Thus, this categorical variable takes values in the range from 0 (not at all knowledgeable about personal finance) to 10 (very knowledgeable about personal finances). Similar to the previous modifications, this variable is transformed into a vector of dummy variables.
Dummy variable knowl10 takes value 1 for families who believe they are very knowledgeable about personal finances, 0 otherwise. Dummies to knowl0 are created in the same manner, which takes value 1 for families who are not at all knowledgeable about personal finance, 0 otherwise2.
historically have the smallest average debt. I attribute this to the smaller participation in the labor force and thus lower incomes, which set a constrain for borrowing. Groups between 35 and 75 take the lead, and although they have better accessibility to the credit, they also had to cut down on borrowing more drastically after the Global Financial Crisis.
The average debt is increasing with the higher obtained degree by the household’s head (Figure 10C). We can see that for households with Doctor of Professional school degree the average debt is soaring above all lower degrees and also peaks in 2007 with a gradual decrease even below bachelor group average in 2010. On the one hand, better education should empower people to undertake more rational choices. However, higher credit card debt does not explicitly mean irrational choices – given that families evaluate risks, analyze interest rates and compare utility from borrowing at a given period of time versus keeping the consumption constraint lower, higher credit card debt can be attributed to
Source: The SCF, 2017, own calculations
Figure 10 –Average Credit Card Debt by Demographic Factors
rational behavior. Further investigation of the data will provide more confirmations of this argument: I will show that both average income and average highest attained level of education are positively correlated with the objective financial literacy. Apart from that, higher education is traditionally attributed to higher standards of living, thus, although absolute values of credit card debt might be high, they might relatively lower as a portion of total household income for consumers with higher education.
Married households have an average debt higher than non-married households for all respectful years and they have a great magnitude of changes (Figure 10D). This can be explained by a more differentiated pool of needs which can be covered by credit card for families with more members.
As the last historical development breakdown, I show the average ratio of credit card debt to the total income based on education groups. As we can clearly see in Figure 11, empirical data confirms the explanation of the average credit card debt distribution across educational groups. Although in absolute terms families with the most educated head have the highest average credit card debt, the ratio of credit card debt to total income shows the reversed pattern: it is the lowest for the highest obtained degrees. As per 2016, credit card debt to income ratio was 1,1 percent for families with a head who has a doctorate or professional school degree, whereas for families with a head who has the
Source: The SCF, 2017, own calculations
Figure 11 – Average Ratio of Credit Card Debt to Total Income by Education
high school diploma at best this ratio was 3,6 percent. Thus, although families with better-educated heads borrow more, for them these borrowings are a small fraction of their total income comparing to families with a head with lower attained education. This figure also illustrates the responsiveness of more affluent families to the negative shocks: the group with the highest degree of education decreased the ratio from 2,9 percent in 2007 to 0,7 percent in 2010. This also indicated that these families are not hugely dependent on credit, whereas less educated families have a bigger reliance on credit cards to stimulate their consumption.
Table 1 provides an overview of the means of the variables employed in the model. The average credit card debt among all families who participated in the survey was 2 506 USD. The average age of the head of the household is 53 years. The average number of years of schooling is 10 (which corresponds to the associate degree in college - occupation/vocation program) (FED Staff 2017, p. 728 - 729). 33 percent of households reported a generally favorable attitude towards credit with 49 percent reporting neutral attitude.
When it comes to the reported financial literacy, on average, families are quite self-confident with the score of 7,5 points out of 10 on average. The average score for the financial literacy questions is also quite impressive: 2,3 points out of 3 on average. Almost
Characteristics Mean (st. d.) %
Credit Card Debt 2 506,68 (7 874)
Age 52,7 years (16,22)
Years of education 10,04 years (2,8)
Favorable credit attitude 33,29%
Neutral credit attitude 48,84%
Self-reported financial literacy 7,46 points (2,16)
Financial literacy 2,29 points (0,84)
White 71,56%
Professional 36,24%
Income $102 273,97 (5 452 944)
House Ownership 66,84%
Marriage 62,48%
N = 6248
Source: The SCF, 2017, own calculations Table 1 – Variables Means
72 percent of households’ head ethnic group is white and 36% of heads of the households work in the managerial or professional industry. The average income is 102 thousand USD (with a great deviation due to some extremely wealthy outliers and many poor families). 67 percent of households possess some kind of real estate and 62 percent of households are either married or living with a partner. The total number of households participated in the SCF in 2016 is 6248.
In order to elaborate more on the characteristics of the sample and address the patterns based on the demographic breakdown, I would like to show average scores for all factors based on the number of correctly responded questions on financial literacy and attitude towards credit.
As we can see in Table 2, specific patterns start to emerge as we add granularity through the financial literacy questions.
The average credit card debt is increasing with the number of correctly answered financial literacy questions. However, the average level of education of the head of the family is positively correlated with financial literacy as well. It conforms with the explanations of rising credit card debt with higher education discussed previously. Thus, the average level of education of the head of the family for a group who did not respond any of the financial literacy questions correctly is just above 8 years (which corresponds to high school diploma), whereas for the group with the highest financial literacy score the average level of education is 11 years (associate degree in college - academic program). Both favorable and neutral attitudes towards using credit are also peaking for the group with the highest number of correct answers (35 and 51 percent respectively). Eventually, families with
Characteristics Mean % Mean % Mean % Mean %
Credit Card Debt $1 258,13 $2 047,82 $2 226,33 $3 046,69
Age 50,95 years 48,80 years 51,65 years 54,56 years
Years of education 8,39 years 8,46 years 9,38 years 11,01 years
Favorable credit attitude 27,71% 30,08% 32,78% 34,89%
Neutral credit attitude 44,91% 44,08% 47,72% 51,14%
Self-reported financial literacy 6,72 points 6,83 points 7,22 points 7,83 points
White 55,60% 54,23% 66,45% 80,72%
Professional 15,09% 17,14% 28,75% 47,79%
Income $46 124,23 $52 511,07 $68 095,04 $155 871,21
House Ownership 44,40% 45,54% 60,22% 78,52%
Marriage 43,53% 50,23% 55,76% 71,52%
Number of correct financial literacy answers
0 1 2 3
Source: The SCF, 2017, own calculations Table 2– Means by Financial Literacy Score
better objective financial literacy are more confident in their finances: self-reported knowledge of family’s finances is increasing from 6,7 points for the least financially educated group to 7,8 points for the most financially educated group. The average age of head of the households is the highest for the most financially educated households, peaking at 54,6 years.
Other variables also respond drastically to the change in financial literacy. The share of households with a head of white ethnic group category is bumping from 56 percent for a group with zero correct financial literacy answers to 81 percent in the group with the highest score. The percentage of households with the head employed at the professional or managerial industry is also increasing from 15 percent for the least financially educated group to 48 percent for the most educated group. Average income is rising from 46 thousand USD up to almost 156 thousand USD from the least educated group to the most educated one respectively, thus increasing by more than 300 percent. The share of households with real estate in their possession is also following the trend, ranging from 44 percent for the least financially educated group up to 79 percent in the best financially educated group. Additionally, a portion of married households is increasing from the least financially educated group to the highest one from 44 percent up to 72 percent.
Based on the results from Table 2 we can outline a specific profile of financially educated families in the US with respect to the factors employed in this analysis: these are affluent, married families with the ethnic white majority. A larger share of heads of financially educated families is employed at professional or managerial industries, they are more confident in one’s finances, better educated, hold bigger credit card debts, and hold a more positive attitude towards credit. This portion of analysis shows that credit cards can be utilized as a powerful consumption stimulus tool and provides a completely new perspective on the credit card debt: affluent, educated and financially educated US families seem to use it as a leverage tool to increase consumption when their incomes are secured, but they are not dependent on the credit since it adds to a relatively small share of their income. This picture is generally consistent with the findings of Gorbachev and Luengo-Prado (2016).
Table 3 shows the means of all variables differentiated by households’ attitudes towards credit.
The group which reported the neutral attitude towards credit holds the highest average credit card debt, which is more than twice as big as the average credit card debt for the group with a negative attitude towards a credit (2 956 USD versus 1 305 USD respectively). Since using credit cards imposes certain risks (possible penalty fees, rising interest rates, and extra commissions) we can attribute a negative attitude towards credit as a risk-averse behavior. On the contrary, a positive attitude towards credit is closer to risk-seeking behavior, because of the increasing uncertainty about one’s finances with credit cards. Neutral attitude towards credit would be eventually attributed to risk-neutral behavior. Since average credit card debt is higher for both risk-seeking and risk-neutral groups, the question is whether neutral or positive attitudes towards credit pay off? Based on other factors the answers seem to be yes.
For both favorable and neutral attitudes towards credit groups, the average degree of the head of household is an associate degree in college, whereas for negative attitudes towards credit groups the average level of education is college with no degree. Families with neutral or positive attitudes towards credit are also better financially educated (average score for both favorable and neutral credit attitude is 2,34 points versus 2,08 for negative credit attitude group). Accordingly, both favorable and neutral credit attitude groups are more self-confident in the family’s finances.
Characteristics Mean % Mean % Mean %
Credit Card Debt $2 532,57 $2 956,08 $1 305,25
Age 51,97 years 51,98 years 56,02 years
Years of education 10,27 years 10,22 years 9,10 years
Self-reported financial literacy 8,03 points 7,33 points 6,74 points
Financial literacy 2,34 points 2,34 points 2,08 points
White 71,22% 73,10% 68,00%
Professional 39,85% 38,69% 22,93%
Income $104 638,15 $112 333,23 $72 178,53
House Ownership 71,32% 67,36% 57,16%
Marriage 66,36% 63,74% 51,91%
Credit Attitude
Favorable Neutral Negative
Source: The SCF, 2017, own calculations Table 3– Means by Credit Attitude
Average income is the highest for the group with a neutral credit attitude, which is equal to 112 thousand USD and 72 thousand USD for the negative credit attitude group. The share of the house ownership is highest for favorable credit attitude group (71 percent) as well as a portion of married families (66 percent).
Clustering US households based on their attitude towards credit yields another intriguing insight about credit behavior: families who hold neutral and favorable attitudes towards credit are more willing to take financial risks associated with credit cards. Nevertheless, these risks seem to pay off, since a larger share of households with neutral or favorable credit attitude are better educated and more financially prosperous comparing to households who hold negative attitudes towards credit, hence are not willing to take associated financial risks.
Adding levels of details to the descriptive analysis amplifies the importance of identifying the key factors affecting the level of credit card debt in the US. We have outlined the profile of the sample by main demographic indicators and have shown different properties of families based on the financial literacy and attitudes towards credit. Intriguing patterns that emerged as a result of descriptive analysis emphasize the importance of all three categories of factors that affect the level of credit card debt (demographic, economic, and attitudinal).