By Juan Sánchez, Vice Vice President and Economist, St. Louis Fed; Ryan Mather, Research Associate, St. Louis Fed; Kartik Athreya, Executive Vice President and Research Director of the Richmond Fed; and José Mustre-del-Rio, senior economist at the Kansas City Fed
The following is the fourth in a series that examines the potential impact of COVID-19 on people living in areas where there are already above-average financial problems.
The COVID-19 pandemic will lead to economic losses How These losses are distributed among the population and are just as important as the total amount. Our previous posts in this series have revealed an unsettling pattern. Communities with the greatest financial hardship – the least able to survive economic losses – both:
We used these results to provide a simple prediction of how much more income from less-favored areas would lose compared to less needy areas. We then fed this forecast into an economic model to see how consumption would react. Not surprisingly, the results indicated that consumption losses would be significantly higher in areas with greater financial hardship.
In this post, we continue to examine the same exercise in the model, and this time ask how the loss of income will affect financial assets, late payments, and bankruptcy. It is important that our definition of financial assets in the model does not contain any risky assets (such as shares) and instead refers to assets such as check and savings accounts or certificates of deposit. shares fell towards the start of the COVID-19 pandemic, but it is difficult to say how these losses will spread to the population. Instead, we’re just changing the income within the model and examining how that channel alone affects savings on risk-free assets, home equity, and rejection of unsecured debt through late payments and bankruptcy.
How financially disadvantaged people react to loss of income
The following table makes it clear that financial hardship is again an issue. We see that the reaction of the individual points examined differs significantly in the quintiles of financial hardship, with the first quintile representing the 20% of the postcodes with the least financial hardship and the fifth quintile the 20% with the most. We allow the level of income loss to vary in the quintile of financial hardship to capture the non-uniformity of the COVID-related employment risk that we documented in our first post.
The first line shows the loss of income that was fed into the model for each quintile of financial need. The remaining lines show the reaction of various items in the budget balance sheet to the given drop in income. By construction, Row a equals 100% because we divide all rows by the quantile-specific drop in income.
|Quintiles of financial hardship|
|Percentage decrease in income||-6.36||-6.78||-7.66||-8.33||-9.37|
|Percentage of decrease in income|
|a. Decline in income||100.0||100.0||100.0||100.0||100.0|
|b. Decline in assets||54.5||49.1||45.5||41.8||32.2|
|c. Increase in debt||1.5||2.0||2.2||2.4||2.8|
|d. Decrease in home equity||17.1||18.2||17.5||18.7||20.1|
|e. Decrease in consumption||26.8||30.7||34.8||37.1||45.0|
|SOURCES: Authors’ calculations based on the model used in their work “Consumption in the Great Recession: The Channel for Financial Need”.|
If we focus on people in the first quarter (the least financially troubled), we see that they reduce the consumption the least (Line e) – only 27 cents a dollar loss of income. They are able to protect most of their consumption from loss of income by delving into their savings (Line b) because the decline in their wealth offsets more than 50% of the decrease in income. This decrease in savings combined with a decrease in home equity (Row d) helps to compensate more than 70% of the loss of income. This suggests that these people in the model have enough assets in their homes to borrow against them in difficult times.
In sharp contrast to this, in the fifth quarter, due to their “already existing state” of being in the greatest financial emergency and having weaker balance sheets, people reduce consumption by almost twice their colleagues with healthy balance sheets. Your expenses decrease by 45 cents per dollar of income loss. The decline in consumption is not because these consumers prefer it, but because they cannot effectively dive into assets (savings and home equity) to survive the income shock. For these people, immersing themselves in savings and home equity only makes up about half (52.3%) of the drop in income, leaving their old standard of living unreachable.
The role of debt in this breakdown is minimal. However, debt is the only source of finance for tenants with no liquid savings. This is probably true for younger households. Also note that debt may vary due to changes in financial hardship. Debt increases for people who are fined for skipped payments (late payments) and decreases for people who choose to file for bankruptcy. The following table describes the development of the financial emergency.
How the financial emergency changes in response to loss of income
The following table shows how the financial emergency changes in response to loss of income and how these changes are disproportionately carried out by groups who were already in an increased emergency. To simplify the interpretation, we scale the model to the labor force of the US economy so that it represents around 164 million people.
Given this number, the first row of the table shows that more than 2 million additional people are likely to default on their debts than would otherwise be the case, and that about a third million more will go bankrupt.
|Overall among all workers||2,186,146||326.045|
|Of the top 20% of the already existing financial emergency|
|Share in the total number||28%||22%|
|Of the top 50% of the already existing financial emergency|
|Share in the total number||54%||55%|
|NOTES: The top 20% are the 20% of those in greatest financial need. Similarly, the top 50% are the 50% of the most deprived.|
|The authors’ calculations are based on the model used in their work “Consumption in the Great Recession: The Channel for Financial Need”.|
The rest of the table shows that the increase in arrears and bankruptcies is concentrated on those who are already in need. For example, people who are in the top 20% of pre-existing financial hardship (i.e., those in Q5, the worst financial hardship) suffer from 28% of the additional arrears resulting from the simulated income shock. This is surprising in that these additional cases of crime and bankruptcy are added to the already elevated conditions. In other words, people with the worst pre-existing financial hardship are also most prone to need more deeply, which exacerbates the impact of pre-existing conditions.
That being said, the lower box in the second table shows that if we reduce the distribution of financial hardship, things will become more proportional. Indeed, 54% of the additional arrears resulting from the shock are among the top 50% of pre-existing financial emergencies. In other words, they account for a portion of additional arrears that are closer to their share of the population.
One thing that is not covered in this table is the timing of the response to late payments and bankruptcies. In the model, most of the increase in financial hardship occurs after the initial income shock. This reflects a certain delay, which we believe also exists in the real world. Assuming that a person is up to date with their debt payments at the time of the loss of income, it will take at least 30 days before they can be in arrears for 30 days. This is the threshold from which the New York Federal Reserve / Equifax The Consumer Credit Panel (CCP) record records debt as criminal.
Overall, our simulated model suggests two main economic predictions during the COVID 19 pandemic: First, communities with the greatest financial hardship are likely to reduce their consumption the most, both because they are less prepared for weather shocks and because the income shocks they experience are expected to be larger. Second, as the pandemic progresses, these communities are more likely to face additional financial difficulties.
In our view, these results suggest that communities in need should be given special attention when the government takes action. In a future post, we’ll evaluate some of the suggested guidelines and give an insight into which may be the most effective.
Notes and references
1 See for example our youngest Working paper We estimate that the uneven distribution of property price losses during the Great Recession increased the overall decrease in consumption by 40%.
2nd See also ours daily updates of this data, which shows that severely financially disadvantaged communities continue to be at higher risk of developing the disease.
3rd The model used in this and this post is described in more detail in our last post Working paper.
4th You can find details of their construction in our second post.
5 This was our source for the computation of financial distress statistics in previous posts and is the main source for this topic.
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