Introduction

Credit Card Utilization Rate by Education Level


    Unknown  Uneducated High School     College    Graduate   Doctorate 
       1519        1487        2013        1013        3644         451 

# A tibble: 6 × 2
  Education_Level mean_avg_util_ratio
  <chr>                         <dbl>
1 College                        0.27
2 Doctorate                      0.26
3 Graduate                       0.28
4 High School                    0.28
5 Uneducated                     0.27
6 Unknown                        0.28

Question: Do education levels have a significance in one’s utilization ratio?

This line plot observes the relationship between credit limits and and utilization rate between different education levels.

The plot shows a negative relationship between credit limit and utilization rate. What this means is that as credit limit increases, the utilization ratio decreases.

Conclusions from this data:

The most simple answer is that because one’s credit limit is lower to begin with, the debt accounts for more of the balance then one with a higher balance.

In a more in depth scale, the percentage of a zero utilization ratio per education goes as follows:

Unknown = 24.7%

Uneducated = 24.4%

High School = 23.7%

College = 26.8%

Graduate = 23.9%

Doctorate = 25.1%

Though the numbers are all close to one another, those with college as their highest education level had the highest amount of people not using their credit card at all.

Also looking at the average utilization ratio across education levels, there is not enough disparity between them to say that there is any association. With all their averages in the 0.20-0.30 range I would not say one education level tends to have a higher ratio then others.

Data Source

Kaggle

Age vs Credit Limit by Gender and Income Level

Questions for this data:

How does the age of a credit card customer impact the credit limit they receive?

How does this vary between gender and between income level?

Conclusions from this data:

These two plots look at the relationship between credit limits and age with a third variable, the first graph includes the gender and the second graph includes the income categories. (As a note the legitimacy of this data is unclear but it is realistic to what the actual data would look like.)

In the gender graph, men receive more credit than women throughout all ages. This could be understood in the context of the ongoing gender inequality present in the country.

In the income category graph, there is a clear positive relationship between income level and credit limit. This is because a person who does not have a high income will not be able to/be as likely to pay back their debt on their credit card bills. Similarly, a credit card customer with a high income will likely have more disposable income and will be approved for a more “premium” card with a higher line of credit.

Data Source: Kaggle

Card Transactions across Industries (February 2020 - Present Day)

Question How has spending changed across industries as a result of the COVID pandemic?

This panel contains higher charts of the monthly spending of credit card transaction across 4 significant industries. The monthly values are represented by the predicted percent different from the typical level of spending and are summarized by the median value and lower or upper bounds, from a 95% confidence interval. As seen in the graphs, spending fluctuates from each month with the most drastic fluctuations seen in the second half of 2020. This is reflective of the economic changes and difficulties that consumers faced as a result of the COVID pandemic, in March of 2020.

As of today these are the median predicted percentages:

Gasoline Stations : 5.6%

Ambulatory Health Care: 8.1%

Gambling Amusement and Recreation :-7.4%

Food Service and Drinking Place : -12.9%

Data Source U.S. Bureau of Economic Analysis

Total Food and Retail Transactions and Deliquency Rates

This plot observes the total food and retail transactions, from the previous monthly spending predictions, against delinquency rates on credit cards. The total food and retail transactions follow a similar fluctuation in the second half of 2020, while the delinquency rate shows trends between 1.5 and 3. The lowest period of delinquency rate can be seen from Quarter 2 of 2021 through the end of 2021.

Data Source

FRED

Delinquency Rates
Date Rate
2020-01-31 2.66
2020-04-30 2.45
2020-07-31 2.00
2020-10-31 2.12
2021-01-31 1.86
2021-04-30 1.59
2021-07-31 1.54
2021-10-31 1.58
2022-01-31 1.67
2022-04-30 1.84
2022-07-31 2.07
2022-10-31 2.27
2023-01-31 2.44
2023-04-30 2.76
2023-07-31 2.97
2023-10-31 3.10