mandell (1973), and Bertaut and Haliassos (2004). Nevertheless, Kim and Devaney (2001) concluded that increase in income stimulates the credit card debt, thus, further research can be conducted in this field.
Both neutral and positive credit attitudes positively affect the level of credit card debt, which also conforms with the previous findings (Chien and Devaney (2001); Lown and
Ju (1992); Godwin (1997); Kim and Devaney (2001)). The highest level of self-perceived financial literacy shrinks the level of credit card debt. The positive effect of financial education on reducing the credit card debt was captured by Agarwal et al. (2008). The link between low financial literacy and high credit card debt was also established by Ludlum et al. (2012). However, Gorbachev and Luengo-Prado (2016) concluded that households trapped with credit card debt puzzle tend to have higher financial literacy scores. This contradiction also creates room for supplimetary research in the field of credit card debt puzzle.
Variables controlling for correspondent’s ethnicity and objective financial literacy were omitted from the analysis. Further on, variables for education and occupational status of the head of the family turned out to be insignificant. We speculate that correlations among explanatory variables can be the reason that these variables were ruled out.
The investigation of dataset led to some valuable insights about the structure of credit card debt for US families: we have seen that the families with level of education of the head of household are more likely to have higher average credit card debt, however, it presents a lower share of income for families with more educated head. We have also seen that there are specific characteristics of families who have the highest financial literacy: these families are more likely to be confident in one’s finances, they are more likely to be better educated, have higher income and credit card debt and hold positive attitudes towards credit. We have also seen that families who hold positive attitudes towards credit are more likely to be financially educated, more likely to have a higher average income and higher average credit card debt.
The credit card industry in the US is an essential part of the financial system of the country. We have evaluated the main characteristics of households that affect the level of credit card debt. This can help to enhance the policymaking process because understanding the types of households who excessively use credit can gain us access to sharpening the credit card issuance policies and undertake actions to ensure that all participants in credit card networks understand the structure of the market and use the best credit practices. This might be now important more than ever before, with the credit card market being on the verge of potentially one of the most catastrophic financial crises in modern history. The gains from loans for the credit card issuers are eroded, and people are facing severe unemployment and financial instability, which has the power to
discourage spending for a long period of time (Andriotis and McCaffrey 2020).
Comprehension of fundamental factors affecting the level of debt can help us to build a more flexible system and mitigate the consequences of this crisis.
As has been discussed above, some factors were ruled out from the model or turned out to be insignificant. This indicates the limited explanatory power of this thesis, but also creates prospects for further researches. Since the consumer preferences are being altered greatly nowadays, it would be intriguing to compare the results of similar analysis a few years from now to capture the effect of the pandemic. Apart from that, it might be curious to evaluate the factors affecting the level of credit card debt across different countries and point out the main differences. We have also briefly touched the alternative explanations of the factors altering the credit card debt from the behavioral economics standpoint. This creates yet another intriguing potential for the research, since combining socioeconomic predictors together with behavioral theories (such as hyperbolic discounting and mental accounting) can create a more comprehensive picture of the factors influencing the borrowing behavior in the credit card market. The significance of the topic of credit card debt will persist throughout the time and new findings, more available data and large-scale economic events will keep reigniting the academic interest in this topic.
Abbreviations List
ABS Asset-backed securities APR Annual percentage rate
CDO Collateralized debt obligations CDS Credit default swaps
FDCPA Fair Debt Collection Practices Act FED Federal Reserve System
FICO Fair, Isaac and Company GFC Global Financial Crisis LCH Life-cycle hypothesis OLS Ordinary least squares PEU Primary economic unit
SCF Survey of Consumer Finances
SDA Survey Documentation and Analysis portal SPV Special purpose vehicle
TALF Term Asset-Backed Securities Loan Facility
List of Figures and Tables
Figure 1 – Credit Card Network Structure ... - 3 -
Figure 2 - Total Outstanding Debt in the US and its Composition ... - 15 -
Figure 3 - Percentage Change of Total Credit Card Debt Versus Total Outstanding Debt in the US ... - 16 -
Figure 4 - Number of Accounts in the US by Loan Type ... - 17 -
Figure 5 - Commercial Bank Interest Rate on Credit Card Plans ... - 18 -
Figure 6 - Transition into Serious Delinquency (90+) ... - 19 -
Figure 7 - Delinquency Rate on Credit Card Loans, Banks Not Among the 100 Largest in Size ... - 20 -
Figure 8 – US ABS Issuance ... - 21 -
Figure 9 – Percentage Difference in Outstanding ABS for Credit Card Debt ... - 22 -
Figure 10 – Average Credit Card Debt by Demographic Factors ... - 30 -
Figure 11 – Average Ratio of Credit Card Debt to Total Income by Education - 31 - Figure 12 – Credit Card Debt and Unemployment Rate in the US: Actual versus Predicted ... - 47 -
Table 1 – Variables Means ... - 32 -
Table 2 – Means by Financial Literacy Score ... - 33 -
Table 3 – Means by Credit Attitude ... - 35 -
Table 4 – Stepwise Regression Output ... - 38 -
Table 5 – Tobit Regression Output ... - 41 -
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Appendix
Variable Definition Coding
AGE Age of head of household Continuous
EDUC
Highest completed grade by head
of household Continuous
-1 - Less Than 1st Grage 1 - 1st, 2nd, 3rd,or 4th Grade 2 - 5th or 6th Grade
3 - 7th or 8th Grade 4 - 9th Grade 5 - 10th Grade 6 - 11th Grade
7 - 12th Grage, No Diploma 8 - High School Graduate - High School Diploma Or Equivalent 9 - Some College But No Degree 10 - Associate Degree In College - Occupation/Vocation Program 11 - Associate Degree In College - Academic Program
12 - Bachelor's Degree (For Example: BA, AB, BS) 13 - Master's Degree
14 - Doctorate or Professional School Degree
BSHOPGRDL Favorable attitude towards credit 1 = yes 0 = no BSHOPMODR Moderate attitude towards credit 1 = yes
0 = no BSHOPNONE Negative attitude towards credit 1 = yes
0 = no KNOWL0 Not at all knowledgeable about
personal finance
1 = yes 0 = no