Research

Weathering Volatility 2.0

A Monthly Stress Test to Guide Savings

October 23, 2019

Findings

Executive Summary

In this report, the JPMorgan Chase Institute uses administrative bank account data to measure income and spending volatility and the minimum levels of cash buffer families need to weather adverse income and spending shocks.

Inconsistent or unpredictable swings in families’ income and expenses make it difficult to plan spending, pay down debt, or determine how much to save. Managing these swings, or volatility, is increasingly acknowledged as an important component of American families’ financial security. In prior JPMorgan Chase Institute (JPMCI) research, we have documented the high levels of income and expense volatility families experience. In this report, we make further progress toward understanding how volatility affects families and what levels of cash buffer they need to weather adverse income and spending shocks. We explore six key questions:

  1. What is the trend of month-to-month income volatility between 2013 and 2018?
  2. What is the distribution of income volatility and is it persistent from year to year?
  3. What are the prevalence and magnitude of income spikes versus dips?
  4. How does income volatility differ across demographic groups?
  5. How does month-to-month spending volatility compare to income volatility, overall and across demographic groups?
  6. What are the minimum levels of cash buffer that families need to weather adverse income and spending shocks?
Infographic describes about FROM THE ENTIRE UNIVERSE OF NEARLY 40 MILLION CHASE DEPOSIT CUSTOMERS

Source: JPMorgan Chase Institute

From the entire universe of nearly forty million Chase deposit customers, we assembled six million anonymized families to form a 75-month balanced panel (October 2012 to December 2018), for whom we have detailed transaction-level information on income, spending, and account balances (checking and savings) held at Chase. Our unit of analysis is the primary account holder, which we refer to as a “family.”

To be included in our sample, an account holder must have: 1. At least five transactions (inflows or outflows) from a personal checking account in every month between October 2012 and December 2018. This attempts to ensure the Chase account observed is the account holder’s active bank account. 2. At least $400 in average monthly total income for every twelve-month rolling period. This serves to filter for account holders whose income is likely landing at the Chase account observed. 3. At least $10 in average spending, and at least $1 spent every month. This attempts to ensure we see spending activity for a given account.

Incomes we observe are take-home incomes, meaning after taxes and payroll deductions. Income categories we construct in our data set include labor income (i.e. payroll and other direct deposits) and non-labor income (i.e. government income, capital income, and otherwise).

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Income volatility remained relatively constant between 2013 and 2018. Those with the median level of volatility, on average, experienced a 36 percent change in income month-to-month during the prior year.

Line graph describes about Median coefficient of variation for total income

Source: JPMorgan Chase Institute

Line graph showing the median coefficient of variation (CV) for total income between 2013 and 2018. Coefficient of variation (CV) is our measure of month-to-month volatility. CV measures the dispersion of a family’s income in a given month relative to the mean income of the prior twelve-months, including the month measured. The median CV remains constant, around 0.38 between 2013 and 2018.

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There is wide variation in the levels of income volatility families experience, both across families at a given point in time and also for a given family across time.

Bar graph describes about Distribution of coefficient of variation (total income)

Source: JPMorgan Chase Institute

Bar graph showing the distribution of coefficient of variation for total income. Families vary significantly in levels of income volatility they experience. The distribution of CV has a standard deviation of 0.37 with a long right tail. About eight percent of family-months have a CV above 1.0, which would correspond to a more than 60 percent change in monthly income within a year.

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On average, families experience large income swings, in almost five months out of a year. Income spikes are twice as likely as income dips and most common in March and December. Families with the most volatile incomes experience swings that are larger but not more frequent than families with less volatile incomes.

Line graph1 describes about Frequency of income spikes/dips vs. coefficient of variation and Line graph2 describes about Magnitude of income spikes/dips vs. coefficient of variation (percent change from baseline income)

Source: JPMorgan Chase Institute

Two scatterplots measuring the magnitude of income spikes and dips as a percent change from the median monthly income during the prior year, and the frequency of income spikes and dips as a percent change from the median monthly income during the prior year. Families whose coefficient of variation (CV) fall in the middle bin of the CV distribution experience spikes that are 51 percent above and 56 percent below their baseline income. As families’ incomes become more volatile, the frequency of income swings they experience flattens out but not the magnitude. Beyond a CV of 1.0, families’ frequency of income swings plateaus. The magnitude of income swings, however does not plateau as families’ incomes become more volatile. As CV increases, the magnitude of income spikes and dips continues to increase, especially that of spikes.

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Income volatility is greatest amongst the young and the high income. However, downside risks, as measured by the magnitude and frequency of income dips, are greatest among low-income families.

Line graph1 describes about Frequency of income swings (median number of spikes/dips in a year) and Line graph2 describes about Magnitude of income swings (percent change from baseline income)

Source: JPMorgan Chase Institute

Two dot plots examining the heterogeneity of income volatility n terms of the average number of income spikes and dips by take-home income quintile. Higher-income families experience more frequent income swings. Lower-income families experience the largest dips when dips happen.

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The trend of spending volatility was flat between 2013 and 2018. While the level of spending volatility was also high, it was 15 percent lower than that of income volatility, except among account holders over the age of 75 and those with the largest cash buffers.

Bar graph describes about Median coefficient of variation of spending and income by demographics

Source: JPMorgan Chase Institute

Bar graph examining heterogeneity of median spending and income coefficient of variation by demographic groups. We show demographic groups by: age bins of 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75+; five income quintiles, gender, and five quintiles of cash buffer month. Spending volatility is lower than income volatility, except among account holders above age 75 and those with the largest cash buffers.

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Families need roughly six weeks of take-home income in liquid assets to weather a simultaneous income dip and expenditure spike. Sixty-five percent of families lack a sufficient cash buffer to do so.

Infographic describes about Event Frequency Magnitude of cash buffer needed to weather event (median weeks of income) Proportion of families with insufficient cash buffer to weather event
Event Frequency Magnitude of cash buffer needed to weather event (median weeks of income) Proportion of families with insufficient cash buffer to weather event
Simultaneous Income did & expenditure spike Once everry 5.5 years 6.2 weeks 65 percent
Income dip once every 9 months 2.8 weeks 48 percent
Expenditure spike Once every 4 months 2.6 weeks 46 percent

Our findings have important implications for designing savings strategies to improve families’ financial health and resilience. They suggest that the tools currently available to help families weather volatile income and spending could be better tailored to an individual’s cash flows. Simply saving a certain percentage of monthly income may leave a family with an inadequate cash buffer, exacerbating financial distress in cash flow negative months and resulting in under-saving during cash flow positive months. Instead, families may need to more aggressively harvest savings opportunities during income spike months. We provide empirical guidance for families, financial health advocates, financial advisors, and policymakers on the minimum levels of cash buffer families need to weather adverse shocks. Given the key role stability plays in the health of families’ financial life, it is critical that we continue to gauge how income and spending volatility are changing for American families and the implications for families’ financial health.

Authors

Chenxi Yu

Chenxi Yu

Data Scientist

Diana Farrell

Diana Farrell

Founding and Former President & CEO

Fiona Greig

Fiona Greig

Former Co-President