Board of Governors of the Federal Reserve System

Changes in U.S. Family Finances from 2016 to 2019: Evidence from the Survey of Consumer Finances

Neil Bhutta, Jesse Bricker, Andrew C. Chang, Lisa J. Dettling, Sarena Goodman, Joanne W. Hsu, Kevin B. Moore, Sarah Reber, Alice Henriques Volz, and Richard A. Windle, of the Board's Division of Research and Statistics, prepared this article with assistance from Kathy Bi, Jacqueline Blair, Julia Hewitt, and Dalton Ruh.

 

The Federal Reserve Board's triennial Survey of Consumer Finances (SCF) collects information about family income, net worth, balance sheet components, credit use, and other financial outcomes.¹ The 2019 SCF reveals improvements in economic well-being among large parts of the income and wealth distributions since the previous time the survey was conducted in 2016, and many groups with historically lower income and wealth saw relatively large gains.²

During the three years between the beginning of the 2016 and 2019 surveys, real gross domestic product grew at an annual rate of 2.5 percent, and the civilian unemployment rate fell from 5.0 percent to 3.8 percent.³ These changes in aggregate economic performance were unevenly reflected in the income of families with different characteristics. Several observations from the SCF about real family income, which is measured for the year before the survey, stand out:

• Between 2016 and 2019, median family income rose 5 percent, and mean family income decreased 3 percent (figure 1). These changes suggest that the income distribution narrowed slightly over the period, particularly as the decrease in mean income was mainly driven by families in the top 1 percent of the income distribution (see box 1, "The Data Used in This Article"). These patterns stand in contrast to the 2010–16 period, during which mean income growth vastly outpaced median income growth and the income distribution widened considerably.

• Between 2016 and 2019, families that were high wealth, had a college education, or identified as White non-Hispanic experienced proportionally smaller income growth than other groups of families but continued to have the highest income:

  • In grouping families by wealth, families at the top of the distribution experienced a sharp decline in average income (following particularly outsized gains over the 2010–16 period), whereas families in the lower and middle portions of the wealth distribution all saw modest gains.

  • In grouping families by the reference person's educational attainment, those with a college degree experienced relatively large declines in both median and mean income, whereas those with a high school diploma and those with some college experience saw gains. More broadly, the income gaps between families with a college degree and those without one decreased.

  • Black non-Hispanic families and White non-Hispanic families experienced similar growth in median income, but mean income fell for White non-Hispanic families and rose slightly for Black non-Hispanic families.

The improvements in economic activity along with rising house and corporate equity prices combined to support continued increases in median and mean family net worth (wealth) between 2016 and 2019.⁴ The national CoreLogic Home Price Index increased at an annual rate of 5.2 percent between early 2016 and early 2019, exceeding the rate of consumer price inflation. The value of corporate equity holdings, as measured by a broad stock price index, grew at around an 11.5 percent annual rate between the two surveys, leading to large inflation-adjusted increases in equity holdings.⁵ These price trends contributed to the following changes in the distribution of household net worth:

• Between 2016 and 2019, median net worth grew 18 percent, and mean net worth rose a modest 2 percent (figure 2). In contrast, the 2010–16 period saw outsized gains in mean net worth relative to median net worth, driven by growth between 2013 and 2016.
• Families at the top of the income and wealth distributions experienced very little, if any, growth in median and mean net worth between 2016 and 2019 after experiencing large gains between 2013 and 2016.
• Families near the bottom of the income and wealth distributions generally continued to experience substantial gains in median and mean net worth between 2016 and 2019.
• Wealth continued to increase among families with either a high school diploma or some college. However, families without a high school diploma, which saw the largest proportional gains in median and mean net worth between 2013 and 2016, saw the largest drops between 2016 and 2019.
• The homeownership rate increased between 2016 and 2019 to 64.9 percent, a reversal of the declining trend between 2004 and 2016. For families that own a home, the median net housing value (the value of a home minus home-secured debt) rose to about $120,000 from about $106,000 in 2016.
• Nearly two-thirds of working-age families participated in retirement plans in 2019, down slightly from 2016. Participation continued to be uneven across the income distribution. Less than 40 percent of families in the bottom half of the income distribution were in a retirement plan, compared with more than 80 percent of upper-middle-income families and more than 90 percent of families in the top decile of income.
• Ownership rates of corporate equities increased between 2016 and 2019, driven by families in the lower half of the income distribution. Still, less than one-third of lower-income families in 2019 were participating in the stock market, compared with about 70 percent of upper-middle-income families and more than 90 percent of families in the top decile of the income distribution.
• About 13 percent of families in the 2019 SCF owned a privately held business, similar to 2016. Business ownership increases with income, and nearly 40 percent of families in the top decile of the income distribution owned a business.

Between 2016 and 2019, average consumer loan interest rates for major types of debt increased: The average 30-year, fixed-rate mortgage interest rate rose from 3.7 percent to 4.3 percent, the average new vehicle loan interest rate rose from 4.2 percent to 5.5 percent, and the average credit card interest rate rose from 12.3 percent to 15.1 percent.⁶ While the fraction of families with any kind of debt basically held steady between 2016 and 2019, debt balances among families with debt increased:

• Overall, debt obligations increased modestly between 2016 and 2019. Among families with debt, median debt rose 2 percent, and mean debt increased 7 percent.
• Debt secured by residential property increased substantially between 2016 and 2019. About 42 percent of families in both 2016 and 2019 had debt secured by their primary residence, and the median value of this debt increased 14 percent to $134,800.
• Between 2016 and 2019, the fraction of families with credit card debt increased. In addition, median and mean balances for families with credit card debt increased, to $2,700 and $6,300, respectively.
• About 22 percent of families owed student loan debt in 2019, similar to the fraction in 2016. Median balances among families owing such debt rose 10 percent between 2016 and 2019, to more than $22,000.
• Amid rising balances and interest rates, median debt payment-to-income ratios among families with debt increased slightly between 2016 and 2019. The fraction of families with payment-to-income ratios greater than 40 percent increased 0.4 percentage point to 7.4 percent. These increases reversed declining trends observed from 2007 through 2016.

 

Income

Median and mean inflation-adjusted before-tax family income moved modestly in different directions between 2016 and 2019 (table 1).⁷ Median income rose 5 percent, from $56,000 to $58,600. Mean income decreased 3 percent, from $109,300 to $106,500. The decrease in mean income was mainly driven by families in the top 1 percent of the income distribution (for details, see box 1, "The Data Used in This Article"). Altogether, these changes are consistent with a slight narrowing of the income distribution over this period.⁸

Table 1. Before-tax median and mean family income, by selected characteristics of families, 2016 and 2019 surveys

The income distribution widened considerably over the 2010–16 period, as a sharp 18 percent increase in mean income far outpaced a more moderate 4 percent growth in median income. Further, the changes over the most recent survey period suggest that, despite an initial bounceback in income growth between 2013 and 2016 to the robust pre-crisis rates, the United States has yet to return to the general pattern of sustained increases in income between surveys that had dated back to the early 1990s.⁹

Some predictable patterns in income levels across demographic groups are observed in the 2019 SCF, and those patterns are largely consistent with previous surveys.¹⁰ Across age groups, income shows a life-cycle pattern, rising to a peak for families in which the reference person is in one of the middle age groups and then declining for those in which the reference person is older and increasingly likely to be retired.¹¹ Income also shows a strong positive association with education; in particular, income among families in which the reference person has a college degree tends to be substantially higher than for those with less schooling. Mean income among college-educated families in the 2019 SCF was more than twice that of families in any other education group.

Among families in which the survey respondent identifies as White non-Hispanic, income is substantially higher than for all three other race and ethnic groups—Black non-Hispanic, Hispanic, and other or multiple race families.¹² Income is also considerably higher for homeowners and for families living in urban areas than for other families.¹³ Finally, family income is positively correlated with net worth.

Changes in Income by Family Characteristics

With few exceptions, median income displayed broad-based gains between 2016 and 2019 across different types of families, whether grouped by economic characteristics such as usual income, wealth, urbanicity, or homeowner status, or by demographic characteristics such as age, education, or race and ethnicity.¹⁴ However, with respect to mean income, some types of families saw gains, while many others saw losses.

A family's income at a particular time may not be indicative of its "usual" income.¹⁵ A recent spell of unemployment, a bonus from an employer, a capital loss or gain on investments, or other factors may cause income to deviate temporarily from the usual amount. Across the distribution of families grouped by their usual level of income, all quintiles saw increases in median income between 2016 and 2019. While there was little variation across income groups, families in the top decile saw the largest proportional gain.¹⁶ However, with respect to mean income, several income groups saw declines: Although changes in either direction were relatively modest for the bottom four quintiles, the top decile posted a notable 6 percent decrease, consistent with some narrowing of the income distribution between 2016 and 2019.¹⁷

All prime working-age groups (that is, those younger than age 55) saw increases in median and mean income between 2016 and 2019, ranging from 4 to 13 percent. The youngest families saw the largest gains in both median and mean income—13 percent and 9 percent, respectively. Except for the 75 and older age group, which saw a very modest increase in median income, both median and mean income decreased for all groups 55 and older, with mean income dropping 13 percent for the 55 to 64 age group.¹⁸ Notably, these groups all experienced exceptionally large growth in mean income between 2010 and 2016.

Between 2016 and 2019, growth in median income decreased with educational attainment. Families without a high school diploma saw a 9 percent increase in their median income, while families with a college degree saw a 2 percent decrease. Growth in mean income by attainment group exhibited less consistency, with both the most and least educated groups experiencing decreases. Mean income decreased 4 percent among families that did not complete high school and dropped 13 percent among families that completed college. Those with a high school diploma and those with some college saw increases of 5 percent and 10 percent, respectively. These patterns indicate that the gaps in income between families with a college degree and those without one decreased between 2016 and 2019. This change, to some extent, reflects compositional changes among families with a college degree over this period, as there was a decrease in the share of families aged 45 to 64—which, as previously noted, tend to have relatively high income—and a modest increase in the share of families that are retired.¹⁹ That said, families with a college degree experienced relatively robust growth in median and mean income between 2010 and 2016.

Over the 2016–19 period, median income rose for nearly all families grouped by race or ethnicity, with the exception of Hispanic families, which experienced a slight decline.²⁰ With respect to mean income, both Hispanic and White non-Hispanic families saw declines, while Black non-Hispanic families and families that identified as other or multiple races saw gains.²¹ Indeed, despite Black non-Hispanic families and White non-Hispanic families experiencing similarly robust growth in median income, White non-Hispanic families saw the largest drop in mean income of all the groups. That said, this drop comes on the heels of a 24 percent surge in mean income among families in this group between 2010 and 2016.

Reflecting the top-line changes, both homeowners as well as renters and other non-owners experienced median income growth but mean income losses between 2016 and 2019. The median income gap between families living in a metropolitan statistical area (MSA) and those living in a non-MSA widened slightly between 2016 and 2019 as median income grew similarly among families in both types of areas (and the level of median income is lower for those in non-MSAs). However, the mean income gap between the two groups shrank.

In grouping families by percentile of net worth (as measured concurrently in the SCF), both median and mean income rose almost throughout the distribution between 2016 and 2019. The one exception is the mean income of families in the top decile of net worth, which dropped 12 percent, marking the first decline in either median or mean income for that group since 2010.²² Over the 2010–16 period, growth in both median and mean income among families in the top decile of the wealth distribution was exceptionally strong—19 percent and 38 percent, respectively. For families in the lowest quartile of net worth, median income jumped 11 percent to $29,900, and mean income grew 4 percent to $37,900 between 2016 and 2019. Families in the middle quartiles saw growth in median and mean income ranging from 1 to 8 percent over this period.

Net Worth

Median and mean inflation-adjusted net worth—the difference between families' gross assets and their liabilities—increased between 2016 and 2019 (table 2). Specifically, the median net worth of all families rose 18 percent to $121,700, and the mean net worth increased a modest 2 percent to $748,800. These patterns continued growth seen over the 2013–16 period, although the gain in mean net worth over that period was a much larger 25 percent. Median and mean net worth over the 2010–13 period were effectively stagnant despite the recovery in house and other asset prices that followed the large declines in the 2007–10 period amid the Great Recession.²³

Table 2. Family median and mean net worth, by selected characteristics of families, 2016 and 2019 surveys

Net worth tends to rise systematically with income, as higher-income families have higher levels of saving, which results in a feedback effect of higher income from the accumulated assets.²⁴ In addition, net worth generally increases with age until it plateaus (or decreases modestly) for the oldest age groups as they retire, a pattern reflecting the fact that individuals usually save for retirement throughout their working career and then spend those savings in retirement. Finally, net worth exhibits strong differentials across groups defined in terms of education, racial or ethnic background, urbanicity, and housing status; these differentials generally mirror those for income, but the wealth differences are larger.

Changes in Net Worth by Family Characteristics

Families with higher levels of usual income reported greater levels of net worth, but changes in net worth varied substantially across the usual income distribution. On the one hand, median and mean net worth rose—in most instances, considerably—between 2016 and 2019 for the bottom two quintiles. Those in the lowest usual income quintile saw a large jump in median net worth (37 percent to $9,800) and a similar increase in mean net worth (38 percent to $114,100). Further, families in the fourth quintile rebounded from relatively modest gains in median and mean net worth between 2013 and 2016. On the other hand, families in the third quintile of usual income saw declines in both median and mean net worth (1 percent and 9 percent, respectively). Those toward the top of the distribution experienced the largest declines in median net worth and essentially stable mean net worth.²⁵

 

Nearly all age groups experienced increases in median net worth between 2016 and 2019, while mean net worth decreased for several age groups. With respect to both median and mean net worth, families aged 75 and older experienced the largest declines (10 percent and 14 percent, respectively), and families aged 35 to 44 experienced the largest increases (44 percent and 42 percent, respectively).²⁶ Growth in net housing wealth contributed to increases among those aged 35 to 44, while the drawing down of financial assets factored into decreases among the oldest group. As with income, prime working-age families generally experienced substantial gains in net worth, with the only exception being a modest decline in mean net worth among families younger than age 35.

Between 2016 and 2019, there was considerable variation in the extent to which median and mean net worth changed by education group, ranging from an 18 percent decrease to a 26 percent increase. Both measures of net worth continued to increase among families in which the reference person had either a high school diploma or some college. However, although families without a high school diploma saw the largest gains in both median and mean net worth between 2013 and 2016, they saw the largest drops between 2016 and 2019 (16 percent and 18 percent, respectively); nonetheless, both measures remained above their 2013 levels. Among families with a college degree, declines in median and mean net worth were more modest, leaving median net worth essentially unchanged from its 2013 level and mean net worth well above its 2013 level. As with income, these patterns generally point to a shrinking gap in net worth between families with and without a college degree between 2016 and 2019, with this gap nonetheless remaining substantial.²⁷

From 2016 to 2019, median net worth increased for all types of families grouped by race or ethnicity. Further, large gains among Black non-Hispanic and Hispanic families seen over the previous survey interval continued. Non-White and Hispanic families experienced larger proportional growth in median net worth than White non-Hispanic families during the 2016–19 period; nonetheless, the typical White non-Hispanic family still had more than double the amount of wealth than the typical family in any other racial or ethnic group in 2019. With respect to mean net worth, only families identified as other or multiple race saw an increase.²⁸ While two of the remaining groups experienced relatively modest decreases, mean net worth among Hispanic families dropped 19 percent.²⁹

The median net worth of homeowners grew modestly between 2016 and 2019, while their mean net worth essentially did not change. Renters or other non-homeowners, whose average levels of net worth are far exceeded by those of homeowners, experienced an 18 percent jump in median net worth and a 1 percent decline in mean net worth.

Between 2016 and 2019, families living in MSAs experienced a 20 percent increase in median net worth and a slight 1 percent increase in mean net worth, while families in other areas saw a modest 3 percent dip in median net worth but an 11 percent increase in mean net worth.

Median and mean net worth rose for much of the net worth distribution, with the bottom quartile's 5 percent loss in mean net worth representing the only decline. For the lowest quartile, median net worth was $100 in 2016 and $300 in 2019, and mean net worth was negative $13,000 in 2016 and negative $13,500 in 2019. The wealth portfolio of families in the middle of the net worth distribution is dominated by housing, and, as such, changes in their wealth between surveys tend to reflect the extent to which growth in house prices surpassed inflation.³⁰ Indeed, both the second and third quartiles saw growth in their median and mean net worth between 2016 and 2019, with the second quartile experiencing particularly large gains in both measures (36 percent and 22 percent, respectively). Finally, following outsized increases in both median and mean net worth between 2010 and 2016, families near the top of the wealth distribution saw very little change in net worth; nonetheless, median and mean net worth for that group were about four and eight times the levels of the next decile, respectively—evidence that wealth remains concentrated among these families.

Assets

In 2019, virtually all families owned some type of asset, little changed from 2016 (table 3). Conditional on holding at least one asset, median total asset holdings rose 13 percent, to about $228,000 in 2019. The conditional mean value of total assets rose a modest 2 percent.

Table 3. Holding and values of assets, 2016 and 2019 surveys

Financial Assets

Overall, 98.7 percent of families in 2019 owned at least one financial asset—which includes transaction accounts, certificates of deposit, savings bonds, other bonds, stocks, pooled investment funds, retirement accounts, cash value life insurance, and other managed assets—little changed from 2016.³¹ The conditional median value of all financial assets held by families rose 3 percent to $25,700 in 2019. The conditional mean value increased a modest 1 percent to $363,700. The large difference between median and mean values reflects the highly disproportionate share of financial assets held by some households.

Transaction accounts—which include checking, savings, money market, call accounts, and prepaid debit cards—remained the most commonly held type of financial asset in 2019, with an ownership rate of more than 98 percent.³² The conditional median value of transaction accounts rose 11 percent between 2016 and 2019, to $5,300. The mean value of transaction accounts in 2019 was about $42,000, down slightly from 2016.

The second-most commonly held type of financial asset continued to be retirement accounts—including individual retirement accounts, Keogh accounts, and certain employer-sponsored accounts, such as 401(k), 403(b), and thrift savings accounts. About half of families owned such accounts in 2019. The conditional median value of retirement accounts increased a modest 2 percent between 2016 and 2019, to $65,000, while the conditional mean value rose 5 percent to $255,200 in 2019.³³

Direct ownership of stocks increased slightly between 2016 and 2019, to more than 15 percent of families. Stocks may also be indirectly held in pooled investment funds and other managed assets, which were held by 9 percent and nearly 6 percent of families, respectively (for more on families' stock holdings, see box 5, "Direct and Indirect Holdings of Publicly Traded Stock"). Rates of ownership of almost all other types of financial assets are below 10 percent, the exception being cash value life insurance at 19 percent.

Nonfinancial Assets

Ownership of nonfinancial assets—including vehicles, residential or nonresidential property, and business equity—remained high in 2019 at about 91 percent. Ownership of primary residences increased slightly to almost 65 percent in 2019.³⁴ Reported values of primary residences rose markedly between 2016 and 2019. The conditional median value increased 14 percent to $225,000. In addition to ownership of primary residences, about 13 percent of families in 2019 reported owning other residential property, such as second homes or time shares.

The most commonly held type of nonfinancial asset in 2019 was vehicles, which includes cars, vans, sport utility vehicles (SUV), trucks, motor homes, recreational vehicles, motorcycles, boats, airplanes, and helicopters. Between 2016 and 2019, the fraction of families owning a vehicle held steady at about 85 percent, but the conditional median value declined 7 percent, to about $17,000.³⁵

Ownership of business equity was about 13 percent in 2019.³⁶ The conditional median value was more than $89,000, and the conditional mean value was more than $1.2 million. The wide difference between the median and mean values reflects the small fraction of privately held businesses with very high valuations. Ownership of equity in nonresidential property was 6.7 percent in 2019, and conditional median and mean values of equity in nonresidential property were about $70,000 and $375,000, respectively.

Debt, Debt Burden, and Credit Market Experiences

The share of families holding any type of debt held steady between 2016 and 2019, at roughly 77 percent (table 4).³⁷ The conditional median value of debt increased 2 percent to nearly $65,000, and the conditional mean value increased 7 percent to more than $140,000.

Table 4. Holding and values of debt items, 2016 and 2019 surveys

Debt Holdings by Type

 

About 42 percent of families in 2019 held debt secured by a primary residence, similar to the percentage in 2016. As observed earlier in table 3, about 65 percent of families in 2019 owned their principal residence. These numbers imply that almost two-thirds of homeowners have home-secured debt, while more than one-third of homeowners own their home free of debt.

The conditional median and mean values of home-secured debt increased between 2016 and 2019, closely tracking the rise in home values shown in table 3. The conditional median value of home-secured debt increased 14 percent, from $118,100 to $134,800, and the conditional mean value of home-secured debt increased 8 percent.

Credit card debt continued to be the most widely held type of debt in 2019, with more than 45 percent of families reporting a credit card balance after their last payment. Of those with credit card debt, the median family owed $2,700 in 2019, up 10 percent from 2016. About 37 percent of families held vehicle loans, and nearly 22 percent of families had education loans in 2019. Conditional median and mean balances on vehicle loans declined between 2016 and 2019, as did vehicle values shown in table 3. In contrast, conditional median and mean balances on education loans increased much more between surveys—to $22,300 and $40,300, respectively—representing a continuation of the long-term trend of rising education debt. (For more on education debt, see box 9, "Education Debt.")

In 2019, 10.5 percent of families held other installment loans, down slightly from 2016. These loans are often associated with purchases of furniture, appliances, and other durable goods, though the category also includes medical debt.

Debt Burden

The ability of individual families to service their loans is a function of many factors, including the level of their loan payments and the income and assets they have available to meet those payments. In planning their borrowing, families make assumptions about their future ability to repay their loans. Problems may occur when events turn out to be contrary to those assumptions. If economic shocks are sufficiently large and prevalent, a broad pattern of default, restraint in spending, and financial distress in the wider economy might ensue.

The SCF data can be used to construct three measures of debt burdens: leverage ratios, debt-to-income ratios, and payment-to-income ratios. Leverage ratios compare debts to assets, debt-to-income ratios compare debt to income, and payment-to-income ratios compare payments made on debt relative to income. All three ratios can be constructed either in aggregate or as a median for debtors.³⁸

Most of these ratios increased slightly between 2016 and 2019, implying families faced somewhat higher debt burdens, after having declined from 2010 to 2016 (table 5).³⁹ However, these ratios remain below their levels just before the 2007–08 financial crisis. In 2019, the median leverage ratio for debtors was slightly below its 2007 level at 33.9 percent; median debt-to-income and payment-to-income ratios for debtors in 2019—at 95.7 percent and 15.3 percent, respectively—were well below 2007 levels.⁴⁰

Table 5. Debt burdens and credit market experiences, 2007–19 surveys

Another important indicator of potential financial distress is the proportion of families with unusually large debt burdens. In 2019, 7.4 percent of debtors had payment-to-income ratios greater than 40 percent. This value represents a slight increase from 2016, when 7.0 percent of debtors had payment-to-income ratios greater than 40 percent. However, the level in 2019 is substantially lower than the level observed in 2007.

Credit Market Experiences

The SCF also collects various measures of respondents' recent experiences with credit markets, such as information on credit applications and payment behavior. The SCF asks several questions that attempt to capture whether families are credit constrained, two of which are (1) whether the family was turned down for credit over the past 12 months, and (2) whether the family decided not to apply for credit during the past 12 months for fear of being turned down.⁴¹ In 2019, almost 11 percent of families responded "yes" to the first question, and about 13 percent responded "yes" to the second.

SCF families' capacity to stay current on their financial obligations improved between 2016 and 2019, continuing a trend since 2010 (table 5). Families that have any debt at the time of their interview are asked whether they were behind on any of their loan payments in the preceding year. In 2019, 12.3 percent of families reported being late on payments, down from 13.5 percent in 2016 and 20.8 percent in 2007. The percentage of families that reported being 60 days late or more declined from 5.8 percent in 2016 to 4.6 percent in 2019, substantially below the recent peak of 8.1 percent in 2010.

The SCF asks respondents if they have taken out a payday loan in the past year. Payday loans are unsecured loans that are typically small and short term, and they generally carry interest rates far exceeding those for conventional forms of credit. In 2019, about 3 percent of families reported taking out a payday loan, similar to 2016.

Additional measures of financial distress are whether families have declared bankruptcy or experienced a foreclosure. In 2019, 2 percent of families reported having declared bankruptcy in the past five years, and about 1 percent of families reported having foreclosure proceedings brought against properties they owned in the past five years.⁴²

Finally, between 2016 and 2019, the share of families that used a credit card exclusively for convenience (that is, they did not carry a balance) essentially did not change, remaining several percentage points below the 2010 and 2013 shares.

Appendix: Survey Procedures and Statistical Measures

The 2019 data used here are derived from the final internal version of the survey information. Data from this survey, suitably altered to protect the privacy of respondents, along with additional tabulations of data from the surveys beginning with 1989, are expected to be available in September 2020 on the Federal Reserve Board's website.⁴³

As part of the general reconciliations required for this article, the survey data were compared with many external estimates. One particularly important comparison is between the Survey of Consumer Finances (SCF) and the Federal Reserve's Statistical Release Z.1, "Financial Accounts of the United States," for the household sector.⁴⁴ This comparison suggests that when the definitions of the variables in the two sources are adjusted to a common conceptual basis, the estimates of totals in the two systems tend to be close. The data series in the SCF and in Statistical Release Z.1 usually show very similar growth rates.⁴⁵ In general, the median values for income and net worth in the SCF are most comparable with values in other household surveys because of the special design of the SCF sample.⁴⁶

Adjustment for Inflation

In this article, unless otherwise specified, all dollar amounts from the SCF are adjusted to 2019 dollars using the "current methods" version of the consumer price index (CPI) for all urban consumers. In an ongoing effort to improve accuracy, the U.S. Bureau of Labor Statistics has introduced several revisions to its CPI methodology. The current-methods index attempts to extend these changes to earlier years to obtain a series as consistent as possible with current practices in the official CPI.⁴⁷ To adjust assets and liabilities to 2019 dollars and to adjust family income for the preceding calendar year to 2019, the figures given in the following table were applied:

Definition of "Family" in the Survey of Consumer Finances

The definition of "family" used throughout this article differs from that typically used in other government studies. In the SCF, a household unit is divided into a primary economic unit (PEU)—the family—and everyone else in the household. The PEU is intended to be the economically dominant single person or couple (whether married or living together as partners) and all other persons in the household who are financially interdependent with that economically dominant person or couple.

This report also designates a reference person within the PEU, not to convey a judgment about how an individual family is structured but as a means of organizing the data consistently. For example, the age and educational classifications ascribed to families throughout this report describe the age and education of the reference person. If a couple is economically dominant in the PEU, the reference person is the male in a mixed-sex couple or the older person in a same-sex couple. If a single person is economically dominant, that person is designated as the family reference person in this report. Note that the term "reference person" is a new descriptor as of the 2019 survey, replacing the outdated "household head" terminology used in previous surveys.

Asset and Liability Categories in the Survey of Consumer Finances

The specific concepts of asset and liability categories in the SCF are necessarily tied to the survey question wording and associated field interviewer instructions, both of which can be found in the SCF codebook for the year(s) in question.⁴⁸ What follows is a general exposition of the asset and liability categories reported in the tables.

Transaction accounts include checking, savings, and money market deposit accounts; money market funds (MMF); call or cash accounts at brokerages; and prepaid debit cards. Call accounts include those that hold money received from the sale of securities until the money is reinvested. The savings account category includes a relatively small number of tax-preferred accounts such as medical or health savings accounts and Coverdell or 529 education accounts. Prepaid debit cards, collected in the SCF for the first time in 2016, include reloadable prepaid debit cards and government benefit cards.

Certificates of deposit are accounts held for a set period that must be cashed or renewed at the maturity date. Savings bonds include only U.S. government issues; recent series include EE, HH, and I, and older bonds may be series E and H. Other bonds include only those held directly (not part of a managed investment account or bond fund) and include corporate and mortgage-backed bonds; federal, state, and local government bonds; and foreign bonds. Stocks include publicly traded stocks that are directly held—that is, corporate equities not held as part of a managed investment account or mutual fund.

Pooled investment funds include stock funds, tax-free bond funds, government bond funds, other bond funds, and any combinations thereof but exclude MMFs and indirectly held mutual funds. These funds include all other types of directly held pooled investments, such as traditional open-end and closed-end mutual funds, exchange-traded funds, real estate investment trusts, and hedge funds.

Retirement accounts include individual retirement accounts, Keogh accounts, and certain employer-sponsored accounts, such as 401(k), 403(b), and thrift savings accounts from current or past jobs; other current job plans from which loans or withdrawals can be made; and accounts from past jobs from which the family expects to receive the account balance in the future. This definition of employer-sponsored plans is intended to confine the analysis to accounts that are portable across jobs and for which families will ultimately have the option to withdraw the balance. Usually, such accounts may be invested in virtually any asset, including stocks, bonds, pooled investment funds, options, and real estate. In principle, employer-sponsored plans may be invested in a similarly broad way, but, in practice, a person's choices for investment are sometimes limited to a narrower set of assets.⁴⁹

Cash value life insurance is the current (nonzero) value of any life insurance policies with a cash value that can be withdrawn. The survey measures the value of such policies according to their current cash value, not their death benefit. In this article, the cash value is included as an asset only when the cash value at the time of the interview was nonzero. This designation excludes term life insurance policies, which only provide a death benefit.

Other managed assets include personal annuities and trusts with an equity interest and managed investment accounts. Annuities may be those in which the family has an equity interest in the asset or in which the family possesses an entitlement only to a stream of income. The wealth figures in this article include only the annuities in which the family has an equity interest.⁵⁰ The trusts or managed investment accounts included in other managed assets are those in which families have an equity interest and for which components were not separately reported. Typically, such accounts are those in which the ownership is complicated or the management is undertaken by a professional.⁵¹

Other financial assets include oil and gas leases, futures contracts, royalties, proceeds from lawsuits or estates in settlement, and loans made to others. One specific financial asset excluded from this category and any other is employment-related stock options. Because such options are typically not publicly traded or their execution is otherwise constrained, their value is uncertain until the exercise date; until then, meaningful valuation would require complex assumptions about the future behavior of stock prices.

Vehicles include cars, vans, sport utility vehicles (SUV), trucks, motor homes, recreational vehicles, motorcycles, boats, airplanes, and helicopters.⁵² Primary residences include mobile homes and their sites, the parts of farms and ranches not used for farming or ranching business, condominiums, cooperatives, townhouses, other single-family homes, and other permanent dwellings. Other residential property includes second homes, time shares, one- to four-family rental properties, and other types of residential properties. It also includes outstanding balances on loans that the family may have made to finance the sale of properties the family previously owned and that are still owed to the family.

Nonresidential real estate includes the following types of properties unless they are owned through a business: commercial property, rental property with five or more units, farmland and ranch land, undeveloped land, and all other types of nonresidential real estate. Most often, nonresidential real estate properties are functionally more like a business than a residential property. They may have several owners, they are typically worth a considerable amount, and they often carry large mortgages, which appear to be paid from the revenues from the property, not the family's other income. As in the case of privately owned businesses, the value of the property in this analysis is taken to be the net value.

Business equity includes net worth in the following forms of business: sole proprietorships, limited partnerships, other types of partnerships, S corporations and other types of corporations that are not publicly traded, limited liability companies, and other types of private businesses. If the family lived on a farm or ranch used at least in part for agricultural business, then the value of that part, net of the corresponding share of associated debts, is included with other business assets. In the survey, self-employment status and business ownership are independently determined.⁵³

Debt secured by residential property consists of first- and junior-lien mortgages and home equity lines of credit (HELOC) secured by the primary residence. For purposes of this article, first- and junior-lien mortgages consist only of closed-end loans—that is, loans typically with a one-time extension of credit, a set frequency of repayments, and a required repayment size that may be fixed or vary over time in accordance with a pre-specified agreement or with changes in a given market interest rate.⁵⁴ As a type of open-ended credit, HELOCs typically allow credit extensions at the borrower's discretion subject to a prearranged limit and allow repayments at the borrower's discretion subject to a prearranged minimum size and frequency.

Lines of credit not secured by residential property are any lines of credit except HELOCs and borrowing on credit cards.

The term "installment loan" describes closed-end consumer loans—that is, loans that typically have fixed payments and a fixed term. The most common examples are education loans, automobile loans, and loans for furniture, appliances, and other durable goods. Other installment loans include all closed-end consumer loans that are not for education or a vehicle—that is, loans that typically have fixed payments and a fixed term. Examples include loans for furniture, appliances, and other durable goods.

Credit card balances consist of balances on bank-type cards (such as Visa, MasterCard, and Discover as well as Optima and other American Express cards that routinely allow holders to carry a balance), store cards or charge accounts, care cards, gasoline company cards, so-called travel and entertainment cards (such as American Express cards that do not routinely allow holders to carry a balance and Diners Club), other credit cards, and revolving store accounts that are not tied to a credit card. Balances exclude purchases made after paying the most recent bill.

The "other" debt category comprises loans on cash value life insurance policies, loans against pension accounts, borrowing on margin accounts, and a miscellaneous category largely composed of personal loans not explicitly categorized elsewhere.

Finally, the SCF measure of liabilities excludes debt owed by family-owned businesses and debt owed on nonresidential real estate; in this article, such debt is netted against the corresponding assets.

Measures of Debt Burden and Credit Market Experiences in the Survey of Consumer Finances

The SCF includes several questions designed to capture information about respondents' debt burdens and interactions with credit markets. The specific concepts addressed in the SCF are necessarily tied to the survey question wording and associated field interviewer instructions, which can be found in the SCF codebook for the year(s) in question.⁵⁵ What follows is a general exposition of the debt burden and credit market experience measures reported in the tables.

Leverage ratios compare the total of all debts to the total of all assets. The aggregate version of this measure is the sum of all debts for all SCF respondents, divided by the sum of all assets for SCF respondents. The median for debtors is the median of each individual family's leverage ratio and is calculated for those with positive values of total debt only.

The aggregate debt-to-income ratio is the sum of liabilities for all SCF respondents, divided by the total income for all SCF respondents. The median for debtors is the 50th percentile of an individual family's debt-to-income ratios and is calculated for those with positive values of total debt only.

Payment-to-income ratios measure total debt payments relative to total income.⁵⁶ The aggregate version of this measure is the sum of all debt payments for all SCF respondents, divided by total income for all SCF respondents. The median for debtors is the 50th percentile of an individual family's payment-to-income ratios and is calculated for those with positive values of total debt only.

The aggregate measure of the payment-to-income ratio referenced in this article can differ from other published measures that are conceptually similar, such as the debt service ratio, for several reasons.⁵⁷ First, the debt payments included in each measure are different. The aggregate-level measure includes only debts originated by depositories, finance companies, and other financial institutions, whereas the survey includes, in principle, debts from all sources. Second, the aggregate-level measure uses an estimate of disposable personal income from the national income and product accounts for the period concurrent with the estimated payments as the denominator of the ratio, whereas the survey measure uses total before-tax income reported by survey families for the preceding year; the differences in these two income measures are complex. Third, the payments in the aggregate-level measure are estimated using a formula that entails many assumptions about minimum payments and the distribution of loan terms at any given time; the survey measure of payments is directly asked of the survey respondents but may also include payments of taxes and insurance on real estate loans. Fourth, because the survey measures of payments and income are based on the responses of a sample of respondents, they may be affected by both sampling error and various types of response errors. As mentioned earlier in this article, the survey income measure tracks the most comparable measure of income in the U.S. Census Bureau's CPS.

The SCF asks multiple questions intended to capture whether families are credit constrained, which is broadly defined as having difficulty accessing credit.⁵⁸ One question asks the respondent whether the respondent or the spouse or partner applied for particular types of credit in the past year. Among those that answer in the affirmative, a follow-up question asks the respondent whether a lender declined an application for credit or provided less credit than was sought at any point in the past year. Among those that answer in the negative, a follow-up question probes the rationale behind the decision not to apply for credit in the past year and, among other choices, offers "you did not think you would get approved" as a possibility. A combination of these questions is used to measure overall credit constraints.

Delinquency on debt obligations is captured by asking families that have any debt at the time of their interview whether they have been behind in any of their loan payments in the preceding year. The survey asks if respondents have been behind at all and if they have been behind in payments for 60 days or more.

Payday loans are defined as loans that are meant to be repaid in full out of the respondent's next paycheck; they are unsecured loans that are typically small, short term, and carry above-average interest rates.

Bankruptcy behavior over the past five years is based on a series of retrospective questions that ask whether the respondent or the partner or spouse has ever declared bankruptcy and, if so, the most recent year.

Foreclosure experience over the past five years is based on a series of retrospective questions that ask whether the respondent or the partner or spouse has ever had a foreclosure proceeding brought against an owned property and, if so, the most recent year.

Finally, convenience use of credit cards is determined using questions on whether a respondent had positive balances after the most recent payment for bank-type cards (such as Visa, MasterCard, and Discover as well as Optima and other American Express cards that routinely allow holders to carry a balance), store cards, gasoline company cards, and other credit cards.

Percentiles of the Distributions of Income and Net Worth

Throughout this article, references are made to various percentile groups of the distributions of income or net worth. For a given characteristic, a percentile can be used to define a family's rank relative to other families. For example, the 10th percentile of the distribution of usual income is the amount of income received by a family for which less than 10 percent of other families have lower incomes and 90 percent have higher incomes. The percentiles of the distributions of income and net worth used to define the income and net worth groups in tables 1 and 2 in the article are given in the following table:

The groups that are created when a distribution is divided at every 10th percentile are commonly referred to as deciles. Similarly, when a distribution is divided at every 20th (25th) percentile, the groups are known as quintiles (quartiles). Families in the first income decile, for example, are those with income below the 10th percentile.

Racial and Ethnic Identification

In this article, the race and ethnicity of a family in the SCF are classified according to the self-identification of that family's original respondent to the SCF interview. For greater comparability with earlier SCF data, the data reported in this article group respondents into four classifications based on their responses to the racial identification question: White non-Hispanic, Black non-Hispanic, Hispanic or Latino, and other or multiple race. The "other or multiple race" classification consists of respondents identifying as Asian, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, other race, and all respondents reporting more than one racial identification.⁵⁹

The questions underlying the method of classification used in the survey were changed in both 1998 and 2004. Starting in 1998, SCF respondents were allowed to report more than one racial identification; in surveys before then, only one response was recorded. For maximum comparability with earlier data, respondents reporting multiple racial identifications were asked to report their strongest racial identification first. In data reported in this article, respondents reporting multiple racial identifications in the surveys starting with 1998 are classified as "other or multiple race." In the 2019 SCF, 6.8 percent of respondents reported more than one racial identification, up from 6.4 percent in 2016, 6.1 percent in 2013, 5.4 percent in 2007, and 2.3 percent in 2004. The public release data set includes the racial and ethnic identification variables, enabling the construction of alternative classifications.

Beginning with the 2004 survey, the question on racial identification is preceded by a question on whether respondents consider themselves to be Hispanic or Latino in culture or origin; previously, such ethnic identification was captured only to the extent that it was reported as a response to the question on racial identification. The classifications in this article ignore the information on ethnic identification available in the surveys since 2004, again for greater comparability with earlier SCF data. Of those who responded affirmatively to the question on Hispanic or Latino identification in 2019, 90.2 percent also reported "Hispanic or Latino" as one of their racial identifications, and 82.2 percent reported it as their primary racial identification. Because the question on Hispanic or Latino ethnicity precedes the one on racial identification in the surveys from 2004 through 2019, the answer to the second of these two questions may have been influenced by the answer to the first.⁶⁰

This all said, evolution of the racial and ethnic composition of the survey population represents important context for interpreting statistics describing the experiences of families by race or ethnicity over time. The next table displays the share of the SCF population each racial or ethnic group has represented in each survey since 2004 using current survey classifications:

Classifications of Educational Attainment

In this article, the educational attainment of a family refers to the highest degree obtained by the household reference person. Beginning with the 2016 survey, the SCF modified its educational attainment question to align the SCF more closely with other household surveys, including the Census and CPS. For surveys before 2016, respondents were asked to list the highest grade of school or year of college completed, and follow-up questions asked respondents about the type of degree obtained. Starting with the 2016 survey, the first educational attainment question asks about the highest degree obtained, and follow-up questions ask respondents who report a high school degree whether it was obtained by getting a General Education Development certificate or completing another equivalency program. Follow-up questions also ask respondents who report some college about the number of years of college. It is possible this change may have prompted changes in reporting, although the educational attainment of SCF families was similar to CPS families in both 2013 and 2016.⁶¹

Similar to race and ethnicity, evolution of the educational composition of the survey population represents important context for interpreting statistics describing the experiences of families by educational attainment over time. The next table displays the share of the SCF population each education group has represented in each survey since 2004 using current survey classifications:

Age Classifications

In this article, the age of a family refers to the age of the household reference person. Similar to race and ethnicity, as well as educational attainment, evolution of the age composition of the survey population represents important context for interpreting statistics describing the experiences of families by age over time. The next table displays the share of the SCF population each age group has represented in each survey since 2004 using current survey classifications:

The Sampling Techniques

The survey is expected to provide a core set of data on family income, assets, and liabilities. The major aspects of the sample design that address this requirement have been largely constant since 1989. The SCF combines two techniques for random sampling. First, a standard multistage area-probability sample (a geographically based random sample) is selected to provide good coverage of characteristics, such as homeownership, that are broadly distributed in the population.

Second, a supplemental sample is selected to disproportionately include wealthy families, which hold a relatively large share of such thinly held assets as noncorporate businesses and tax-exempt bonds. Called the "list sample," this group is drawn from a list of statistical records derived from tax returns. These records are used under strict rules governing confidentiality, the rights of potential respondents to refuse participation in the survey, and the types of information that can be made available. Persons listed by Forbes as being among the wealthiest 400 people in the United States are excluded from sampling.⁶²

Of the 5,783 interviews completed for the 2019 SCF, 4,291 were from the area-probability sample, and 1,492 were from the list sample; for 2016, 4,754 were from the area-probability sample, and 1,500 were from the list sample. The number of families represented in the surveys considered in this article is given by the following table:

Overall population growth between 2016 and 2019 was 1.5 percent, according to figures from the U.S. Census Bureau, down from the 2.2 percent growth rate between 2013 and 2016. Also according to U.S. Census Bureau estimates, the number of households increased 2.1 percent between 2016 and 2019—well below the rate of household formation between 2013 and 2016, which was 2.9 percent. With the population growing at a slightly slower rate than household formation, the average number of persons per household barely changed, from 2.56 people in 2016 to 2.55 in 2019.

The Interviews

Although questions have been modified and new questions added over time, the core of the survey questionnaire has changed in only minor ways since 1989. Changes to the questionnaire generally include instances in which the structure was altered to accommodate changes in financial behaviors; changes in types of financial arrangements available to families, including those with businesses that are not publicly traded; and changes in regulations covering data collection. In 2016, interview sections on educational attainment, education loans, payment methods, and financial institutions were revised, and additional questions addressing financial literacy among respondents, parental educational attainment, and decisions under hypothetical financial situations have also been included.⁶³ For all changes, every effort has been made to ensure the maximum degree of comparability of the data over time. Except where noted in the article, the data are highly comparable over time.

The generosity of families in giving their time for interviews has been crucial to the ongoing success of the SCF. In the 2019 SCF, the median interview length was about 100 minutes. However, in some particularly complicated cases, the amount of time needed was substantially more than three hours. The role of the interviewers in this effort is also critical. Without their dedication and perseverance, the survey would not be possible.

The SCF interviews were conducted largely between the months of May and December in each survey year, with a small fraction of interviews conducted in the first four months of the next calendar year, by NORC, a social science and survey research organization at the University of Chicago. The majority of interviews were obtained in person, although interviewers were allowed to conduct telephone interviews if that was more convenient for the respondent. Each interviewer used a program running on a laptop computer to administer the survey and collect the data.

The use of computer-assisted personal interviewing has the great advantage of enforcing systematic collection of data across all cases. The computer program developed to collect the data for the SCF was tailored to allow the collection of partial information in the form of ranges whenever a respondent either did not know or did not want to reveal an exact dollar figure.

For the 2019 SCF, about 200 area-probability interviews were conducted via a mixed mode strategy of web and in-person or telephone interviews. These cases were a test of the feasibility of offering the web as a potential mode for completing the SCF. Potential respondents were provided a personalized link to obtain access to the sections of the survey to complete via the web, with a follow-up in-person or telephone interview to complete the remainder of the interview. The questions for the web portion were nearly identical to those asked in the regular in-person or telephone interview, aside from formatting changes necessary for the web instrument.

The response rate in the area-probability sample is more than double that in the list sample. In 2019, about 60 percent of households selected for the area-probability sample actually completed interviews, down from 65 percent in 2016. The overall response rate in the list sample did not change in 2019 at about one-third; in the part of the list sample likely containing the wealthiest families, the response rate was only about one-half that level.

Weighting

To provide a measure of the frequency with which families similar to the sample families could be expected to be found in the population of all families, an analysis weight is computed for each case, accounting for both the systematic properties of the sample design and differential patterns of nonresponse. The SCF response rates are low by the standards of some other major government surveys, and analysis of the data confirms that the tendency to refuse participation is highly correlated with net worth. However, unlike other surveys, which almost certainly also have differential nonresponse by wealthy households, the SCF has the means to adjust for such nonresponse. A major part of SCF research is devoted to the evaluation of nonresponse and adjustments for nonresponse in the analysis weights of the survey.⁶⁴

Sources of Error

Errors may be introduced into survey results at many stages. Sampling error—the variability expected in estimates based on a sample instead of a census—is a particularly important source of error. Such error can be reduced by either increasing the size of a sample or, as is done in the SCF, designing the sample to reduce important sources of variability. Sampling error can be estimated, and for this article we use replication methods to do so.

Replication methods draw samples, called replicates, from the set of actual respondents in a way that incorporates the important dimensions of the original sample design. In the SCF, weights were computed for all of the cases in each of the replicates.⁶⁵ Every value for which standard errors are reported in this article is a weighted statistic estimated using the replicate samples. To estimate the overall standard error, a measure of the variability of these estimates is combined with a measure of the variability because of imputation for missing data.

Other errors include those that interviewers may introduce by failing to follow the survey protocol or misunderstanding a respondent's answers. SCF interviewers are given lengthy, project-specific training and ongoing coaching to minimize such problems. Respondents may introduce error by interpreting a question in a sense different from that intended by the survey. For the SCF, extensive pretesting of questions and thorough review of the data tend to reduce this source of error.

Nonresponse—either complete nonresponse to the survey or nonresponse to selected items within the survey—may be another important source of error. As noted in more detail previously, the SCF uses weighting to adjust for differential nonresponse to the survey. To address missing information on individual questions within the interview, the SCF uses statistical methods to impute missing data; the technique makes multiple estimates of missing data to allow for an estimate of the uncertainty attributable to this type of nonresponse.

Footnotes