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. Author manuscript; available in PMC: 2021 Dec 8.
Published in final edited form as: J Marriage Fam. 2020 Nov 26;83(3):667–682. doi: 10.1111/jomf.12742

Net Worth Poverty in Child Households by Race and Ethnicity, 1989–2019

Christina Gibson-Davis 1, Lisa A Keister 2, Lisa A Gennetian 3
PMCID: PMC8653971  NIHMSID: NIHMS1751453  PMID: 34887593

Abstract

Objective:

This study is the first to examine net worth poverty, and its intersection with income poverty, by race and ethnicity among child households in the United States.

Background:

Scholarship on economic scarcity for children has largely concentrated on income deficits and thus leaves open important questions about wealth deficits.

Method:

Data come from the 1989–2019 waves of the Survey of Consumer Finances, on households with at least one resident child under the age of 18. Net worth poverty is measured as household net worth, defined as total assets minus total debts, that is less than one-fourth of the federal poverty line.

Results:

In 2019, 57% of Black and 50% of Latino child households were net worth poor. The majority of these households were not income poor. Racial and ethnic differences in net worth poverty (unlike those for income poverty) persist even when sociodemographic variation predicting income poverty is controlled for.

Conclusion:

Net worth poverty is so prevalent in the lives of non-White children that, after sociodemographic characteristics are controlled for, Black and Latino child households have about the same probability of not being poor as they do of being net worth poor.

Implications:

A focus on income deprivation alone will overlook the precarious economic conditions related to family net worth and ignore growing disparities by race and ethnicity.

Keywords: children, inequality, poverty, race and ethnicity, wealth

Child poverty rates in the United States are staggeringly high. In 2019, 14% of those under 18—or 10.5 million children—were residing in poverty (Semega et al., 2020). Childhood poverty in the United States is characterized by pronounced racial and ethnic disparities: relative to White children, Black and Latino children are 2.5 to 3 times as likely to be residing in poverty (U.S. Census Bureau, 2020). Racial/ethnic disparities in childhood poverty are only likely to increase with the economic fallout of COVID-19 and the disease’s disproportionate impacts on the financial well-being of Black and Latino households (Parolin & Wimer, 2020).

The future economic resilience of U.S. households with children will likely be shaped by income but also by wealth. Indeed, policy that focuses solely on income enhancement may miss a large fraction of children who may be experiencing economic distress that can have equally unfavorable long-term consequences. Specifically, an income-centric approach to measuring economic well-being ignores the fraction of children who are net worth poor, which refers to households where wealth (total assets minus total debts) is less than one-fourth of the federal poverty line. This threshold measures whether a household has a stock of assets sufficient to meet its basic needs, as defined by the poverty line, for 3 months (Brandolini et al., 2010; Haveman & Wolff, 2004). Wealth influences children’s life chances through its effects on educational attainment, academic achievement, and socioemotional functioning (Conley, 2001; Diemer et al., 2019; Pfeffer, 2018). Given low rates of intergenerational wealth mobility, children who grow up in low-wealth households are likely to be low-wealth adults (Pfeffer & Killewald, 2017), with attendant negative consequences for themselves and their children (Hällsten & Pfeffer, 2017; Yellen, 2016).

The importance of wealth to children’s well-being is particularly concerning given that Black and Latino households have very modest levels of wealth that are substantially lower than that of White households (Wolff, 2018). As has been shown for adults with one resident child under 18 (hereafter, child households) had median net worth of $294 and $3,637, respectively; by contrast, the median net worth for White child households was $47,250 (Percheski & Gibson-Davis, 2020). Racial/ethnic disparities in wealth exceed those in income (Percheski & Gibson-Davis, 2020; Wolff, 2018). Low levels of wealth suggest that many non-White families have insufficient levels of net worth to promote child flourishing (Yellen, 2016).

To date, though, scholarship on economic scarcity for children has largely concentrated on income deficits, leaving open important questions about wealth deficits. In this study, we propose that a more comprehensive picture of poverty and the economic circumstances of children should include measures of net worth poverty (Brandolini et al., 2010; Rothwell et al., 2019). We estimate the presence and contours of net worth poverty by race/ethnicity and describe how net worth poverty intersects with income poverty. Our work provides a way to codify the low levels of wealth for child households at the bottom of the wealth distribution (Gibson-Davis & Percheski, 2018), thus providing a necessary stepping stone toward subsequent examination of how net worth poverty influences children’s development.

Background

Theoretical Mechanisms of Wealth on Family Life and Children’s Well-Being

Wealth is posited to affect the life chances of children through four primary mechanisms informed by economic theory and family science: parental investments, familial stress, subjective financial well-being, and future expectations (Gibson-Davis & Hill, 2020). Through the lens of these conceptual mechanisms, the stock of current and cumulative financial resources can matter for child development above and beyond the effect of income (Williams Shanks, 2007).

Wealth could affect parental investments in children in several ways. Wealth that is liquid can act like income and thus can be used to purchase goods and services that meet the consumption needs of children (Yellen, 2016). The influence of liquid wealth via parental investments would also most likely be seen in the quality of childcare, education, and children’s learning environments at home, and neighborhood and community contexts (Evans & Wachs, 2010; Williams Shanks, 2007). Wealth, whether liquid or nonliquid, can also act as collateral to secure loans and access to credit markets, potentially facilitating investments in children’s higher education, career opportunities, and safer neighborhoods or schools (Gibson-Davis & Hill, 2020).

Wealth may reduce parental and family stress by allowing parents to better manage risk and financial uncertainty and provide a psychological reassurance about the adequacy of parental resources (Barr, 2012; Blank & Barr, 2009; Conger & Elder, 1994). In fact, though untested, the ability of wealth to offer an economic buffer and insurance against income losses and instability suggests that it may as effective at reducing parental stress about finances as predictable flows of income (Gennetian & Shafir, 2015). Nonliquid wealth, such as homeownership, may also decrease stress by stabilizing children’s lives (Leventhal & Newman, 2010). Increases in parental stress leads to less warm and sensitive parenting, which in turn is associated with children’s behavior problems (McLoyd, 1998).

Potentially unique to wealth is its influence on parent and child subjective financial well-being (Gibson-Davis & Hill, 2020), the broad set of emotions and attitudes about current and future finances, including future orientation and optimism, risk aversion, and sense of financial control (Vera-Toscano et al., 2006). Some aspects of wealth, such as homeownership or a savings account in mainstream banking, may signal economic security, enhance feelings of self-reliance, and support feelings of social belonging (Sherraden, 1991). As a rung on an economic ladder, higher wealth may increase parents’ future orientation and optimism for intergenerational economic opportunity and mobility (Yeung & Conley, 2008).

Wealth may influence parent and child expectations about future educational attainment and economic success (Shanks & Destin, 2009). Parental expectations can mediate the relationship between wealth and college outcomes (Elliott et al., 2011; Vera-Toscano et al., 2006), and may be a stronger explanatory factor for the effects of wealth on educational attainment than are direct parental investments (Diemer et al., 2019). Wealth may also shape children’s own aspirations for college or other goals (Destin & Oyserman, 2009).

Net Worth and Income Poverty

Children need sufficient levels of parental wealth to flourish (Putnam, 2016; Yellen, 2016). Household wealth is positively associated with years of completed schooling, high school graduation, college attendance, and college completion (Conley, 2001; Doren & Grodsky, 2016; Jez, 2014). Children of the wealthiest parents are more than 40 percentage points more likely to graduate from college than children from the least wealthy families (Pfeffer, 2018). For children under the age of 18, parental wealth is positively correlated with standardized test scores and academic achievement (Friedline et al., 2015; Yeung & Conley, 2008), as well as higher sociability and fewer behavior problems (Diemer et al., 2019; Ream & Gottfried, 2019).

As has been shown for adults (Sherraden, 1991), both wealth and income play important yet independent roles in children’s well-being. Nevertheless, net worth poverty differs from income poverty in in two substantive respects. First, net worth poverty is a measure of the adequacy of the stock of resources owned by a household, whereas income poverty measures the adequacy of a flow of resources. Wealth, as a store of value, therefore represents goods that a household can access to meet unexpected expenses, buffer against income loss, or contend with economic shocks, such as medical emergencies, that can deplete financial reserves (Killewald et al., 2017). When a household has few or no assets, even a minor financial setback can make it difficult to pay for necessities such as food, clothing, and shelter. Net worth poverty might better capture families’ long-term economic health and security than income poverty (Haveman & Wolff, 2005).

Second, net worth poverty is likely to affect family functioning and child well-being through different mechanisms than income poverty (Gibson-Davis & Hill, 2020; Diemer et al., 2019). For one, wealth provides parents with psychological safety nets that hedge against the anxieties of child-rearing (Shapiro, 2004): parents with sufficient resources can purchase a home in a safe neighborhood, accumulate savings that provide an economic and psychological buffer against unexpected expenses, and experience fewer concerns about the costs of college. Wealth is also directly tied to homeownership and neighborhood choice, which shape two critical contexts of child development (Leventhal & Newman, 2010). Finally, net worth captures accumulation of household debt, which can operate independently of assets in its association with child well-being (Berger & Houle, 2019; Zhan & Sherraden, 2011). Debt can compromise parenting ability by increasing levels of anxiety and stress (Berger & Houle, 2019), but it may also facilitate parents’ ability to invest in their children’s social or human capital (Zhan & Sherraden, 2011).

Net Worth Poverty in the United States

The most recent evidence available suggests that roughly one-quarter of Americans were net worth poor in the early 2000s, a slight increase over the early 1980s (Brandolini et al., 2010; Caner & Wolff, 2004;Haveman & Wolff, 2004; Haveman & Wolff, 2005). African Americans and Latinos, people with low education levels, and unmarried individuals were more likely than others to be net worth poor (Haveman & Wolff, 2004; Haveman & Wolff, 2005). Notably, these estimates do not extend beyond 2001, when disparities between low- and high-wealth families accelerated.

No extant study or policy brief has estimated levels and trends of net worth poverty for U.S. child households. The most relevant scholarship is provided by Rothwell et al. (2019), who estimated that, in 2013, 63% of U.S. child households were liquid asset poor. By excluding nonliquid assets and debts (such as home equity or mortgage debt), Rothwell et al. chose to focus on the availability of assets to cover short-term consumption. However, in the United States, estimates of liquid asset poverty may not generalize to estimates for net worth poverty because homeownership and associated mortgages are the primary assets and debts held by most households (Wolff, 2017). Blumenthal and Rothwell (2018) found that nearly one-third of Canadian child households were net worth poor; like Rothwell et al., they excluded home equity and debt. Neither study considered racial/ethnic variation, a notable omission given vast racial/ethnic disparities in wealth in the United States (Percheski & Gibson-Davis, 2020; Wolff, 2018).

Although research on net worth poverty has largely overlooked child households, net worth poverty for these households is likely high and increasing. Since the late 1980s, wealth inequality has increased among child households, in part because wealth levels of the least wealthy have dropped dramatically (Gibson-Davis & Percheski, 2018). Black and Latino households experienced most of these declines (Percheski & Gibson-Davis, 2020; Thompson & Suarez, 2015). Recent trends in wealth inequality were exacerbated by the Great Recession (2007–2009), which disproportionately affected the wealth of non-White households (Pfeffer et al., 2013). Since the Great Recession ended, wealth levels for households at the bottom of the distribution have increased slightly (Wolff, 2017). Still, child households in the bottom half of the wealth distribution had lower levels of median wealth in 2016 than in 1989, with Black households experiencing the largest relative declines (Gibson-Davis & Percheski, 2018; Percheski & Gibson-Davis, 2020).

Increasing White-Black disparities in net worth poverty would stand in contrast to trends for child income poverty. Between 1989 and 2019, the childhood income poverty rate fell from 19.6% to 14.4% (U.S. Census Bureau, 2020). The decline in income poverty rates was notably larger for Black and Latino children than for White children, and as a result, racial/ethnic gaps in child income poverty were smaller in 2019 than in 1989 (U.S. Census Bureau, 2020).

Because net worth poverty in child households has largely been absent from the literature, scholars have not readily codified the absence of wealth among households with children, potentially overlooking an important source of economic precarity. Scholars have not had an objective way to define the absence of wealth, relying instead on measures (like lowest quintiles) that may vary depending on the dataset used and cannot be readily compared across datasets. With a definition of net worth poverty in hand, scholars could revisit the associations between wealth and child well-being, and begin to develop a deep knowledge base (similar to that for income poverty) as to how wealth scarcity affects child well-being.

Racial/Ethnic Disparities in Net Worth Poverty

Beginning with the seminal work of Oliver and Shapiro (1995); Shapiro, 2004), scholars have identified a number of factors that contribute to large racial/ethnic gaps in wealth (Darity & Mullen, 2020). Different life trajectories—lower levels of human capital, weaker attachment to the labor market, poorer health, and less stable family structures—can lead to differences in income, which in turn affect asset accumulation (Shapiro et al., 2014). And compared with White adults, Black and Latino adults receive relatively little from their families of origin through in vivo transfers and inheritances (Killewald & Bryan, 2018; Oliver & Shapiro, 1995). Notably, though, these demographic and in vivo transfer differences cannot fully explain wealth disparities. With such factors as age, education, marital status, and presence of inheritances held constant, Black and Latino households still have lower levels of wealth than White households (Percheski & Gibson-Davis, 2020; Thompson & Suarez, 2015).

Racist and discriminatory policies and practices that have long marked the American landscape impede wealth accumulation for people of color (Oliver & Shapiro, 1995). Despite antidiscrimination laws, non-White Americans continue to face racial discrimination in obtaining credit, purchasing homes, and securing loans (Darity & Mullen, 2020). Residential segregation results in Black and Latino households occupying neighborhoods that have lower housing values and higher rates of vacancy and foreclosure (Massey, 2015). Redlining—the designation of minority neighborhoods as financially risky areas, which mortgage lenders and insurance companies often used as grounds for rejecting mortgage applications (Thurston, 2018)—has been replaced by “reverse redlining,” in which lenders target minority households for subprime loans and other predatory loan practices (Rugh & Massey, 2010). Policies associated with mass incarceration and the use of fines and fees in the criminal justice system disproportionately affect Black households and impede wealth accumulation by lowering family incomes, imposing legal costs, and incurring debts (Harris et al., 2010; Sykes & Maroto, 2016). When combined with an American labor market in which many non-Whites occupy jobs that offer lower wages and fewer benefits (Bahn & Cumming, 2020), these policies substantially impede wealth accumulation in Black and Latino households.

Methods

Data

Data are from the Survey of Consumer Finances (SCF), a repeated cross-sectional study of U.S. households conducted by the Federal Reserve triennially (Board of Governors of the Federal Reserve System, 2020). The SCF is considered the best source of wealth data in the United States as it includes both a nationally representative sample of households and a sample of high-income households designed to represent the distribution of net worth (Keister, 2014; Kennickell, 2008). Because the SCF’s unit of analysis is the primary economic unit, which is akin to the U.S. Census’s definition of a household, we refer to our unit of analysis as households. We used 10 SCF waves between 1989 and 2019 and report monetary estimates in 2019 dollars.

The sample was limited to households with at least one resident member under the age of 18 (N = 19,013, 36% of all households). Of those under age 18, 98% were designated as child of the respondent or their spouse’s child; we refer to these household members as children. This definition excludes households with children who reside elsewhere and may include households with resident children over the age of 18. Analyses were divided by the race and ethnicity based on self-report by the respondent: non-Latino White, non-Latino Black, or Latino (the SCF did not include information on multiracial households and other groups such as Native American and Asian were not large enough to conduct reliable analyses over time). Race and ethnicity of children or other household members is not available.

Regression models used SCF sampling weights to account for the survey design and survey nonresponse. Model standard errors were adjusted to account for the imputed replicates of the data provided by the Federal Reserve. Descriptive statistics are based on one replicate dataset and are weighted. Sample sizes are based on one replicate dataset and are unweighted.

Measures

We define net worth as the value of total household assets less total debts. Assets include the value of savings and checking accounts, certificates of deposit, pooled investment accounts, stocks, bonds, retirement accounts, the value of the primary residence and other real estate, business assets, tangible assets (i.e., art and jewelry), and assets not classified elsewhere. Debts include mortgages on the primary residence, mortgages on other real estate, business debt, credit card debt, educational debt, vehicle loans, and other liabilities. Each of the assets can feasibly be converted and used to pay for current expenses. Because we subtract debt from assets, we include only the portion of each asset that would remain if the household sold the asset.

Following previous work (Haveman & Wolff, 2004; Haveman & Wolff, 2005), our measure of net worth excludes the future value of pension and Social Security income, assets that a household may realize in the future but does not currently possess. We also exclude the value of a household’s vehicles because such vehicles typically have a high consumptive value (e.g., are necessary transportation for work) and cannot realistically be converted to cash. In a separate analysis, levels of net worth poverty were lower when future pensions and vehicles were included. However, trends over time and racial/ethnic disparities in net worth when these assets were included were substantively the same as described here (results available upon request).

We define net worth and income poverty using 2019 census thresholds (Semega et al., 2020). Households are net worth poor if their net worth is less than one-quarter of the poverty line, adjusted for family composition. Following convention (Haveman & Wolff, 2004), we measured net worth poverty over a 3-month period; although arbitrary, 3 months may correspond to the amount of savings displaced when households suffer an income shock (see Brandolini et al., 2010 for more on this issue). In 2019, a two-adult/two-child household was poor if total household income was less than $25,926 (Semega et al., 2020). The same household would be net worth poor if their net worth was less than $6,482. This definition of net worth poverty has limitations; for example, like income poverty, net worth poverty is an absolute measure, and a relative measure might be preferred (Brady, 2003). Nevertheless, our net worth poverty measure provides a sensible estimate of whether households have an adequate stock of resources to meet basic needs for a predetermined length of time.

In complementary analyses, we divided households into one of four mutually exclusive categories: not poor, income but not net worth poor, net worth but not income poor, and both income and net worth poor. We also analyzed whether a household was net worth poor using asset levels alone, based on either liquid (e.g., savings and checking accounts, certificates of deposit, pooled investment accounts, stocks, bonds) or nonliquid assets (e.g., home or real estate equity, business equity, the value of other tangible assets, retirement accounts). As a robustness check, we also defined households as net worth poor if their net worth was less than the median level of net worth for child households (results not presented but available upon request). Estimates of net worth poverty changed relatively little using this alternative definition.

Regression models included measures of the household head’s age (linear and squared terms), education, and marital/partnership status; the number of children in the household; the age of oldest child in the household, and the presence of adult over the age of 65. Education consisted of four categories: no high school diploma, high school diploma (omitted category), some college, and bachelor’s degree or higher. Marital/partnership status was a combination of the relationship status and gender of the household head, with five categories: married couple, cohabiting couple, never-married mother, divorced mother (including the few widows), and single father. Married and cohabiting couples include both different- and same-sex couples. Too few never-married and divorced fathers and same-sex couples were surveyed to constitute a separate category. Number of children was measured categorically (one [omitted], two, or three or more), as was age of oldest child (less than 6 years old [omitted], 6 to 11, or 12 to 17).

Descriptive statistics (Table 1) show that across years, 34% of child households were net worth poor, twice the percentage that were income poor. Nearly two-thirds of households were neither income nor net worth poor. Net worth poverty (26%) was eight times as common as income poverty (3%) and three times as common as being both income and net worth poor (11%). Across all four poverty categories, the share of households who were net worth poor rose, whereas the fraction of households who were income poor decreased. In the 1989 cohort, 68% of the child households in the sample were White, 74% were married households, and 22% of household heads had a bachelor’s degree or higher. In 2019, only 56% were White, 63% were married, and 37% of household heads had at least a bachelor’s degree.

Table 1.

Descriptive Statistics, Child Households

All years 1989 2004 2019
Poverty
 Net worth 0.34 0.32 0.30 0.35
 Income 0.17 0.18 0.14 0.11
Type of poverty
 None 0.61 0.61 0.65 0.63
 Income 0.05 0.07 0.04 0.03
 Net worth 0.22 0.21 0.20 0.26
 Income and net worth 0.11 0.11 0.10 0.09
Net worth (median dollar) 50,580 57,521 66,971 63,480
Income (median dollar) 65,160 62,277 69,615 71,268
Race/Ethnicity
 White 0.63 0.68 0.65 0.56
 Black 0.16 0.15 0.14 0.16
 Hispanic 0.13 0.12 0.14 0.15
 Other race/Ethnicity 0.08 0.05 0.07 0.14
Education
 No high school 0.14 0.22 0.13 0.11
 High school or equivalent 0.32 0.32 0.32 0.24
 Some college 0.25 0.23 0.25 0.28
 BA or higher 0.29 0.22 0.30 0.37
Marital status
 Married 0.66 0.74 0.66 0.63
 Cohabiting 0.09 0.04 0.09 0.13
 Divorceda 0.16 0.15 0.17 0.13
 Never married 0.09 0.06 0.09 0.11
 Single father 0.03 0.01 0.04 0.04
Age of oldest child
 Under 6 0.28 0.33 0.26 0.27
 6 to 11 0.31 0.27 0.32 0.31
 12 to 17 0.41 0.40 0.42 0.42
Person >65 in Household 0.03 0.02 0.02 0.04
Household size 3.99 4.09 3.93 4.03
(1.28) (1.30) (1.26) (0.98)
Number of children in Household 1.91 1.95 1.88 1.94
(1.01) (1.01) (0.99) (1.02)
Sample size 19,013 1,138 1,646 1,759
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a

Includes widowed households.

Analyses

We first present descriptive estimates of net worth and income poverty by year and race/ethnicity of the household head. We then examine how racial/ethnic gaps in both income and net worth poverty have grown over time. We model either income or net worth poverty as a function of year and an interaction term between year and Black or Latino child household. Statistically significant interaction terms would indicate that, relative to White households, net worth (or income) poverty grew in that year for that subgroup. We then describe variation over time, by race and ethnicity, in the four poverty status categories. Finally, we present results of multinomial logistic regression models to explore how much of the racial/ethnic gaps in each of the four poverty status category variables can be explained by differences across groups by observed covariates. Our dependent variable is a multicategory indicator coded as net worth poverty only, income poverty only, both types of poverty, or no poverty (omitted). We present model results as predicted probabilities by race with other covariates held at their means.

Results

Income and Net Worth Poverty Over Time

As anticipated, Black and Latino child households had higher levels of income and net worth poverty than did White child households (Figure 1). At every time point examined, White households had income and net worth poverty rates that were at least half as large as those of Black or Latino households. In 2019, 57% and 50%, respectively, of Black and Latino child households were net worth poor, whereas 19% of Black and 18% of Latino child households were income poor.

Figure 1.

Figure 1.

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Trends in Income and Net Worth Poverty, by Race and Ethnicity.

Consistent with national estimates (U.S. Census Bureau, 2020), income poverty (Figure 1, Panel B) decreased over time, particularly for Black and Latino child households. Because of these declines, both White-Black and White-Latino disparities in income poverty were smaller in 2019 than they were in 1989.

In contrast, net worth poverty rose for Black households, and White-Black disparities in net worth poverty grew over time. Rates of net worth poverty for Black child households increased in the early 2000s, from 46% in 2001 to 47% in 2004 and 51% in 2007. These increases continued until 2013, before rates abated slightly. A notable increase in net worth poverty for White child households (from 24% to 30%) occurred between 2007 and 2010, corresponding to the Great Recession. Since then, White households’ net worth poverty rates have decreased, and their poverty rate in 2019 (23%) was nearly identical to their poverty rate in 1989 (24%). The increase in net worth poverty for Black households, when combined with the null increase for White households, has widened the racial gap over time.

Unlike the narrowing of income poverty gaps, Latino-White disparities in net worth poverty remained relatively constant. Changes in Latino net worth poverty were small, and trends for Latino households (rising during the Great Recession and falling afterward) closely mirror those for White households. As a result, White-Latino disparities in net worth poverty were similar in 1989 and 2019.

Regression models examining changed over time (Table 2) indicate higher levels of net worth poverty for Black and Latino households, with growing disparities in the Black-White net worth poverty gap. In models without covariates (Model 1), Black and Latino households, relative to White households, were associated with increases of 26% and 29%, respectively, in net worth poverty rates (p<.001 for both). Relative to 2004 (the omitted category), the Black-White poverty disparities were significantly larger in 2013 and 2016. Latino child households did not see any increase in net worth poverty beyond what was experienced by White child households, and White-Latino gaps were statistically equivalent over time. The inclusion of covariates (Model 2) did not substantially alter the pattern of findings: Black and Latino households had higher net worth poverty rates, and Black households had proportionally larger increases in net worth poverty than White households in the years after the Great Recession.

Table 2.

Regression Models of Net Worth Poverty and Income Poverty

Net worth poverty
 Income poverty
Model 1 Model 2 Model 1 Model 2
Black .258 (.032)*** .107 (.033)*** .203 (.034)*** .033 (.029)
Hispanic .285 (.028)*** .123 (.025)*** .161 (.028) .009 (.027)
Year (reference = 2004)
 1989 .008 (.022) −.032 (.020) .006 (.015) −.015 (.013)
 1992 .010 (.015) −.008 (.014) .020 (.011) .008 (.011)
 1995 .010 (.018) −.020 (.016) .031 (.012)* .009 (.011)
 1998 .026 (.021) .007 (.017) .040 (.015)** .030 (.012)*
 2001 −.012 (.017) −.015 (.014) .001 (.013) −.006 (.011)
 2007 .027 (.016) .021 (.014) −.002 (.011) −.011 (.011)
 2010 .081 (.013)*** .093 (.011)*** .016 (.010) .009 (.010)
 2013 .072 (.016)*** .083 (.014)*** .017 (.012) .007 (.011)
 2016 .030 (.016) .047 (.014)** .006 (.011) −.004 (.010)
 2019 .017 (.017) .042 (.015) −.020 (.012) −.016 (.011)
Interaction terms
 Black×1989 .038 .053 .029 (.047) .129 (.052)* .090 (.049)
 Black×1992 .078 .050 .073 (.048) .074 (.053) .083 (.044)
 Black×1995 .080 .054 .059 (.047) .148 (.049)** .141 (.040)***
 Black×1998 −.032 .048 −.049 (.043) .067 (.046) .068 (.037)
 Black×2001 −.001 .040 −.013 (.042) .022 (.043) .037 (.038)
 Black×2007 .002 .038 .015 (.036) .016 (.042) .051 (.038)
 Black×2010 .020 .034 .028 (.030) −.041 (.038) −.002 (.035)
 Black×2013 .087 .039* .104 (.036)** −.040 (.039) −.001 (.032)
 Black×2016 .117 .038** .115 (.037)** −.021 (.042) −.005 (.037)
 Black×2019 .083 .041 .110 (.039) −.072 (.042) −.045 (.038)
 Hispanic×1989 .031 (.053) .080 (.055) .116 (.074) .145 (.071)*
 Hispanic×1992 .061 (.042) .041 (.043) .191 (.051)*** .165 (.045)***
 Hispanic×1995 .025 (.047) .079 (.042) .074 (.053) .125 (.048)**
 Hispanic×1998 −.016 .050 .007 (.041) .073 (.041) .075 (.041)
 Hispanic×2001 .089 .041* .061 (.036) .120 (.040)** .111 (.039)**
 Hispanic×2007 −.047 .039 −.024 (.033) −.019 (.041) .020 (.043)
 Hispanic×2010 −.044 .033 −.027 (.028) .009 (.035) .030 (.037)
 Hispanic×2013 .037 .041 .044 (.037) .028 (.039) .054 (.037)
 Hispanic×2016 −.044 .038 −.010 (.036) −.012 (.036) .016 (.033)
 Hispanic×2019 −.020 .040 −.005 (.039) −.038 (.034) −.008 (.033)
Sample size 19,013 19,013 19,013 19,013
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Note. Model 2 controls for other race/ethnicity, education, relationship status, number of children in the household, age and age squared, child age, if household has member greater than age 65, and household size.

*

p<.05.

**

p<.01.

***

p<.001.

Results for income poverty suggest a different pattern of findings. Whereas unadjusted models indicate that Black and Latino households had higher income poverty rates than White households, adjusted models indicate that these racial/ethnic disparities can be explained largely by differences in sociodemographic characteristics. In unadjusted models, Black households had a statistically significant 20.3% increase in income poverty (p<.001); in adjusted models, that coefficient fell to a nonsignificant increase of 0.3%. Moreover, racial/ethnic disparities in income poverty did not increase in the 2010s. With the exception of Latino families in the 1990s, the relative gap in income poverty between Latino and White households was relatively constant over time, as was the gap between Black and White households.

Child Households Considering Both Income and Net Worth Poverty Status

A cross-tabulation of income and net worth poverty indicated that most net worth poor child households were not income poor (results available upon request). In 2019, of the 35% of child households that were net worth poor, only 25% were also income poor. The fraction of net worth poor child households that were also income poor was lower for White (20%) than for Black and Latino child households (28, respectively). Although the share of child households that were both net worth and income poor fluctuated over time, net worth poverty became increasingly distinct from income net worth poverty. Since 2001, for all races and ethnicities, the majority of child households that were net worth poor were not income poor.

Figure 2 displays results across the four categories of poverty. The modal category for White child households was the absence of poverty, whereas the modal category for Black and Latino households was poverty. (Note that the percentage of households that were income poor is lower in Figure 2 than in Figure 1 because some of the income poor households in Figure 1 have been reclassified into the combined income and net worth poverty category in Figure 2.) Across years, two-thirds to three-quarters of White child households were neither income poor nor net worth poor. In contrast, in any given year, between 54% and 67% of Black and Latino child households experienced income poverty, net worth poverty, or both types of poverty.

Figure 2.

Figure 2.

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Child Households by Poverty Status and Race/Ethnicity, Select Years.

For child households residing in poverty, the dominant category over time was net worth poverty only (the exceptions being Black child households in 1989 and 1995). In 2019, of the 60% of Black child households in poverty, 42% were net worth poor, 3% were income poor, and 16% were both income and net worth poor. Similarly, White and Latino child households in net worth poverty in 2019 represented 71% and 66%, respectively, of all those households in poverty.

White child households were less likely than Black and Latino child households to experience both types of poverty. Relatively few White child households (across years, 4% to 7%) were both income poor and net worth poor. In contrast, 14% to 33% of Black or Latino child households experienced both types of poverty. Over time, the shares of Black and Latino child households that experienced both types of poverty fell; nevertheless, in 2019, 16% and 14% of Black and Latino child households, respectively, were both income and net worth poor.

Predicted probabilities of poverty type (Figure 3) suggest that, in models that adjust for covariates, the likelihood of child households experiencing income poverty was relatively low—at .02 for White child households and .05 for Black and Latino households. In contrast, the likelihood of experiencing net worth poverty was quite high: one in four White child households and one in three non-White child households were predicted to be net worth poor. The racial/ethnic gap in income poverty (3 percentage points) was substantially lower than the similar gap in net worth poverty (13 to 14 percentage points).

Figure 3.

Figure 3.

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Predicted Probabilities, Poverty Categories, by Race/Ethnicity.

Notes. Models adjust for education, relationship status, number of children in the household, age and age squared, child age, households with a member greater than age 65, household size, and year. Estimates are weighted. Bars represent 95% confidence intervals. Unweighted sample size is 19,013.

Notably, our findings indicate that Black and Latino child households had statistically similar probabilities of not being poor as they did of being net worth poor. Net of variations in demographic characteristics and year, the predicted probability of Black and Latino child households being net worth poor was .36 and .37, respectively. These estimates were only slightly lower (and statistically indistinguishable) from the estimates for not being poor (.38 and .40, respectively). In contrast, the predicted probability of net worth poverty for White child households was about .23, almost 50 percentage points lower than that of not being poor (.71).

Sensitivity of Racial/Ethnic Differences by Assets, Homeownership, and Debt

Our findings were robust to alternative definitions of net worth poverty (see Appendix Figure S1). In particular, when net worth poverty was defined using assets alone, poverty rates were (a) slightly higher when poverty was defined in reference to liquid assets and (b) slightly lower when poverty was defined in reference to nonliquid assets. Disparities between Black and Latino households differed somewhat across definitions: liquid asset poverty was generally lower for Black households than for Latino households, whereas the reverse was true for nonliquid asset poverty. Regardless of the definition used, however, White households had substantially lower levels of net worth poverty across years than did Black or Latino households. In 2019, 59% of Black and 50% of Latino child households were net worth poor based on liquid assets alone, relative to 19% of White child households. Similar disparities were evident for nonliquid asset poverty, with poverty rates of 54%, 56%, and 27% for Black, Latino, and White child households, respectively. (Estimates here differ somewhat from Rothwell et al.’s, 2019 estimates of liquid asset poverty because of differences in the data source, measurement of assets, and poverty threshold.)

To examine whether net worth poverty was simply a proxy for homeownership, we also examined net worth poverty by homeownership status (see Appendix Figure S2). Findings indicate that the estimated differences by racial/ethnic group in net worth poverty were not explained by homeownership status. Although child households without homes had high net worth poverty rates (from 73% to 95%), homeownership did not guard against net worth poverty. For Black and Latino child households, the share of homeowners that were net worth poor generally increased over time, reaching 18.8% and 15.3%, respectively, in 2019. Racial/ethnic disparities in net worth poverty among homeowners were large, with White child households having the lowest share of net worth poverty of homeowners.

Discussion

Our study is the first to examine the intersection of income and net worth poverty for child households in the United States, finding that one-third of child households experienced net worth poverty in 2019. Past studies have documented low levels of wealth for child households (Gibson-Davis & Percheski, 2018; Percheski & Gibson-Davis, 2020) but have not codified wealth deprivation or compared relative risks of experiencing income and net worth poverty. Yet, we found that net worth poverty was the dominant form of poverty: White, Black, and Latino child households were two to three times more likely to be net worth poor than income poor. In 2019, strikingly high numbers of Black (57%) and Latino children (50%) lived in net worth poverty. Indeed, in models that adjusted for a host of other demographic characteristics, the likelihood of not being poor for Black and Latino child households (36% and 37%, respectively) was substantively similar to the likelihood of being net worth poor (38% and 40%).

Although racial/ethnic disparities in income poverty narrowed over time, racial/ethnic disparities in net worth poverty increased. These disparities were also larger than similar gaps in income poverty. Overall, during a period when child income poverty declined slightly (down 6 percentage points), net worth poverty trended in the opposite direction (up 1 percentage points). This divergence was amplified for Black child households, for whom income poverty declined by 37% but net worth poverty increased by 18% between 1989 and 2019. Interaction models further suggested that the Black-White net worth poverty disparity grew in the years following the Great Recession. After we adjusted for sociodemographic characteristics, however, the Black-White income poverty gap remained relatively constant over the observation period. Latino-White gaps in net worth poverty did not, on net, change between 1989 and 2019, but because income poverty declined for Latino child households, net worth poverty disparities were larger than income poverty disparities.

Net worth poverty was not reducible to homeownership, insofar as one-fifth of Black and one-fourth of Latino child households in 2019 experienced net worth poverty even if they owned homes. High rates of Black net worth poverty for homeowners are consistent with the contention that policies promoting homeownership would be insufficient to close the Black-White wealth gap (Darity et al., 2018). Furthermore, given that net worth poverty was not fully captured by homeownership, studies on the influence of homeownership child well-being (e.g., Leventhal & Newman, 2010) are unlikely to fully capture the developmental role of net worth poverty.

Overall diverging trends in income and net worth poverty among child households and their magnified divergence by race and ethnicity not only underscore the analytic differences between these two poverty constructs but also point to the depth and breadth of economic deprivation along racial dimensions. In 2019, 16% of Black child households experienced both income and net worth poverty—a figure 11 percentage points higher than that for White child households and 2 percentage points higher than that for Latino child households. Although sociodemographic variation statistically predicted racial/ethnic disparities in income poverty, these characteristics did not statistically capture similar variation in racial/ethnic disparities in net worth poverty. Explaining racial/ethnic disparities in net worth poverty is beyond the scope of this analysis. However, unexplained variance in net worth, but not income, poverty raises questions about the role of structural inequality (i.e., racist practices that limit access to credit markets, impede home acquisition, and inhibit educational opportunities) in asset accumulation for non-white households (Darity & Mullen, 2020; Oliver & Shapiro, 1995).

Our findings also raise questions about the extent to which policy solutions focused solely on income enhancement will support wealth accumulation or reduce wealth deficits. Policies designed to support earnings accumulation (e.g., the earned income tax credit) or offer in-kind support (e.g., the women, infants, and children food packages) can alleviate constraints on immediate consumption but will affect wealth only to the extent that net income is increased and used toward offsetting debt or investing in assets. A number of revisions to policy could support wealth accumulation. Expanding housing or childcare subsidies—as two of the largest costs to working households—could free up net income such that it is redirected to wealth accumulation. Asset tests in U.S.-based safety net programs that require participants to draw down their wealth before becoming eligible could be relaxed so that families are not disincentivized to accumulate assets (Ratcliffe et al., 2016). Offering low- or no-cost grants or loans to families receiving income-based government benefits could facilitate transitions to homeownership or the establishment of savings and educational accounts.

Regulating predatory lending behavior, including underwriting of mortgages and loan structure, that occurs in low income and communities of color can prevent foreclosure and debt traps. Such regulation could be coupled with opportunities to be banked and access credit with the formal financial sector that is particularly relevant given that our findings show that net worth poverty is not reducible to homeownership. Indeed, Black households own fewer financial assets at all levels of the wealth distribution than other households. Finally, child directed policies to reduce intergenerational transmission of wealth deficits, such as baby bonds and child development accounts that are gaining public and political support, could be considered, though the implementation of such strategies to date have had mixed success (Jones et al., 2019; Shanks, 2014; Sherraden et al., 2018).

Our study has its limitations. Our estimates are descriptive, not causal, and we cannot disentangle the endogenous relationship between income and wealth. The SCF is one of the only datasets allowing for the type of detailed measurement of net worth poverty we pursue here, but because it does not follow families over time, we cannot examine how net worth and income poverty fluctuates across children’s developmental milestones. In addition, the SCF does not have large enough samples of other important racial/ethnic groups whose income poverty is consistently tracked by the U.S. Census, such as Asians and Native Americans; and, identification of race and ethnicity is limited to the respondent and not the child or other members of the household. Finally, each of our primary net worth measures includes multiple assets and debts. Future research could usefully explore how net worth poverty varies across time and by race/ethnicity by decomposing assets and debts into its component parts.

The increasing prominence of net worth poverty in the lives of child households, particularly those who are Black or Latino, underscores the economic precarity faced by many children in the United States. Our results of deepening rates of net worth poverty for Black households accords with the over-time declines in wealth found for Black child households in the bottom of the wealth distribution (Percheski & Gibson-Davis, 2020). By codifying wealth deprivation, though, we seek to stimulate research on the potential harms of net worth poverty. Net worth poverty likely poses risks much as income poverty does, insofar as wealth shows robust positive associations with child development that cannot be attributed solely to income (Berger & Houle, 2019; Conley, 2001; Jez, 2014; Pfeffer, 2018). Moreover, we found that the overlap between the two types of poverty was low: most child households that were net worth poor were not income poor. We also found that net worth poverty, but not income poverty, is on the rise. In light of findings that wealth during childhood affects both current and future well-being (Hällsten & Pfeffer, 2017; Pfeffer & Killewald, 2017; Pfeffer & Schoeni, 2016), the well-being of economically vulnerable children will hinge not only on addressing income poverty but also on understanding and addressing the depth of net worth poverty.

Supplementary Material

Supplemental Material

Figure S1. Net Worth Poverty by Definition and by Race/Ethnicity

Figure S2. Net Worth Poverty Status by Homeownership, Select Years

Footnotes

Christina Gibson-Davis acknowledges support from the Russell Sage Foundation. Lisa Keister acknowledges support from the National Science Foundation.

Supporting Information

Additional supporting information may be found online in the Supporting Information section at the end of the article.

Appendix S1. Supporting information.

Contributor Information

Christina Gibson-Davis, Box 90312, Duke University, 302 Towerview Road, 212 Rubenstein Hall, Durham, NC 27708..

Lisa A. Keister, Box 90088, Duke University, 302 Towerview Road, 212 Rubenstein Hall, Durham, NC 27708.

Lisa A. Gennetian, Duke University, 302 Towerview Road, 212 Rubenstein Hall, Durham, NC 27708..

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Supplementary Materials

Supplemental Material

Figure S1. Net Worth Poverty by Definition and by Race/Ethnicity

Figure S2. Net Worth Poverty Status by Homeownership, Select Years

NIHMS1751453-supplement-Supplemental_Material.pdf (185.6KB, pdf)

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