INTRODUCTION

Economic stability is inextricably connected to physical and mental health. As one of the five social determinants of health (SDOH) categories, economic stability is broad and includes employment and income,¹ noncash benefits, such as Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and Supplemental Nutrition Assistance Program (SNAP), wealth,² and financial services, such as banking and credit scoring. Economic stability enables access to other SDOH, such as food security, housing stability, safe and healthy neighborhoods (see Chapter 6), education (Chapter 4), and social capital (Chapter 7). Access to these health-protective resources and conditions is one mechanism tying economic stability to health outcomes. Additionally, economic stability enables access to quality health care, which directly impacts health outcomes (see Chapter 5) (NASEM, 2017). Healthy People 2030 recognizes the impact of economic stability on health outcomes and considers how it can be affected by factors such as illness and disability (HHS, 2020). Although this chapter is titled “Economic Stability,” the committee recognizes that enabling all people to thrive economically, rather than just achieve stability, is needed to advance equity and create a sustainable path to well-being.

Progress has been made toward improving the economic stability of U.S. households in recent decades. Median household incomes have been growing, and poverty rates have been decreasing (Creamer et al., 2022; Semega and Kollar, 2022; Wimer et al., 2022). On the other hand, median household wealth, an important source of protection against economic and health shocks, has been stagnant in recent decades (Horowitz et al., 2020). Additionally, millions of people still live in poverty despite progress made, and poverty is disproportionately experienced by racially and ethnically minoritized populations and tribal communities (KFF, n.d.; Krogstad, 2014). Racial, ethnic, and tribal inequities also exist in income and employment, wealth, and financial services, such as banking and credit scoring. These inequities stem in part from the legacy of structural racism and policies, such as redlining, territorial dispossession, and the disparate access to benefits of the 1944 GI Bill, that have harmed racially and ethnically minoritized and tribal populations. This chapter reviews these areas as opportunities for federal policy to be leveraged to improve economic stability for these populations and in turn advance health equity. Specifically, the chapter explores the following: the effect of incarceration and pretrial detention on income and wealth; the federal minimum wage; how administrative barriers and eligibility restrictions impact participation in federal social benefit programs; how nonprofit sector partnerships can play a role in poverty alleviation and emergency food assistance; the role of past policies, such as redlining and disparate access to benefits from the 1944 GI Bill, on inequities; policies to support savings and wealth accumulation, such as baby bonds; and access to safe and affordable banking services, such as bank accounts and low-cost credit. These examples include both policies that have intentionally perpetuated inequities (i.e., redlining) and policies that promote equity and can be improved for greater success (i.e., federal social benefit programs whose reach can be expanded by eliminating or reconsidering administrative barriers and eligibility restrictions). See Chapter 1 for an overview of the committee's process for selecting the policies reviewed in this report.

This chapter is unable to report on the Native Hawaiian and Pacific Islander (NHPI) population specifically due to the absence of data, rendering them invisible in this discussion. Additionally, data aggregation masks differences between NHPI and Asian people. Similarly, there is a lack of data, and in some cases inaccurate data, for American Indian and Alaska Native (AIAN) populations, masking the extent of the challenges faced by these communities (see Chapter 2 for more details). The frontmatter of this report includes a list of the committee's chosen terminology for the racial and ethnic groups discussed therein. However, sometimes the terms used in the report are determined by the language in data systems or a specific research study referred to or summarized. Although the committee chose to use “Latino/a” to refer to persons with cultural connections to Latin America, “Hispanic” appears frequently in this chapter due to its use in many of the research studies to which the chapter refers.

INCOME AND EMPLOYMENT

Income is associated with health outcomes across the life span, with higher income consistently predictive of better health. Babies born to low-income families have higher infant mortality compared to middle and higher income families. They also have lower birthweights compared to middle income families (Kennedy-Moulton et al., 2022). Lower-income adults die on average at earlier ages, and the gap in life expectancy has been growing in recent decades (NASEM, 2015). In between, there are gaps across income levels in access to health care services and differences in behavior, stress, and other factors that contribute to health inequities (Garfield et al., 2020; NASEM, 2017). When individuals are asked to assess their health status, lower-income individuals report worse health than higher-income individuals report (Schanzenbach et al., 2016b). Substantial evidence indicates that increasing incomes can improve health outcomes (Finkelstein et al., 2022). Those with more resources can invest in opportunities for health improvement, including medical care, housing and neighborhood amenities that promote good health, and other important determinants of health.

Income and race are correlated, with White and Asian people consistently having higher incomes than Black, Hispanic, and AIAN people (Semega and Kollar, 2022). Figure 3-1 shows 2021 Census data for mean and median household incomes by the reported race or ethnicity of the householder. The median for White households was just over $74,000, compared to just over $48,000 for Black households, just over $51,000 for AIAN households, and nearly $58,000 for Hispanic households. The median among Asian households was just over $100,000 (Semega and Kollar, 2022). Although mean household incomes are higher than median household incomes, differences across racial and ethnic groups are similar in percentage terms for both measures.

FIGURE 3-1

Median household income by race and ethnicity of householder, 2021. NOTES: The data for Native Hawaiian and Pacific Islander populations are not reported separately, providing an inaccurate representation of this distinct racial Office of Management and (more...)

One way to analyze changes over time is to compare how gaps in income relative to White households have evolved (see Figure 3-2). At the median, relative income among Black households has remained stagnant. It was 64 percent of White household income in 2002 and 65 percent in 2021. Hispanic household incomes went from 73 percent of White household incomes in 2002 to 78 percent in 2021. Income among AIAN households relative to White households went from 73 percent of White household incomes in 2002 to 69 percent in 2021. Asian households' relative incomes have increased from 17 percent higher than White households in 2002 to 37 percent higher in 2021 (Semega and Kollar, 2022).

FIGURE 3-2

Relative median income, 2002 and 2021. NOTES: The data for Native Hawaiian and Pacific Islander populations are not reported separately, providing an inaccurate representation of this distinct racial Office of Management and Budget minimum category and (more...)

Gaps in household incomes reflect differences across many characteristics, including household composition (Census Bureau, 2022), whether household members are employed and work full time or part time (Cajner et al., 2017; Federal Reserve Bank of St. Louis, n.d.), and salary and wage rates (Gould, 2020). For example, the unemployment rate—measured as the share of people in the labor force (not retired, students, or otherwise not seeking work) who are not employed—is consistently higher for Black and Hispanic people than White and Asian people (see Figure 3-3). Between 1975 and 2021, it averaged more than twice as high for Black people than White people (Federal Reserve Bank of St. Louis, n.d.). Over this period, it ranged from about 30–90 percent higher among Hispanic people than White people (Federal Reserve Bank of St. Louis, n.d.). Research finds that factors such as age, education, marital status, and geography explain relatively little of the difference in unemployment rates between Black people and White people (in contrast to the larger explanatory power of these factors in differences in earnings, described below) but a higher share of the difference in unemployment rates between Hispanic people and White people (Cajner et al., 2017; Siripurapu, 2022).

FIGURE 3-3

Unemployment rate, by race and ethnicity, 1975–2021. NOTE: Data for American Indian and Alaska Native and Native Hawaiian and Pacific Islander people were not available through the Federal Reserve Bank of St. Louis. SOURCE: U.S. Bureau of Labor (more...)

Despite recent progress, large and troubling gaps remain among the employed in income and earnings across racial and ethnic groups. Many factors contribute to these gaps, including differences in education, age, family composition, occupation, time in the workforce, gender, geography, and incarceration rates (Bayer and Charles, 2016; Grodsky and Pager, 2001; Looney and Turner, 2018; Neal and Johnson, 1995). However, even accounting for these factors, sizable gaps in earnings across racial and ethnic groups persist (Bayer and Charles, 2016; Chetty et al., 2020). Between 2000 and 2019, after statistically accounting for the effects of age, gender, education, and geography, the gap in wages between Black and White wage earners grew from 10.2 percent to 14.9 percent, but wage gaps between Hispanic and White wage earners decreased from 12.3 percent to 10.8 percent (Gould, 2020).

Research demonstrates differential treatment by race in the job application process, pointing to labor market discrimination as one explanation for these remaining gaps. In a 2004 study, fictitious resumes with randomly assigned names that sounded “White” or “Black” were submitted to help-wanted ads and received different responses based on the names' racial perception (Bertrand and Mullainathan, 2004). The study found that resumes assigned names that sounded White were 50 percent more likely to be asked to interview. The authors noted that the racial gap did not vary across occupation, industry, or employer size and found little evidence that names were being used to infer social class instead of race. The authors conclude that “differential treatment by race still appears to be prominent in the U.S. labor market” (Bertrand and Mullainathan, 2004, p. 991). Additional studies have similarly found that job applications with names that sound Black have a lower response rate compared to names that sound White (Agan and Starr, 2018; Kline et al., 2022).

Differences in economic outcomes across racial and ethnic groups have followed different patterns among men and women (Altonji and Blank, 1999). Among men, across all racial and ethnic groups, wages and employment have declined over time and become more unequal. Men's labor force participation rates have declined for White, Hispanic, and Black men over the past 50 years, with White and Hispanic men participating in the labor force at higher rates and experiencing a smaller decline over time than Black men (Council of Economic Advisers, 2016). Inflation-adjusted hourly wages for men fell for each group between 1979 and 2016, but wage gaps have increased because Black and Hispanic men's wages dropped by 8.9 and 7.6 percent, respectively, compared to 1.4 percent for White men (Shambaugh et al., 2017). Although some of the differences in earnings between Black and White men (which is more often studied than the Hispanic–White gap) can be explained by easily measurable factors, such as age and education, the unexplained share has grown from about one-third in 1979 to about half in 2016 (Daly et al., 2017). Studies also show that significant portion of the wage gap between Hispanic and White male workers is attributable to education, a gap that has widened (Mora and Dávila, 2018).

The trends are somewhat different among women. Labor force participation rates increased for White, Hispanic, and Black women from 1971 through around 2000 but dropped since then. White and Black women's participation is nearly equal, with Hispanic women's participation about 10 points lower (Black et al., 2017). Women's occupations vary across racial and ethnic groups. For example, in 2021, 25.0 percent of employed Black women worked in service occupations, compared to 18.6 percent of White women and 29.7 percent of Hispanic or Latina women (BLS, 2023). Inflation-adjusted hourly wages increased for each group between 1979 and 2016, but gaps have increased because Black and Hispanic women's wages increased by 16.9 and 17.5 percent, respectively, compared to 33.7 percent for White women (Shambaugh et al., 2017). Similar to the pattern among men, both the gap and the share of the Black–White gap among women that is unexplained have grown over time (Daly et al., 2017).

Research has highlighted some similar and different explanations for the income gaps between White people and Black, Latino/a, and AIAN people. As discussed in Chapter 2, data on AIAN populations have been too often lacking. For example, only recently did the Census Bureau produce monthly data on AIAN unemployment, showing that rates are higher than for any other racial or ethnic group: 11.1 percent in January 2022, more than double that of White people (Maxim et al., 2022). Household composition is an important factor in the income gap, as 55 percent of AIAN mothers are the sole or primary earner in households with children under 18, compared to 37 percent of White mothers (Pathak, 2021). The income gap between White and AIAN people also reflects centuries of federal actions, practices, policies, and laws, such as forced assimilation, territorial dispossession, extermination, and failure to honor treaty obligations (discussed in Chapter 7).

“Hispanic,” like all racial and ethnic classifications, encompasses individuals from many distinct and unique backgrounds. Mexico is the largest country of origin for Hispanic people in the United States, at 63 percent, but this means that more than one in three is from another country (Scherer and Mayol-García, 2022). Research has shown that Hispanic people from different countries have differing economic outcomes; this is sometimes lost when data are aggregated. For example, among Hispanic people from different countries of origin, there are differences in economic resilience³ and security.⁴ Figure 3-4 looks at material hardship during the recent COVID-19–induced recession: Salvadoran and Dominican people were more likely to experience hardship, while Colombian and Cuban people were less likely, indicating greater sources of economic resilience within those groups (Scherer and Mayol-García, 2022). Households were considered to have experienced material hardship if they experienced hardship in at least one of the following: food, bill-paying, or housing.

FIGURE 3-4

Share experiencing material hardship by detailed Hispanic origin: 2020 (in percent). NOTE: Material hardship reflects whether individuals lived in households that experienced food, bill-paying, and/or housing hardship. The bars reflect the 90 percent (more...)

Although some federal policies have helped low-income and minoritized people, others have played a role in bringing about the income inequities this section describes. For example, incarceration (at the federal, state, or local level) has a lasting impact on income: Craigie et al. (2020) found that it is associated with a 52 percent reduction in annual earnings and with employment in low-paying jobs, impacting earnings growth for life (its impacts on income are just one aspect of incarceration's effect on equity; for a more comprehensive discussion, see Chapter 7). The Pew Charitable Trusts (2010) found that family income is 22 percent lower while a father is incarcerated and remains 15 percent lower the year after a father is released. They also found that incarceration is associated with reduced total or lifetime earnings of 2, 6, and 9 percent for White, Hispanic, and Black men, respectively (The Pew Charitable Trusts, 2010). These data have particularly important implications for racial and ethnic inequities, given that Black and Latino/a people make up nearly 65 percent of formerly imprisoned people in the United States (Craigie et al., 2020). AIAN people are also disproportionately incarcerated, at 38 percent over the national average, and overrepresented in the prison population of 19 states compared to other racial and ethnic populations (Fox et al., 2023; Wang, 2021).

Pretrial detention can also have a lasting impact on economic outcomes. Research has found that it substantially reduces individuals' subsequent earnings and that higher levels of pretrial detention may decrease intergenerational mobility in later years (Dobbie and Yang, 2021). This impacts racial and ethnic equity because Black and Hispanic people have significantly higher pretrial detention rates than White people (Dobbie and Yang, 2021). Federal policy changes relating to incarceration and pretrial detention therefore provide a potential opportunity to address racial and ethnic inequities in income and thereby health and well-being. Chapter 7 further discusses how federal policy can address racial and ethnic inequities in the criminal legal system. The wealth section of this chapter further discusses how inequities in the criminal legal system propagate racial and ethnic inequities in wealth through fines and fees.

Minimum wage laws are another example of how federal policy can address racial and ethnic inequities in income. Legal standards for minimum wages can be set at the local, state, and federal levels. The federal minimum wage is usually set in nominal terms, with periodic adjustments in law. This structure results in declines in real value during intervals between nominal adjustments (Lee, 1999); the rate of that decline is governed by inflation. The federal minimum wage last increased in July 2009, making this the longest period without an increase since it was established in 1938; in inflation-adjusted terms, it is at its lowest level since the 1950s (Cooper et al., 2022).

Thirty states and D.C. have minimum wages above the federal rate of $7.25/hour (NCSL, 2022). Several states have targeted minimum wages as high as $15 through law (including Illinois, New Jersey, and Rhode Island) or ballot initiative (Florida); others have lower targeted levels. Regional differences in factors such as cost of living and workforce composition create an environment where differences in minimum wage across states make sense (Dube, 2014). Although federal regulation lags behind states in many cases, numerous states have minimum wages at or below the federal level, and some have not set a minimum wage (in the latter two cases, the federal minimum wage applies and an increase could benefit individuals whose wages have not kept up with the cost of living) (DOL, 2023).

Increasing the minimum wage can help increase earnings for those at the lowest level and also reduce racial income disparities (Wursten and Reich, 2022); it would move some families out of poverty but likely also result in some job loss as workers become more expensive. The net effect would be a general reduction of the total number living in poverty, as the first effect is larger than the second. Incrementally increasing the federal minimum wage to $15 by 2027 could lift about 300,000 families above the poverty line, according to the mean estimate from the Congressional Budget Office, and increase real family incomes on net for families earning up to 300 percent of the poverty threshold (CBO, 2022). Real family income for higher-income families could see offsetting losses, primarily clustered among those earning above 600 percent of the poverty threshold (CBO, 2022). By raising incomes and lifting families out of poverty (which is disproportionately experienced by racially and ethnically minoritized populations) increases to the federal minimum wage could address racial and ethnic inequities in economic stability and therefore health and well-being.

Conclusion 3-1: Evidence demonstrates that pretrial detention substantially reduces lifetime income, and strongly links incarceration with lower lifetime earnings and family income for incarcerated individuals. Given racial and ethnic inequities in incarceration and pretrial detention, there are opportunities in these areas to address racial and ethnic inequities in income and, thereby, health and well-being.

Conclusion 3-2: Stagnation in the federal minimum wage, coupled with inflation, has left the real value of the minimum wage at a level not seen since the 1950s. Increases to the federal minimum wage raise incomes among low- and moderate-income families and lift families out of poverty. Since racially and ethnically minoritized populations and tribal communities are disproportionately represented in the groups that would be impacted by an increased federal minimum wage, such an increase is one method to address racial and ethnic inequities in economic stability and, therefore, health and well-being.

POVERTY

Poverty, defined by the Census Bureau as having income below a specific threshold that accounts for family size, unequally affects different racial and ethnic groups (Creamer et al., 2022). As shown in Figure 3-5, in 2021, more than 1 in 10 people who identify as Hispanic (of any race), Black, and AIAN had incomes below the poverty threshold. Just under 1 in 10 Asian people were in poverty, as were about 1 in 17 White (not Hispanic) people. Figure 3-6 shows the percent of children in poverty by race and ethnicity. The poverty rates in Figures 3-5 and 3-6 are based on the Census Bureau's Supplemental Poverty Measure, which accounts for income from a wide range of sources, including earnings and social benefits programs, such as Social Security, SNAP, the Earned Income Tax Credit (EITC), and, especially important in 2021, Economic Impact Payments distributed as part of COVID-19 relief packages. AIAN people have the highest poverty rate of all racial and ethnic groups⁵ (Creamer et al., 2022), with consistently high childhood poverty rates among those living on a reservation (Akee, 2019). AIAN people have the highest poverty of all races for those 65 and older (NCOA, 2023).

FIGURE 3-5

Percent of people in poverty by race and ethnicity. NOTES: Data for Asian and Native Hawaiian and Pacific Islander (NHPI) populations are not shown separately, providing an inaccurate representation of the Asian and NHPI populations as distinct racial (more...)

FIGURE 3-6

Percent of children in poverty by race and ethnicity. NOTES: Data for Asian and Native Hawaiian and Pacific Islander (NHPI) populations are not shown separately, providing an inaccurate representation of the Asian and NHPI populations as distinct racial (more...)

Poverty is just one way to measure an individual's or household's limited access to economic resources. Many families that struggle with income volatility and lack access to necessary resources have incomes above the poverty threshold. For instance, two-thirds of food-insecure families have incomes above the poverty threshold (Schanzenbach et al., 2016a). Black, Hispanic, and American Indian populations have consistently higher rates of food insecurity (Coleman-Jensen et al., 2022; Schanzenbach and Pitts, 2020).

A range of social benefit programs significantly alleviate poverty. Programs differ in their designs in terms of the population targeted (e.g., retirees, people with disabilities, working parents), and the amount, type (cash or “in kind”—such as vouchers for food or housing), and duration of assistance. As a result, they have varying antipoverty effects on different groups (see Figure 3-7). For example, Social Security is most responsible for lifting incomes above the poverty line, but the impact is overwhelmingly on older adults who are the target (some younger adults and children may also be aided, such as by living in a family with an older recipient). The EITC and Child Tax Credit (CTC) lifted over 10 million out of poverty in 2018, with children and their parents the overwhelming beneficiaries (Bitler et al., 2020; CBPP, 2023a).

FIGURE 3-7

Change in number of people in poverty after including each element: 2021 (in millions). NOTE: The figure illustrates the change in the number of people in poverty after the including each tax, benefit, or expenditure deduction; FICA = Federal Insurance (more...)

The 2021 expansion of the CTC through the American Rescue Plan Act⁶ played an important role in this historic reduction of poverty (Hardy et al., 2023; IRS, 2023). The expansion increased the maximum credit amount; made the credit fully refundable, allowing families to claim it even if they did not have earned income or owe income taxes; and allowed families to receive part of the credit in 2021 through advance monthly payments (IRS, 2023). An analysis from the Center on Poverty and Social Policy at Columbia University revealed that monthly delivery of the credit more consistently reduced child poverty rates throughout the year than delivery of the credit in one lump sum after tax filing (Hamilton et al., 2022). These changes to the credit and their subsequent effect are especially critical for children; research demonstrates that poverty alleviation may be particularly important for children because it can improve their development and longer-term health and economic outcomes (Akee et al., 2018; Hardy et al., 2019; Hoynes et al., 2016).

A recent analysis from Child Trends found that child poverty declined by 59 percent from 1993 to 2019 (Thomson et al., 2022). As it declined at similar rates across racial and ethnic groups, inequities persisted. The analysis also found that the role of the social safety net⁷ in decreasing poverty has increased for all racial and ethnic groups from 1993 to 2019, but the size of the effect varies across racial and ethnic groups; it has been consistently larger for Black and White children and smaller for Hispanic and Asian/Hawaiian/Pacific Islander children (Thomson et al., 2022). Other studies have found that the effects of specific programs also differ across racial and ethnic groups. For example, Social Security benefits are more likely to represent more than 90 percent of income among elderly Black, Asian, and Hispanic people than elderly White people (National Academy of Social Insurance, 2021). Looking at impacts on children, Bitler et al. (2023) find that EITC and CTC reduce poverty rates among Black and Hispanic children substantially more than White and Asian children, and SNAP reduces poverty rates most for Black children.

Some especially vulnerable groups may disproportionately experience poverty and have less access to antipoverty programs, including childless adults, formerly incarcerated individuals, and immigrants. Because these groups also disproportionately represent some racial and ethnic groups, their outcomes contribute to health inequities. Research demonstrates that poverty alleviation, through increased earnings and employment and programs such as EITC and SNAP, can improve health and narrow health disparities across racial, ethnic, and tribal groups. For example, EITC increases mothers' likelihood of employment and income levels and reduces their likelihood of being in poverty (Hoynes and Patel, 2015; Schanzenbach and Strain, 2020). It also improves their biological markers for stress and self-reported mental health (Evans and Garthwaite, 2014) and reduces smoking (Averett and Wang, 2013; Hoynes et al., 2015; Strully et al., 2010). EITC also improves infant health, increasing average birthweight (Baker, 2008; Strully et al., 2010) and decreasing low-birthweight births (Hoynes et al., 2015). This is important in part because improvements in birthweight subsequently lead to improved learning outcomes during childhood and improvements across a range of outcomes in adulthood, including wages, disability, health conditions, and human capital accumulation (Almond et al., 2018; Figlio et al., 2014). EITC also improves educational outcomes for children, measured by test scores, high school graduation, and college enrollment (Bastian and Michelmore, 2018; Chetty et al., 2011; Dahl and Lochner, 2012, 2017). Improvements in education have also been shown to improve health outcomes, as described in Chapter 4.

SNAP also has positive impacts on participants' health. SNAP improves health at birth; it has been shown to increase birthweights and reduce the incidence of low birthweight (Almond et al., 2008; East, 2020). SNAP access before age 5 improves health in adolescence (as reported by a parent), potentially through reduced school absences, doctor visits, and hospitalizations (East, 2020). Children with access to SNAP also have better health in adulthood, as measured by obesity, body mass index, and the absence of chronic conditions, such as diabetes and high blood pressure (Hoynes et al., 2016). Access to SNAP during childhood also affects education and economic outcomes, increasing high school graduation rates by 18 percentage points and improving a range of outcomes for women, including earnings, employment, family income, and educational attainment (Hoynes et al., 2016; Northwestern Institute for Policy Research, 2017), which may contribute to better health outcomes.

Participation in WIC has also been found to also improve birthweight and reduces the incidence of low birthweight (Currie and Rajani, 2015; Figlio et al., 2009; Hoynes et al., 2011; Rossin-Slater, 2013). In contrast, when local clinics close or stores end their participation in WIC, expectant mothers' participation declines, harming birth outcomes (Meckel, 2020; Rossin-Slater, 2013). Prenatal WIC participation reduces diagnoses for attention-deficit/hyperactivity disorder and other childhood mental health conditions and grade repetition (Chorniy et al., 2020).

Implications of the Research

The broad-ranging positive impacts of antipoverty programs underscore the importance of ensuring they are administered in a manner that allows eligible families to enroll and receive assistance. Although antipoverty programs are neutral in terms of racial and ethnic health equity, they do have indirect equity implications. Research and experimentation will be needed to facilitate full and equitable access to federal programs administered by states and localities. In addition, community organizations can play an important role in helping people sign up for and remain enrolled.

Monitoring Participation Rates can Facilitate Equitable Access to Federal Programs

Participation rates vary widely overall and across groups in social benefits programs. Although overall participation in SNAP has been high in recent years, it varies widely across groups. Recent estimates indicate that participation among Hispanic families lags far behind other groups (Bitler et al., 2023). Fewer than half of eligible older adults participate (Finkelstein and Notowidigdo, 2018). The Elderly Simplified Application Project is a federal demonstration project that allows streamlined administrative policies for elderly SNAP participants and has been shown to increase participation (Gothro et al., 2020; USDA, 2020a; Waxman, 2021); expanding it would increase participation in SNAP among older adults. Additionally, although data are limited, they indicate that outreach efforts, which could be incentivized by the federal government, may improve SNAP participation (Bleich et al., 2020; Gorman et al., 2013; Mabli, 2015).

WIC is very successful in its efforts to provide infants with access to needed food and breastmilk or formula, but it falls short along other dimensions. Participation rates decrease significantly as children age; 79.3 percent of eligible infants participate, dropping to 25 percent by age 4 (Gray et al., 2019; Schanzenbach and Thorn, 2020). Increasing WIC take-up could help improve outcomes for children and reduce racial and ethnic disparities in maternal and child health and food insecurity. Participation could be improved by establishing performance metrics for cross-enrollment of eligible SNAP and Medicaid participants into WIC, similar to the metrics for the National School Lunch Program (see Chapter 4). This could provide additional incentives for states to do the crucial outreach and institute the appropriate reforms in application and related processes needed to maintain high participation.

Estimates suggest that about 15 million households did not file a federal income tax return in 2019, for reasons such as not being required to do so based on their types and amounts of income (Gleckman and Maag, 2020). Increasing tax filing rates would likely increase participation in EITC and CTC (Goldin et al., 2022). The Internal Revenue Service (IRS) can take steps to increase filing rates, such as using data from administrative records to send out prepopulated tax returns and continuing the “simplified filing” process that allows families with very low incomes to provide a limited set of data to establish tax benefits without having to file full tax returns (Tilly, 2022). States could also help to identify nonfilers by comparing SNAP and Medicaid rolls to the tax filing, so that nonfilers could be offered targeted assistance with tax filing.

Improving Administrative Capacity is Another Promising Approach to Facilitate Equitable Participation in Federal Programs

Administrative churn in programs such as SNAP occurs when otherwise eligible participants fail to recertify, are removed, and reapply as a new case within a short period, such as a few months or a year. Churn drives up administrative costs, because new cases are more expensive to process than recertifications. Churn also reduces the effectiveness of social programs when families lose benefits due to administrative burdens (Homonoff and Somerville, 2021). To address this problem, the federal government could monitor rates of churn and set performance standards. In addition, administrative burdens could be streamlined, and program integrity and effectiveness could be improved through federal investments in expansion of administrative capacity.

For SNAP, administrative burdens reduce recertification among eligible individuals (Homonoff and Somerville, 2021). Reforms that simplify recertification can increase retention (Gray, 2019). Federal policy could authorize, without the need for states to apply for waivers, administrative procedures that make it easier to enroll in and stay on SNAP, as was done during the COVID-19 federal health emergency. These included the extension of certification periods, reduced paperwork and interview burdens, telephonic signatures, and electronic filing of paperwork (CBPP, 2023b).

Maintaining Broad Eligibility Facilitates Equitable Participation in Federal Programs

In recent years, eligibility for antipoverty programs among some groups has been limited by some laws and regulations. Because of the many demonstrated benefits of these programs, any such limitations need to be carefully considered and their potential negative impacts, especially those that might disproportionately affect racial and ethnic minority groups, appropriately weighed. For example, a new “public charge” rule⁸ took effect in 2020 (but was reversed in 2021) that significantly expanded the criteria for denying permanent residency, or green cards, to those who have or likely would participate in antipoverty programs. The rule caused eligible immigrant families to avoid enrolling for fear of consequences (Bernstein et al., 2020, 2021). When legal immigrants were barred from SNAP (then the Food Stamp Program) after the 1996 welfare reform law,⁹ studies show that immigrant children's health worsened (East, 2020) and immigrant adults were less likely to have doctors' appointments (East and Friedson, 2020). Additionally, individuals convicted of drug-related felonies are permanently barred from SNAP, unless the state modifies or eliminates the ban. Denying food assistance to individuals who have completed their sentences makes it harder for them to reintegrate into society and increases recidivism rates (Tuttle, 2019). This ban disproportionately affects people who have been racially and ethnically minoritized (Bolen, 2021) and may also impact health inequities.

Support Nonprofit Sector Partnerships

Nonprofit sector partnerships play an important role in poverty alleviation and emergency food assistance that can influence racial and ethnic health inequities. Federal programs, such as the Emergency Food Assistance Program, help the nonprofit sector more effectively serve those in need by providing food for distribution and grants for infrastructure and capacity building (Cabili et al., 2013; Tiehen, 2002; USDA, 2020b). It is important to ensure that such programs provide an adequate supply of nutritious and culturally relevant foods. Additional targeted grants to invest in elements such as administration and storage capacity for perishable foods, could further improve the reach and impact of the nonprofit sector.

Conclusion

Inadequate, unstable income harms a range of downstream outcomes, from children's educational achievement to health outcomes and neighborhood characteristics. It also reduces wealth accumulation—for example, data demonstrate that homeownership rates increase with household income (see Figure 3-8)—and harms families' abilities to weather economic shocks. Racial and ethnic inequities in wealth will be discussed in the next section of this chapter.

FIGURE 3-8

Homeownership rate by household income in 2001. SOURCE: Herbert et al., 2005.

Conclusion 3-3: Federal social benefit programs, such as the Supplemental Nutrition Assistance Program, Special Supplemental Nutrition Program for Women, Infants, and Children, and the Earned Income Tax Credit, significantly alleviate poverty and reduce the negative health consequences of poverty; however, there are barriers that prevent participation among many people who would otherwise qualify for these programs. Some racial, ethnic, and tribal populations have lower participation rates in these programs, contributing to racial and ethnic health inequity. Therefore, policies that address administrative barriers, hold programs accountable for participation rates, and improve administrative capacity can improve participation rates and reduce racial and ethnic health inequity.

Conclusion 3-4: Federal social benefit programs, such as the Supplemental Nutrition Assistance Program, Special Supplemental Nutrition Program for Women, Infants, and Children, and the Earned Income Tax Credit, significantly alleviate poverty and reduce the negative health consequences of poverty. In some cases, eligibility for these and similar programs has been restricted for some groups, including childless adults, formerly incarcerated individuals, and immigrants. Because these groups disproportionately represent racially and ethnically minoritized populations, these restrictive policies contribute to racial and ethnic health inequity.

Conclusion 3-5: Nonprofit sector partnerships play an important role in poverty alleviation and emergency food assistance that can influence racial and ethnic health inequities. Federal programs, such as the Emergency Food Assistance Program, help the nonprofit sector more effectively serve those in need by providing food for distribution and grants for infrastructure and capacity building.

WEALTH

Wealth, which is typically defined as what you own minus what you owe, is responsible for a significant component of economic inequality (Oliver and Shapiro, 2006; Sherraden, 2005). Race (along with education and age) increasingly determines whether and to what extent one is able to accumulate wealth (Boshara et al., 2015a,b,c). Many experts attribute a substantial component of wealth disparities to the U.S. legacy of slavery and racist economic policies (Siripurapu, 2022). For example, homeownership is the primary route through which people in the United States amass wealth, and for decades, Black people's ability to participate was severely limited by federal policies. The Home Owners' Loan Corporation (HOLC), a federal agency established in the 1930s, mandated redlining, a discriminatory practice that denied mortgages to residents of certain areas based on their race or ethnicity. The Federal Housing Administration (FHA) and HOLC, in tandem with real estate organizations and developers, are widely considered to be responsible for the discriminatory practices that shaped housing markets for decades (Rothstein, 2017). Freund (2010) found that “following the rules that governed FHA practice nationwide, the Detroit area office focused almost exclusively on promoting the construction, purchase, and repair of privately owned homes by certain White people. There is no evidence that Black people qualified for FHA-insured loans before World War II” (pp. 134–135). Other research shows similar situations in Peoria, Illinois, Greensboro, North Carolina, and Baltimore, Maryland (Fishback et al., 2022). Land is a similar form of wealth. The 1887 Dawes Act¹⁰ authorized the federal government to break up tribal lands and allowed American Indians who accepted the division to become citizens. Through this process, 90 million acres of land were seized and sold to non-AIAN people. Only about one-half of the AIAN population received citizenship through the Dawes Act (Rollings, 2004). As discussed in Chapter 7, territorial dispossession resulted in a “near total” reduction in tribal land (Farrell et al., 2021).

Policies such as the mortgage tax deduction and the GI Bill¹¹ were designed to create opportunities for asset accumulation. However, these opportunities have not been equally accessible to all. They enabled wealth generation for (mostly) White people but effectively left Black and other minoritized families even further behind. The 1944 GI Bill, which provided U.S. veterans with funds for education, housing, and unemployment insurance, disproportionately aided wealth building for White veterans. Turner and Bound (2003), for example, show that, among men limited to educational opportunities in the South, where segregation restricted opportunities for Black people, “the GI Bill exacerbated rather than narrowed the economic and educational differences between blacks and whites.” Black veterans were also largely shut out of the homeownership opportunities that the GI Bill granted to White veterans because the bill only provided veterans with a cosigner for loans. They had to first secure a loan from a bank or other lending agency, which was more difficult for Black veterans due to discrimination. They also had to offer collateral for the loan (Onkst, 1998). Similarly, by not accounting for existing inequities, other policies such as the mortgage tax deduction, which rewards existing wealth in the form of homeownership, can exacerbate rather than close gaps.

Today, Black households score consistently lower than White households in nearly every national metric of wealth—asset ownership, intergenerational wealth transfers, and home value (Addo and Darity, 2022; Bhutta et al., 2020; Thompson and Suarez, 2019). Black and Latino/a families are five times less likely to receive a significant inheritance than White families (McKernan et al., 2012). AIAN people living on trust lands (reservations) have very specific probate laws (Trust and Will, n.d.). It is very difficult to know inheritance rates for AIAN people off reservations (87 percent of the AIAN population) (OMH, 2023), as these data are not gathered or reported. Figure 3-9, from the Hamilton Project's 2020 report, illustrates the income and wealth gaps between Black and White households (Hardy and Logan, 2020). Research that centers the systemic barriers to asset accumulation is necessary to understand the factors that contribute to the gap and devise strategies to close it. Additionally, although economic factors play a large role in the existence and growth of racial economic inequality, political and cultural aspects also have an effect (Piketty, 2013). Research also needs to consider the nuanced role of such political and cultural aspects in perpetuating the racial wealth gap.

FIGURE 3-9

Median family income and net worth in 2018, by race. SOURCE: Hardy and Logan, 2020.

The Connection Between Health and Wealth

The connection between income and health has been firmly established, as the preceding sections discuss. The link between wealth and health has been less intensively studied, partly because data on wealth are more complex and inconsistent than data on income. However, a growing body of research illustrates that higher wealth predicts better health outcomes (Braveman et al., 2018). A 2007 review of studies examining the connection found that, in most of the studies examined, greater wealth was associated with better health (Pollack et al., 2007). Wealth operates in tandem with income to enable healthier living conditions, access to quality health care, and amelioration of stress. A 2018 report from the Robert Wood Johnson Foundation argues that wealth affects health in three ways:

1.

Wealth and income can lead to better health by providing material benefits, including healthier living conditions and access to health care.

2.

Wealth and income can promote health by providing psychosocial benefits, including protection from chronic stress.

3.

Parents' wealth shapes their children's educational, economic, and social opportunities, which in turn shapes their children's health throughout life (Braveman et al., 2018, pp. 6–8) (see Figure 3-10).

FIGURE 3-10

Intergenerational transmission of wealth and health. SOURCE: Braveman et al., 2018.

The COVID-19 pandemic has further revealed the way in which wealth and health interact. Black people already had poorer health conditions than White people when the pandemic struck, as other chapters of this report document. Black families' lack of assets, including emergency funds, bank accounts, and homeownership, made them less able to cope with unanticipated expenses, such as medical emergencies, job loss, and transportation issues. They were more likely to fall behind on their mortgage payments than White families and less likely to be able to retire or plan for retirement. In addition to exposing the vulnerability of Black households from a health and wealth perspective, the pandemic has likely also laid the foundation for widening the racial wealth gap (Weller and Figueroa, 2021).

FINANCIAL SERVICES

Credit Scoring

A related barrier to financial health is credit scoring, which privileges those who already possess assets. A credit score is intended to reflect how creditworthy a person is. Scores below about 620 are considered subprime; those above are prime. A minority of people are “prime credits,” with most considered subprime or credit invisible (they have insufficient information from which to create a credit score) (Meni, 2016). Consumers with lower scores are deemed less able to take on and repay debt. As a result, they pay more for credit (in interest and fees). Credit scores are imperfect measures of creditworthiness, especially for certain groups. The data used to compute the scores are limited and often inaccurate. For example, monthly payments for utilities are generally not reflected on credit reports, but mortgage payments are (Chase, n.d.). Credit scores are also based on a relatively narrow definition of what “creditworthy” means. Just as having too much debt can make it harder to get a loan, so can having no debt at all. Using historical information to predict future behavior also has drawbacks. For example, discrimination in access to financial products can result in shorter credit histories for racially and ethnically minoritized people (Rice and Swesnik, 2012). Because the number of years of credit history is one of the largest factors in credit score, scores can remain depressed for minoritized communities for lengthy periods using this backward-looking system (Rice and Swesnik, 2012). Over the past decade or so, credit scores have increasingly been used more broadly. Landlords and employers often use credit scores to screen prospective renters and employees (Servon, 2017). Auto insurance companies use them to determine insurance premiums, sparking a debate about whether they are a proxy for driving risk or being used to screen for income or race (Steeg Morris et al., 2017). This broader use of credit scores, and higher insurance premiums and interest rates on credit cards, auto loans, and mortgages for those with lower scores, amplify challenges for those already disadvantaged by the current system (Consumer Financial Protection Bureau, 2022).

For several years, fintech companies and others have been exploring the use of alternative data to create more accurate pictures of creditworthiness. Those who support this idea argue that including data such as rent, utility, mobile phone, and bank account payments will enable improved assessments of consumers' risk profiles, more timely information, and increased access to lower-cost credit (Kreiswirth et al., 2017). Research has shown alternative data can be as or more predictive than the current system (Berg et al., 2018). Opponents express concern that the kinds of alternative data that have been proposed may be more prone to inaccuracy than the data that are currently used. A fear of discrimination has also been expressed, in the event that a new type of data is closely linked to a factor such as race, gender, or ethnicity (Akinwumi et al., 2021).

Existing credit scores “reflect the social economic disparities that are out there” (Lee, 2022). Many argue that, “although credit scores never formally take race into account, they draw on data about personal borrowing and payment history that is profoundly shaped by generations of discriminatory public policies and corporate practices that limited access to wealth for Black and Latinx families” (Traub, 2021). Differences in credit score by race are substantial; Black people have an average score 57 points lower than White people (see Figure 3-11) (Broady et al., 2021).

FIGURE 3-11

Average credit score by race, 2021. SOURCE: Data from Shift Credit Card Processing, 2021.

One potential solution is to underwrite credit on the basis of a consumer's cash flow, which could replace credit scoring and credit reporting with information on the daily balance of a person's primary bank account. Measuring bank account balance has been shown to be as or in many cases more predictive than credit scores (FinRegLab, 2019). Because cash flow underwriting uses a more current view of a consumer's economic situation, it can be more financially inclusive, providing credit to individuals who would not qualify on the basis of their traditional score (PYMNTS, 2018). There are many impediments to using cash flow or any alternative to credit scoring, including the reliance on credit scores for a variety of regulatory and market functions. Some have described credit scores as the out-of-tune oboe the financial orchestra has tuned into (Klein, 2020).

The Office of Comptroller of the Currency (OCC) has created Project REACh in part to examine disadvantages caused by the credit scoring system, which it estimates impact nearly 50 million people, and identify alternatives and solutions (OCC, n.d.). Other regulators could work similarly or in conjunction with the OCC to identify specific regulatory barriers and remove or alter them to enhance financial inclusion. Congress could help regulators prioritize improvement of the credit scoring system via oversight hearings, letters, or by requesting the Government Accountability Office identify regulatory barriers to alternatives and propose solutions. These and other efforts to use alternative credit scoring methods could enhance financial inclusion and increase economic well-being and health outcomes for low-income individuals and racially and ethnically minoritized populations. Action on this issue could also come from the Consumer Financial Protection Bureau, which supervises credit reporting agencies (USAGov, n.d.).

Conclusion 3-8: Unequal access to safe and affordable financial services, including bank accounts and low-cost credit, is a driver of inequities. Enabling the provision of financial services that allow all Americans to spend, save, borrow, and plan will enable greater economic stability and increase health equity for low-income and racially and ethnically minoritized populations.

CONCLUDING OBSERVATIONS

This chapter outlined numerous examples of how federal policy contributes to inequities in economic stability for racially and ethnically minoritized populations and tribal communities. Rather than proposing recommendations for changes to these specific federal policies and programs, the committee offers mainly crosscutting recommendations. However, many existing National Academies reports have evidence-based and promising recommendations for federal action to advance health equity in the area of economic stability (including and beyond the federal policies reviewed in this chapter) that have not been implemented and are still relevant today (see Box 3-1 for examples) (NASEM, 2019a,b).

BOX 3-1

Example Recommendations from National Academies Reports.

Several crosscutting themes the committee identified provide promising strategies to improve economic stability via federal policies so that all communities can thrive. For economic stability, the policy examples discussed highlight the importance of access, eligibility, and accountability to existing legislation. For example, broadening access to programs such as SNAP, WIC, and EITC by addressing administrative barriers, holding programs accountable for participation rates, and improving administrative capacity can reduce racial and ethnic health inequities. Eligibility restrictions for such programs, such as for childless adults, formerly incarcerated individuals, and immigrants, contribute to health inequities because these groups disproportionately represent racially and ethnically minoritized populations. Ensuring accountability to existing legislation, such as CRA, could improve racial and ethnic inequities in income and banking. A lack of data for NHPI and AIAN populations was also a crosscutting theme identified by the committee. The lack of data for NHPI populations in this chapter makes this issue especially apparent.

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