Weathering Volatility

May 2015

Big Data on the Financial Ups and Downs of US Individuals

May 2015


Cover of the JPMorgan Chase Institute report, 'Weathering Volatility: Big Data on the Financial Ups and Downs of US Individuals'In this inaugural report, researchers from the JPMorgan Chase Institute analyzed proprietary data from JPMorgan Chase & Co. to determine how income and consumption fluctuate on a monthly and a yearly basis.

Download Full Report “Weathering Volatility:Big Data on the Financial Ups and Downs of US Individuals”

View Text Version infographic describing the JPMorgan Chase Institute Data Asset Sample

To draw conclusions about fluctuations in earning and spending amongst US individuals, our findings are summarized into three key points…

Finding One

Income & Consumption Volatility

Finding Two

Behavioral Groupings

Finding Three

Financial Buffer

Individuals experienced high levels of income volatility and higher levels of consumption volatility across the income spectrum.

Volatility was even greater on a month-to-month basis than on a year-to-year basis. The highest income earners experienced as much volatility in both income and consumption as the lowest income earners. Some of the drivers of monthly volatility included months with five Fridays, when employees may be paid three times instead of two, tax bills and refunds, and the year-end shopping season.

Two graphs depicting month-to-month volatility in income and consumption
Two graphs depicting month-to-month volatility in income and consumption
View Text Version of the two graphs depicting month-to-month volatility in income and consumption

Income and consumption changes did not move in tandem; there was only a slightly positive correlation between changes in income and changes in consumption between 2013 and 2014. Three behavioral groupings describe the link between income and consumption changes.

Scatter plot graph showing the change in income compared to the change in consumption from 2013 to 2014

Responders

Individuals for whom income and consumption changes are within 10 percentage points of each other. Responders are more likely to have lower annual incomes and less access to liquidity through credit cards. They account for 28% of our sample.

28% of people
Graph depicting Responders, with income and consumption within 10 percentage points of each other

Sticky Optimists

Individuals for whom consumption changes are higher than income changes by more than 10 percentage points. Sticky Optimists are more likely to have higher annual incomes and more spending power through credit cards. They account for 33% of our sample.

33% of people
Graph depicting Sticky Optimists, with consumption changes higher than income changes by more than 10 percentage points

Sticky Pessimists

Individuals for whom consumption changes are lower than income changes by more than 10 percentage points. Sticky Pessimists are equally represented across income levels — from low–income to high–income — and they make up 39% of our sample.

39% of people
Graph depicting Sticky Pessimists, with consumption changes lower than income changes by more than 10 percentage points

The typical individual did not have a sufficient financial buffer to weather the degree of income and consumption volatility that we observed in our data.

The typical household did not maintain enough liquid savings that could be accessed immediately in the event of a large, unexpected expense sustained at the same time as a loss in income. While many in the field of consumer finance have long advised that consumers maintain an emergency fund, our research into income and consumption volatility shows that a financial buffer is a more important consideration for individuals across the entire income spectrum than is generally understood. We find that not only was volatility high for income and consumption, but also changes in income and consumption did not move in tandem. This creates the risk that people might experience a negative swing in income at the same time that they incur a large, potentially unexpected, expense. Based on our findings, we estimate that a typical middle–income household needed approximately $4,800 in liquid assets — roughly 14% of annual income after taxes — to have sustained the observed monthly fluctuations in income and spending but they had only $3,000. Required levels of liquid assets, however, were largely unavailable to most individuals across quintiles, except top earners.

Conclusion

We conclude from these early findings that, given how noisy and unpredictable financial lives are, most individuals would benefit from innovative tools to better understand and manage their bottom line. These tools could include analytical platforms that help people track their earning and spending patterns as well as the sources, magnitude, and timing of fluctuations in income and consumption. In addition, financial service providers, employers and policy makers can help individuals reduce and manage volatility, better match income and consumption changes, or put these fluctuations to good use to help them save money. Potential solutions include new insurance and credit products to help smooth income and spending; technical solutions, such as making deposited funds more immediately available to banking customers; and products or automated transfers that allow people to save during naturally occurring upswings in income, such as on five-Friday months and tax refund season.

Download Full Report “Weathering Volatility:Big Data on the Financial Ups and Downs of US Individuals”