Key takeaways:

• So far this year, at least 13 states have introduced bills weakening child labor protections, and four have enacted them.
• Meanwhile, only three states have introduced bills to strengthen standards in 2026, compared with 15 in 2025.
• Industry-backed attacks on child labor standards have followed four troubling trends: 1) lowering minimum wages for teen workers; 2) weaponizing “youth apprenticeships”; 3) eliminating youth permits; and 4) weakening safeguards for teen child care workers.
• The Trump administration has undermined federal enforcement of child labor standards, even amid rising violations.
• Oregon enshrined current federal child labor standards into state law, offering a replicable model for states to hold the line against potential federal rollbacks.

Many state lawmakers took encouraging steps in 2023 and 2024 to strengthen their child labor standards—in response to high-profile reporting of widespread child labor violations across the U.S. and simultaneous efforts to weaken state child labor standards in the wake of COVID-19. But trends in 2026 suggest that this momentum may be waning despite continued increases in child labor violations. Meanwhile, opponents of strong child labor standards have continued to erode state standards and—in effect—chip away at the basis for federal standards, which have also come under threat.

In fiscal year 2025, more cases of federal child labor violations were uncovered than during any other year since the Great Recession, and hazardous work violations ticked up again after declining in the year prior (see Figure A). The rate of young worker deaths nearly doubled between 2020 and 2024, and at least one minor was killed on the job in the past year. At the same time, enforcement of federal child labor standards appears to have diminished under the Trump administration, which has proposed weakening existing standards. Since Trump was inaugurated in January 2025, the U.S. Department of Labor’s Wage and Hour Division (WHD) has published news releases about only three child labor enforcement actions. In the last year of the Biden administration, WHD published news releases about 26 cases.

Figure A

Amid this growing child labor crisis, a few states are taking necessary action to shore up or strengthen standards, but in far too many states industry-backed attacks are continuing to succeed in rolling back child labor laws.

Oregon enshrined current federal standards into state law, a model other states can emulate

The 1938 Fair Labor Standards Act (FLSA) sets guidelines for the hours and nonhazardous jobs for which employers can hire minors. It sets a floor above which states can adopt and enforce their own stronger standards, but where state standards are weaker, federal law applies. Oregon, the only state to pass a bill strengthening child labor standards so far this year, enacted a law that enshrines into state law FLSA work hours for minors as of January 2026. Prior to the change, Oregon law followed federal hours guidelines for 14- and 15-year-olds,1 but had prevented the adoption of any state guidelines more restrictive than those in federal law (FLSA). The new law locks in current standards and guards against potential future erosion of federal standards, stipulating that Oregon’s minor work hours rules must be no less restrictive than FLSA standards as of January 1, 2026, and giving the state freedom to implement its own higher standards for minor work hours if needed in the future. Other states can propose legislation that enshrines federal child labor standards into state law and can go further by establishing standards that improve upon the existing federal floor.

Three other states proposed bills to strengthen existing standards

In 2026, Maryland, New Jersey, and New York lawmakers also made progress on bills to strengthen state child labor standards, but none have been enacted as of this publication. A 2025 New York bill mandating that minor workers receive information on their workplace rights in order to receive work authorization passed the Senate in March; a New Jersey bill proposes establishing minimum penalties and increasing penalties for certain child labor violations; and a Maryland bill establishing civil penalties and preventing the executive branch from seeking waivers from the FLSA passed the House but was not taken up by the Senate. The Maryland bill’s provision prohibiting FLSA waivers was likely a response to a proposal in Project 2025 that would allow states to opt out of certain FLSA provisions, which would erode workers’ right to federal minimum wage and overtime protections. Next year, lawmakers should recommit to advancing stronger state standards, especially given the distinct possibility that federal standards will come under threat.

Over a dozen state legislatures attempted to roll back child labor standards this year

So far in 2026, at least 13 states have introduced bills that weaken child labor protections, and four have enacted them (see Table 1). In contrast, only three states have introduced bills to strengthen child labor protections in 2026, and only one has enacted such legislation.2 For comparison, 15 states introduced bills to strengthen child labor standards in 2025.

Table 1

Proposals to erode existing standards this year included: weakening protections from hazardous work; implementing or expanding minimum wage exemptions for minors; extending the number of hours employers can schedule minors to work; eliminating the state’s youth employment documentation system; and lowering minimum age requirements for workers in child care centers (see Table 1). Four particularly troubling patterns have emerged in legislation attempting to weaken child labor standards across multiple states:

1. Attempts to lower the minimum wage for teen workers;
2. Attempts to use state legislation on “youth apprenticeship” or “work-based learning” programs as a vehicle for weakening state child labor standards;
3. Elimination of youth work permits or other systems that ensure the documentation of minor employment; and
4. Attempts to lower or remove safety standards and staffing ratios for teen workers in child care facilities.

Lawmakers continued to propose excluding teen workers from voter-approved state minimum wage increases

As in previous years, state lawmakers continued to advance proposals that would subject minor workers to lower minimum wages than adults, particularly in states where successful ballot measures recently increased the state minimum wage. Florida, Missouri, and Nebraska voters approved ballot measures in recent years that increased the state minimum wage to $15 an hour (Florida’s minimum wage will increase from $14 to $15 in September). Legislators in the same three states are now attempting to exclude minor workers from these higher minimum wages.

Florida lawmakers reintroduced a bill to allow minors in work-based learning programs to “opt out” of receiving the constitutionally-mandated state minimum wage; Missouri lawmakers proposed paying minors nearly $3 less than the state’s new $15 minimum wage; and Nebraska lawmakers successfully enacted a bill that increased the state’s temporary youth training wage but also implemented a permanent subminimum wage for 14- and 15-year-olds. Such proposals undermine the stated goals of lawmakers to boost youth employment, address the “labor shortage,” and allow teens to earn for their futures. Youth subminimum wages do not benefit young people and erode the wage floor, depressing wages for all workers—teens and adults alike.

States continued a troubling trend of using unregulated state “youth apprenticeship” programs to roll back child labor protections

As shown in Table 1, three states proposed bills to weaken hazardous work protections for minors enrolled in work-based learning programs, marking a continued trend of attempts to erode standards that ensure early career training programs provide valuable experiences and skills without unnecessarily exposing young people to hazards known to pose a high risk of illness, injury, or fatality.

In Pennsylvania, lawmakers proposed exempting minors enrolled in work-based learning programs from state child labor standards, except where such standards reflect federal law. In Virginia, lawmakers introduced legislation that would allow employers to set the standards for appropriate work in hazardous occupations, undermining existing state laws that require work-based learning programs to be accredited by the U.S. Department of Labor or state Board of Education. However, education advocates managed to neutralize the bill’s harms by removing that provision, limiting the scope of work-based learning programs to particular industries, and adding language requiring such programs to comply with federal laws prohibiting employers from exposing teens to hazardous work.

In West Virginia, lawmakers used their recently created “youth apprenticeship program” to further erode state child labor standards for all minor workers, exacerbating troubling conflicts between state and federal child labor law created by earlier state legislation. In 2024, West Virginia lawmakers established a new “youth apprenticeship program” (YAP) that appears to permit YAP-enrolled minors to be employed in any of the 17 hazardous occupations prohibited by federal law, even though federal law provides limited exemptions for apprentices and student learners for only seven of the 17 hazardous occupation orders. This year, lawmakers expanded the program by removing the requirement that hazardous work assigned to youth apprentices be “occasional and incidental” to their training. This guardrail, which originates in federal law, is meant to protect youth apprentices from being treated as adult workers in hazardous jobs and to ensure that they are assigned hazardous work very rarely and only when it is necessary to further their training.

As part of the same legislation, West Virginia lawmakers also removed from state code the list of hazardous occupations prohibited for minors under state law. As a result, working minors not covered by the FLSA will no longer have protection from being employed in the deadliest jobs, and if federal protections are unenforced or eroded as Trump’s Project 2025 agenda has threatened, all West Virginia minors working in these jobs would lack protection. Many states have their own list of state hazardous occupation orders, which may differ slightly from the federal list. Where state and federal standards differ, the more protective standard prevails. Removing the state’s list will both endanger young workers and create confusion for employers who may not realize they must still follow federal law in areas where state law has been eroded, leading to increased reputational risk and legal liability for the state’s businesses.

Several states have weakened restrictions on hazardous work while eliminating the state’s ability to identify and investigate child labor violations

The West Virginia playbook for rolling back state child labor laws represents a troubling pattern for lawmakers and advocates to continue to monitor and resist. In 2024, the state created a new work-based learning program that did not conform to federal law, then eliminated youth work permits (and replaced them with weaker age certificates, which have now also come under threat), and a year later further weakened the state’s hazardous work protections using their new work-based learning program as a vehicle.

In just the past three years, two other states—Indiana and Iowa—have both eliminated their systems for documenting youth employment while also weakening prohibitions on hazardous work for minors. In 2023, Iowa lawmakers eliminated youth work permits, weakened hazardous work protections for youth enrolled in “work-based learning” programs, and added new provisions allowing state agencies to “waive” restrictions on hazardous work that violated federal law, among a host of other changes.

And this year, Indiana lawmakers eliminated the state’s “youth employment system” for documenting minor employment after implementing the system to replace eliminated youth work permits in 2020. As a result, state agencies will have no record of teen employment—a change the legislature’s own fiscal analysts acknowledged will impede enforcement of child labor laws. Indiana’s 2026 rollback comes on the heels of numerous changes enacted in 2024 that extended work hours for minors, eliminated night work restrictions, weakened protections for hazardous work, and—though this provision was amended out of the final bill—proposed giving employers complete civil immunity for workplace fatalities of minors enrolled in work-based learning programs.

Recent research shows that youth work permits play an important role in preventing child labor violations by enhancing awareness of child labor standards, creating legal accountability, and aiding in enforcement. In 2024, Wisconsin lawmakers passed legislation eliminating work permits for minors under 16, but the governor vetoed the legislation and stated in his veto message that he objected to eliminating a process that protects youth from exploitation. This year, Wisconsin’s Department of Workforce Development uncovered more than 1,600 child labor violations by a single Burger King franchisee—the largest in the state’s history—including 593 work permit violations. Recent research has shown that states with work permit mandates have fewer child labor violations. Employers violating work permit rules are also often violating work hours and hazardous work protections.

Continued efforts to weaken protections for teen child care workers are part of a larger deregulatory agenda in the care industry

This year, for the third time since 2022, Iowa lawmakers proposed legislation to weaken standards related to teen supervision of children in child care facilities. In 2022, Iowa enacted a bill that lowered the minimum age for child care workers and increased the number of children facilities could place under the care of a single staff person. In 2024, lawmakers proposed allowing a 16-year-old to be charged with the care of four infants, seven toddlers, or 10 three-year-olds without direct supervision, but the bill failed. The bill was supported by the billionaire-founded right-wing dark money group Americans for Prosperity, which also lobbied in support of a 2023 Kansas bill to allow minors as young as 14 to care for young children and allow 16-year-olds to provide child care with no adult supervision.

In 2025, Iowa enacted additional changes through the administrative rulemaking process, allowing teenagers as young as 16 to care for children of any age in limited circumstances. And this year, lawmakers proposed allowing 15-year-olds to care for children without supervision. The 2026 Iowa bill received significant support from lobbyists representing The Family Leader and Family Leader Foundation, Iowa’s state affiliate of the Family Research Council, an anti-LGBTQ and anti-abortion hate group.

Michigan also issued new administrative rules effective April 2026 that increased child-to-staff ratios and allow 16-year-olds to care for numerous young children without supervision—in both group and family child care homes.

The push to lower the minimum age for child care providers and increase child-to-staff ratios is part of a larger industry agenda to deregulate the care economy and avoid reckoning with its true costs. This agenda—which has also involved reducing the education and experience requirements necessary for provider licensing and even using state power to block stronger local standards—has strained providers, degraded the quality of care, and led to injuries and even deaths of young children in recent years. Placing the burden of responsibility of caring for young children on teenagers who are still themselves children harms everyone while sidestepping the real issues facing our child care system: insufficient public investment to make child care affordable and to pay providers adequately.

In an era of federal retrenchment and continued state rollbacks amid rising violations, more state lawmakers should seek to strengthen standards

Though the news media has largely moved on and federal enforcement attention appears to have waned, child labor violations remain a persistent issue and may be getting worse. Fiscal year 2025 saw more child labor cases generally and more minors employed in violation of hazardous occupation orders than any year in recent memory. While some states continued advancing legislation to strengthen child labor standards in 2026, and Oregon succeeded in enacting legislation to guard against federal rollbacks, far more states focused their efforts on weakening existing standards.

Given the very real risk that aspects of FLSA child labor protections could be eliminated (or will go unenforced), all states should at a minimum lock in existing FLSA standards and ensure state capacity to enforce them. Beyond this, states have critical opportunities and responsibilities to modernize child labor standards beyond the minimal, outdated FLSA floor to ensure that minors who must work or choose to work can access safe work experiences that don’t harm their health or education.

1. Maximum of 3 hours per day, 18 hours per week when school is in session; 8 hours per day, 40 hours per week when school is not in session. See: https://www.dol.gov/agencies/whd/state/child-labor

2. We exclude legislation related to child influencers. At least five states have introduced bills to increased protections for children featured in video content in 2026 (AZ, MD, MO, NJ, TN), and two states (NJ, TN) have enacted such legislation.

The Trump-Vance administration has announced each of these charitable contributions with considerable fanfare, staging press and campaign-style events and promising that U.S. companies and other philanthropists will soon follow. What is often missing from the White House celebrations is an explanation of how exactly these gifts and the new Trump Accounts will alleviate child poverty and inequity.

The truth is that the U.S. falls behind peer countries from the developed world in its fight against child poverty. These deprivations are particularly harmful to children of color due to the deterministic role that structural racism plays in the American economy. A pretax and voluntary savings vehicle with little government support, and at the mercy of charitable inclinations, will do little for the millions of low-income families who can’t afford to save. In fact, these accounts are poised to compound structural inequities that have persistently delivered disparate outcomes for disadvantaged families. This is because the Trump Accounts fail to adequately account for the scope of child poverty and inequity. They also crudely overlook the root causes of these issues by framing them as the result of insufficient savings.

Child poverty is a defining feature of the U.S. economy

More than 9 million children struggle with poverty in the United States. This amounts to more than 1 in 10 children who live in households with insufficient resources to provide an adequate and dignifying standard of living in the world’s richest country. This is not a new economic reality. Since 2009, child poverty has declined by less than 4 percentage points in the U.S., leaving the country with the second-highest child poverty rate in the rich world—behind only Uruguay, a Latin American country with a significantly lower living standard.

Child poverty in the U.S. is especially harmful to children of color. More than 1 in 5 Black and Hispanic children struggle below the poverty line (see Figure A). Similarly, more than 1 in 6 (16.6%) American Indian and Alaska Native (AIAN) children are under the poverty line. Overall, children of color are more than twice as likely as their white peers to experience material shortcomings. This prolonged exposure to poverty is likely to translate into even broader disadvantages throughout these children’s lives, with relatively poorer outcomes than their more affluent peers in health, education, earnings, and even retirement.

Figure A

Making a significant dent in child poverty in the U.S. will require more than a voluntary savings vehicle backed by a one-time federal government contribution of $1,000 for each of the 3 million children born annually through 2028. This promise under the new Trump Accounts severely understates the problem of child poverty in the U.S. and it carelessly misidentifies the drivers of early deprivation and inequity.

Child poverty is a policy choice driven by a withering welfare state and structural inequities in the labor market

In 2021, the U.S. proved that it deliberately tolerates high poverty rates for children. The expanded welfare state that followed the pandemic led to a historic decline in child poverty. Economic relief measures and expanded access for low-income families to tax credits (like the Child Tax Credit) and basic needs programs (like SNAP) helped reduce the prevalence of child poverty by nearly half between 2020 and 2021. However, all these gains quickly vanished when this enhanced social safety net expired. Today, child poverty is higher than it was in 2019, and it is likely to worsen with major cuts to programs like SNAP and Medicaid the Trump-Vance administration signed into law last summer. These programs lifted 1.4 million and 6.1 million children out of poverty in 2024, respectively.

Alleviating child poverty in the U.S. will also require confronting the low-wage employment regime sustained by a federal minimum wage that officially became a poverty wage in 2025. Low-wage work leaves more than 10 million workers in poverty. The growing divergence between the productivity of the U.S. economy and the earnings of the typical worker leaves millions of workers vulnerable to poverty and economic insecurity. About 67 million workers earn less than $25 per hour, a threshold considerably lower than the hourly earnings of a typical worker had their pay kept pace with productivity.

The defining role of structural racism in the U.S. labor market means that workers of color are more likely than their white peers to be part of the low-wage workforce. These workers of color are also more exposed than their peers to unemployment and lack dependable and consistent income. Compared with less than 1 in 3 (31.5%) of their white peers, more than 2 in 5 Black (43.3%) and Hispanic (46.3%) adults report having difficulties paying their bills due to monthly fluctuations in income. This economic insecurity means that less than half of Black and Hispanic adults have enough savings to cover expenses for three months in case of a job loss or other emergency (see Figure B). A savings vehicle without continued government support for disadvantaged families will only deepen existing inequities for families who can’t even afford to maintain their living standard in the event of an unexpected economic shock. 

Figure B

Trump Accounts distract us from real solutions that lean on the functional power of wealth, a strong labor market and welfare state

Trump’s new savings vehicle for children falls short of the more ambitious Baby Bonds proposed by economists like Darrick Hamilton and William Darity. Since 2021, versions of Baby Bonds legislation have been introduced federally and in several states. Unlike Trump Accounts, national Baby Bonds commit the federal government to a publicly funded account beyond an initial endowment with additional contributions until the child reaches the age of 18. These continued contributions by the federal government would follow a progressive structure, with children from resource-constrained households receiving relatively larger endowments that they can later access at the age of 18 to purchase a home or start a business.  

The continued commitment to a federally funded and progressively seeded account means that Baby Bonds are intentionally designed to narrow racial disparities in wealth. Since families of color only have a fraction of the net worth that their white peers enjoy, the larger public contributions to children from resource-constrained households would disproportionately benefit children of color. This explains why impact assessments show that Baby Bonds can effectively narrow the racial wealth disparities that reflect a long history of economic exploitation and exclusion.

Without these characteristics, Trump’s voluntary savings accounts are poised to widen wealth disparities for generations. The more resource-constrained families will be unable to keep up with the contributions of their more affluent peers—broadening inequities in wealth even further. This is particularly true under the Trump-Vance economy, characterized by sluggish job growth and rising unemployment. Trump’s economy is also one in which food insecure families that rely on nutritional assistance programs like SNAP—and those that rely on Medicaid and CHIP for health coverage—face ever more stringent conditions intentionally designed to limit access to support and to raise questions about the deservingness of social assistance. At this rate, the Trump-Vance administration is looking to make the U.S. the leader of child poverty in the rich world.

This interactive chartbook provides a statistical snapshot of race and ethnicity in the United States, depicting racial/ethnic disparities observed through

• Population demographics
• Civic engagement
• Labor market outcomes
• Income, poverty, and wealth
• Health

The chartbook also highlights some notable intersections of gender with race and ethnicity, including educational attainment, labor force participation, life expectancy, and maternal mortality. The findings are bracing, as they show how much more work we need to do to address longstanding and persistent racial inequities.

Most charts include data for five racial/ethnic groups in each of the charts—white, Black, Hispanic, Asian American and Pacific Islander (AAPI), and American Indian and Alaska Native (AIAN). In the charts and text, “Americans” refers to all U.S. residents, regardless of citizenship status.

Researchers seeking disaggregated data and statistics for AAPI and AIAN groups are encouraged to look at sources cited in the companion essays in the Anti-Racist Economic Research and Policy Guide: AAPI Data and the Center for Indian Country Development at the Federal Reserve Bank of Minneapolis.

As our efforts illustrate, collecting and maintaining data sources that are representative of the entire U.S. population is an essential first step toward overcoming the invisibility, neglect, and lack of understanding experienced by many communities of color. Future work on this project will involve identifying comparable data from alternative sources that fill in as much of the missing information in the chartbook as possible.

In this interactive chartbook, additional notes and source information can be accessed by clicking on the ellipses ( … ) in the notes and sources lines under the charts.

Population demographics

The U.S. has become more racially and ethnically diverse over the last two decades: Share of U.S. population by race and ethnicity, 2000, 2010, and 2020

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Each decennial Census since 2000 has revealed a more racially and ethnically diverse U.S. population. While the share of people who identify as Black (about 12%) or American Indian and Alaskan Native (0.7%) has remained constant, the non-Hispanic white share of the population has declined from 69.1% in 2000 to 57.8% in 2020. On the other hand, a growing share of U.S. residents identify as Hispanic (increasing from 12.5% in 2000 to 18.7% in 2020) or Asian American and Pacific Islander (increasing from 3.7% in 2000 to 6.1% in 2020). These changing population demographics reflect different trends in birth, mortality, and immigration rates across groups. Since 2000, there have also been significant changes in how people identify racially. Notably, a growing share of people identify as being of two or more races (this would include people who, for example, identify as Black and AAPI, but would not include people who identify as Black and Hispanic, as they are identifying Black alone as their race and Hispanic as their ethnicity). Also, a growing but still small share of people identify as being of a race other than those explicitly defined by the Office of Management and Budget (OMB).

As Trevon Logan notes in his essay, it is the OMB that issues regulations regarding the classifications of race and ethnicity by federal agencies, including the U.S. Census Bureau, which conducts the major household and business surveys used by researchers. There are six permitted race categories and two ethnicity classifications, Hispanic and non-Hispanic. As such, everyone is a member of both a race and ethnicity. For more on the current classifications, see Logan’s essay.

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While U.S. residents are overwhelmingly citizens, Asian American/Pacific Islander and Hispanic citizens are more likely to be first-generation immigrants: Share of U.S. population by race/ethnicity and nativity, 2024

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Across all racial and ethnic groups, an overwhelming majority of people in the United States are U.S. citizens, according to data from the Current Population Survey. However, nativity shares vary across racial groups. White persons (95.9%), American Indian and Alaskan Native (AIAN) persons (81.3%), and Black persons (88.6%) are most likely to have been born citizens (born in the United States or to United States citizens abroad), compared with over half of the Hispanic population (66.7%) and more than one-third (37.8%) of the Asian American and Pacific Islander (AAPI) population.

Immigration status also varies widely. AAPI residents are most likely to be immigrants: more than one-third (38.3%) were not born U.S. citizens but became U.S. citizens (i.e., are naturalized U.S. citizens), while another 23.9% are not citizens. Hispanic residents are next most likely to be immigrants: 12.6% are naturalized citizens and 20.7% are not citizens. These statistics highlight only a fraction of the diversity represented within and across different racial and ethnic groups. As several essays in the Advancing Anti-Racist Economic Research and Policy guide explain, analyses that use categories or group descriptions that are too broadly defined can lead to inaccurate conclusions.

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The uneven geographic distribution of racial and ethnic populations highlights the influence of state and local policy on racial inequality: Share of state population by race and ethnicity, 2020

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The U.S. Census Bureau projects that Black, Hispanic, AAPI, and other people who do not identify as white will collectively account for over half of the population of the United States by 2044. In California, Hawaii, Maryland, Nevada, New Mexico, Texas, and the District of Columbia, the white population is already in the minority, and in Arizona, Florida, Georgia, New Jersey, and New York, white persons make up just over half of the population. This interactive map shows areas of population density for each race or ethnic group (click on a race or ethnic group) along with the racial and ethnic distribution of each state’s population (click on a state). It shows that Southern states and the District of Columbia have the largest shares of residents who are Black, with the highest shares in the District of Columbia (40.9%), Mississippi (36.4%), and Louisiana (31.2%). Southwestern and Western states are home to a large percentage of Latinos, with the highest shares in New Mexico (47.7%), Texas (39.3%), and California (39.4%). AAPI residents, including Native Hawaiians, predictably account for nearly half (46.8%) of the population of Hawaii but are also a significant share of the population in California (15.5%) as well as New Jersey and Washington state (10.2% each). Also, as the group’s name would indicate, American Indian and Alaska Native residents account for the highest share of the population in Alaska (14.8%), followed by New Mexico (8.9%), South Dakota (8.4%), and Oklahoma (7.9%). White Americans account for the largest majority of the population in several Northeastern states (90.2% in Maine, 89.1% in Vermont, and 87.2% in New Hampshire) and West Virginia (89.1%).

The patterns illustrated in the map trace each group’s unique history of settlement, immigration, and migration in this country. But they also help to make a point about the important role that state and local policies play in either improving or worsening racial disparities in the United States. As just one example, EPI research shows that Southern states, which have a high density of Black residents, are more likely than states in other regions to use preemption laws to stop local governments from setting strong labor standards, such as raising the minimum wage and guaranteeing paid sick leave.

For more on preemption laws in the South, see Hunter Blair et al., Preempting Progress: State Interference in Local Policymaking Prevents People of Color, Women, and Low-Income Workers from Making Ends Meet in the South, Economic Policy Institute, September 2020.

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Current population demographics by race/ethnicity and age support projections that people of color will become the collective majority by 2050: Share of U.S. population within given age ranges, by race and ethnicity, 2024

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The changing racial and ethnic makeup of the U.S. population is foretold in the age distribution of different racial and ethnic groups. In 2024, over a quarter (28.9%) of people who identified as Hispanic were under the age of 18, as were about a quarter of those who identified as Black (24.5%), American Indian and Alaska Native (AIAN) (27.9%) and a fifth within those who identified as Asian American and Pacific Islander (19.9%). A smaller share of the white population (17.8%) belonged to this younger age cohort while over a third (36.9%) of white residents were near or at retirement age (age 55 or older)—a much larger share than for other racial and ethnic groups. As the current population ages, the older population will remain predominantly non-Hispanic white while Black, Hispanic, AAPI, and AIAN persons will be a growing share of the younger population. This racial and ethnic generation gap will require balancing the interests of a younger, less wealthy, more racially and ethnically diverse population with those of an older, wealthier, predominantly white population. However, these generations are linked in important ways. Older workers and retirees have a stake in worker, economic, and racial justice for those younger workers who in the years ahead will be a growing share of workers driving the national economy and providing many of the services the aging population relies on. Census population projections from 2022 (the latest available) indicate that in 2050, non-Hispanic white persons will account for less than half (48.4%) of the U.S. population (see U.S. Census Bureau, 2023 National Population Projections Tables, Table 4).

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Men’s educational attainment is highly stratified by race and ethnicity, with American Indian and Alaska Native, Hispanic, and Black men most likely to be “working class”: Share of men aged 25 and older within given level of educational attainment, by race and ethnicity, 2024

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The term working class has been used to describe working-age adults who have less than a bachelor’s degree. Based on their high shares without a bachelor’s degree or more education, American Indian and Alaska Native (AIAN) (85.3%), Hispanic (80.9%), and Black (76.5%) men are more likely to be considered working class (under this definition) than are white (60.3%) or Asian American and Pacific Islander (AAPI) (40.7%) men. Even among the groups of men most likely to be considered working class, there is still a wide range of educational attainment that includes everything from less than a high school diploma to some college. The some college category includes attendance at a four-year or two-year institution, but no degree; it also includes completion of a two-year associate or technical degree. The groups with the highest shares of people with less than a high school education are Hispanic men (27.6%) and AIAN men (23.5%) and 57.7% of Hispanic men and over half of AIAN men (58.2%) have no education beyond high school. While about half (47.0%) of Black men also have no education beyond high school, Black men are more likely than either Hispanic or AIAN men to have a bachelor’s or advanced degree, but still much less likely to have that level of education than either white or AAPI men. AAPI men lead all other racial groups in the share (59.2%) who have a bachelor’s or advanced degree. These patterns of educational attainment are shaped by multiple factors, including differences in immigration policies applied to Asian versus Latin American countries, as well as the legacy of racial discrimination and oppression that severely limited educational opportunities for generations of Black and Native Americans.

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Most women have more than a high school education, but Latinas and AIAN women lag behind other groups in attaining higher education: Share of women aged 25 and older within given level of educational attainment, by race and ethnicity, 2024

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In 2024, across most racial and ethnic groups, at least half of women aged 25 or older had some education beyond a high school diploma. Latinas were the exception—only 49.1% had some level of education beyond high school and 24.2% had less than a high school education, a much higher percentage than any other group of women (1.2 to nearly 5 times as much). Those women least likely to have a bachelor’s or advanced degree were American Indian and Alaskan Native (AIAN) women (19.7%) and Latinas (23.9%). Asian American and Pacific Islander (AAPI) and white women had the highest levels of educational attainment with 56.9% of AAPI women and 41.8% of white women having at least a bachelor’s degree, followed by 29.9% of Black women. As with men, these patterns of educational attainment are shaped by multiple factors, including differences in immigration policies applied to Asian versus Latin American countries, as well as the legacy of racial discrimination and oppression that severely limited educational opportunities for generations of Black and Native Americans. But compared with male educational attainment by race and ethnicity women tend to have higher levels of educational attainment (see Chart 5).

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While the Black and AIAN imprisonment rate has decreased, Black and AIAN people are still five times as likely as white people to be imprisoned: Imprisonment rates per 100,000 U.S. residents by race and ethnicity, 2012–2022

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In response to the demand for criminal justice reform and a shift away from the “tough on crime” politics of the 1980s and 1990s, imprisonment rates for Black, American Indian and Alaska Native (AIAN), Hispanic people have fallen over the last decade. But Black, AIAN, and Hispanic people are still much more likely to be incarcerated than white people, whose imprisonment rate has stagnated over the past decade. Over 1,000 out of every 100,000 U.S. residents who are Black or American Indian and Alaska Native (AIAN) were imprisoned in 2023, followed by 603 out of 100,000 Latino U.S. residents, 229 out of 100,000 white U.S. residents, and 88 out of 100,000 Asian American and Pacific Islander U.S. residents. Thus, the approximately 1.8 million people held in U.S. prisons at the end of 2022 —an often-forgotten segment of the U.S. population—are disproportionately Black, AIAN, Hispanic, and other people of color. 

Data on the size of the overall incarcerated population come from the “Correctional Populations in the United States, 2022—Statistical Tables” published by the U.S. Department of Justice in May 2024. 

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Black and AIAN men have an exceptionally high imprisonment rate: Imprisonment rates per 100,000 U.S residents, by race/ethnicity and gender, 2022

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This chart makes two facts very clear: That imprisonment in the United States is not only a gendered issue—with men being much more likely to be imprisoned—but also an issue of racialized gender, with Black and American Indian and Alaska Native (AIAN) men being far and away the most highly imprisoned group. Among women, AIAN residents had the highest imprisonment rate (173 per 100,000), followed by Black residents who had an imprisonment rate (64 per 100,000) in 2022. AIAN women were almost three times as likely to be imprisoned as Black women, around four times as likely to be imprisoned as White and Hispanic women, and 34 times as likely to be imprisoned as AAPI women. Among men, Black residents had the highest imprisonment rate (1,826 per 100,000), followed by AIAN men (1,443 per 100,000). Black men were more than twice as likely to be imprisoned as Hispanic men, more than five times as likely to be imprisoned as white men, and almost 13 times as likely to be imprisoned as AAPI men. AIAN men were almost twice as likely to be imprisoned as Hispanic men, more than four times as likely to be imprisoned as white men, and more than ten times as likely to be imprisoned as AAPI men.

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Civic engagement

Consistently higher turnout among white voters was challenged by historic Black voter turnout in 2012 and, to a lesser extent by historic Hispanic and Asian voter turnout in 2020: Voter turnout in presidential election years by race and ethnicity, select years 1992 to 2024

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The right to vote is the most powerful right of U.S. citizenship—and widespread voter participation is essential to a functional democracy. Yet many U.S. citizens ages 18 and older do not vote. Data on voter participation during presidential election years since 1992 reveal that turnout varies significantly by race and ethnicity and changes over time. Since 1992, voter turnout has typically been highest among white voters—ranging from 60.7% to 70.9%—although Black voter turnout saw a huge increase in 2008 and 2012 during the election and reelection of the nation’s first Black president, Barack Obama. In fact, 2012 was the only election in which Black voter turnout (66.2%) exceeded white voter turnout (64.1%). Hispanic and Asian voter turnout was less than 50% in all presidential election years between 1996 and 2016, until both groups had the largest turnout in decades in 2020 (53.7% and 59.7% respectively). In the 2024 presidential election, voter participation declined among Black, Hispanic and AAPI adults. While one’s personal decision to participate in an election can be influenced by any number of factors—including enthusiasm about a particular candidate or confidence in the democratic process—rampant forms of voter suppression in some states undoubtedly contribute to these disparities as well.

For more on the impact of state laws that limit access to voter registration, revoke the right to vote for returning (formerly incarcerated) citizens, or otherwise make it more difficult for certain populations to cast a ballot, see “State Voting Laws,” Brennan Center for Justice, accessed May 5, 2022;  “State Voting Rights Tracker,” Voting Rights Lab, accessed May 5, 2022.

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Amid dramatic decline in union membership since the 1970s, Black workers have held onto the highest rate of union membership for decades: Union membership rates, by race and ethnicity, 1973–2024

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Like the constitutional right to vote in civil society, union membership gives workers a voice—in this case, a voice at work. But as the chart shows, since 1973, union membership has declined for all racial and ethnic groups. Union membership is an important metric of the state of the American worker given the role that labor unions play in giving workers a stronger, collective voice to advocate for higher pay, better benefits, and training and promotional opportunities, as well as protections against discrimination and harassment. In a unionized workforce, for example, collective bargaining results in labor contracts that help to create greater transparency through clearly defined policies and pay structures. These contracts help reduce the potential for pay discrimination by limiting an employer’s discretion in paying different wages to comparably qualified individuals doing the same job and by providing workers with critical protections and direct recourse against other forms of exploitation or mistreatment. The benefits of union membership are a likely contributor to the higher union membership rate of Black workers, given their long history of unequal treatment relative to other groups of workers. Between 1973 and 1980, Hispanic workers also had higher rates of union membership than white workers. While the subsequent across the board decrease in union membership has brought union membership rates by race and ethnicity closer together, in 2024, Black workers were still more likely to be union members (11.7%) compared with white workers (10.0%), Asian American and Pacific Islander workers (8.9%), and Hispanic workers (8.5%).

Still, the labor movement, like any other U.S. institution, is not immune to racism. Unions must continue to become more diverse, inclusive, and dynamic as they serve the vital role of leveling the playing field for all workers.

For more on the benefits and protections conferred by union membership, see Celine McNicholas et al., Why Unions Are Good for Workers—Especially in a Crisis Like COVID-19, Economic Policy Institute, August 2020 and Valerie Wilson, “The Costs of Racial and Ethnic Labor Market Discrimination and Solutions That Can Contribute to Closing Employment and Wage Gaps,” testimony before the U.S. House of Representatives Select Committee on Economic Disparity and Fairness in Growth, January 20, 2022.

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Labor market

Black women have maintained the highest labor force participation rate amid post-1970 rise in women’s labor force participation overall: Labor force participation rate for women by race and ethnicity, 1973–2024

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The labor force participation rate is an important indicator of economic well-being. It shows the number of people in the labor force—people who are employed or unemployed but looking for work—as a share of the number of civilian, noninstitutionalized people ages 16 and older. Across racial and ethnic groups, women’s labor force participation rose significantly from the 1970s through the 1990s for a number a reasons: increased access to higher education, and the introduction and widespread availability of the birth control pill, to name a few. After leveling off during most of the first decade of the 2000s, labor force participation by women declined during or after the Great Recession of 2007–2009. And it declined again during the 2020 COVID-19 pandemic and recession as the burden of job losses and care responsibilities disproportionately impacted women. In 2024, Black women had the highest labor force participation rate at 60.5%, followed by Hispanic (58.9%), Asian (58.6%), white (56.7%), and American Indian and Alaska Native women (55.1%). While Latinas have historically had the lowest rates of labor force participation among women, their labor force participation rate had been climbing steadily in the four years leading up to the COVID-19 pandemic. Historically, Black women have had stronger labor force attachments than other groups of women. This is part of the legacy of being forced to work as enslaved people, but the necessity of work has continued for Black women who are often co-breadwinners if not sole earners for their households.

For more on the rise of women’s labor force participation from the 1970s see Elisabeth Jacobs and Kate Bahn “Women’s History Month: U.S. women’s labor force participation”, Washington Center for Equitable Growth, March 22, 2019.  

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Hispanic men have maintained the highest labor force participation rate even as labor force participation of all men has declined since the 1970s: Men’s labor force participation rate by race and ethnicity, 1973–2024

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Across all racial and ethnic groups, men’s labor force participation rates have declined significantly since the 1970s, with the sharpest decline occurring during and since the Great Recession of 2007–2009. While this trend in part reflects an aging population with a growing share of retirees, researchers have suggested that labor force participation has fallen among prime-age men (ages 25–54) due to a rise in serious health conditions that are a barrier to work, the emerging opioid crisis, or technological changes that encourage younger men  (under age 30) to allocate less time to work and more time to leisure activities like playing video games. Unlike with Black women, who have the highest labor force participation rate among women, Black men in 2024 had the lower labor force participation rates than white and Asian men (65.9%). And unlike with Hispanic women, who have historically had the lowest labor force participation rates among women, Hispanic men have had the highest labor force participation rate, which reached 75.5% in 2024. The ranking of men’s labor force participation rates by race and ethnicity has remained constant over the last three decades.

For more on the likely reasons for declining male labor force participation see Alan Krueger, Where Have All the Workers Gone? An Inquiry into the Decline of the U.S. Labor Force Participation Rate, Brookings Papers on Economic Activity, September 2017; and Mark Aguiar et al., “Leisure Luxuries and the Labor Supply of Young Men,” National Bureau of Economic Research Working Paper 23552, June 2017.

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Black and AIAN unemployment is consistently higher than unemployment of all other racial and ethnic groups: Annual unemployment rate by race and ethnicity, 1979–2024

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Relative rates of unemployment by race and ethnicity have been remarkably consistent over time. Typically, the annual unemployment rates of American Indian and Alaska Native (AIAN), Black, and Hispanic workers are significantly higher than those of white workers. The difference between Asian and white unemployment rates is smaller, and the size of the gap fluctuates, as does which group has the lower unemployment rate. In 2024, this pattern held, with an unemployment rate of 6.5% for AIAN workers, 6.0% for Black workers, followed by 5.1% for Hispanic workers, 3.6% for white workers, and 3.5% for Asian workers. While 2023 saw historical low rates for Black unemployment, one of the most enduring features of the U.S. labor market is the roughly 2-to-1 ratio of the Black and white unemployment rates.

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Higher education typically lowers a worker’s chances of being unemployed but does not eliminate racial and ethnic disparities in unemployment rates: Unemployment rate by race/ethnicity and educational attainment, 2024

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A breakdown of unemployment rates by race, ethnicity, and education level shows the limits of educational attainment as a factor in addressing inequitable economic outcomes. As the chart shows, racial and ethnic disparities in unemployment rates exist at every level of educational attainment. And Black workers have the highest rates of unemployment among all groups without a college degree. In fact, even at historically low rates of unemployment in 2024, only the most highly educated Black workers approached anything near unemployment rate parity with their white counterparts. The figure also shows that while education can contribute to better outcomes—unemployment rates are lower for all groups at higher levels of education—education alone does not necessarily create equal outcomes. Reading this chart alongside Chart 13 suggests that differences in the average unemployment rates of racial and ethnic groups can only be partially explained by relative differences in education, skill, experience or local labor market conditions—discrimination remains an undeniable factor.

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Black, Hispanic, and AIAN workers earn lower wages and have smaller gender wage disparities than their white and AAPI counterparts: Median wages by race/ethnicity and gender, 2024

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There are sharp differences in the wages earned by typical workers of different racial groups in the United States. Asian American and Pacific Islander (AAPI) and white workers are paid the highest wages at the median, while Black, Hispanic, and American Indian and Alaska Native (AIAN) workers are paid significantly less. The gender differences are also greater among AAPI and white workers than among Black, Hispanic and AIAN workers. While AAPI and white men far out-earn AAPI and white women, Black and Hispanic men and women have much more similar median wages.

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Even after controlling for education and other factors known to affect earnings, women—particularly Black and Hispanic women—are paid far less than white men: Regression-adjusted hourly wage gaps for women relative to non-Hispanic white men, by race and ethnicity, 2024

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Women of all racial and ethnic groups in the U.S. have a significant pay penalty by virtue of their gender, even when we account for several factors that could reasonably influence a worker’s productivity or wage rate, including education, marital status, age (a measure of potential experience) and geographic area (a measure of local labor market conditions). Black and Hispanic women face an additional pay penalty by virtue of their race or ethnicity. The chart depicts these wage gaps, presented as how much less women make than non-Hispanic white men. The fact that Black and Hispanic women earn about a quarter less than white men on average when calculating regression-adjusted wage gaps mean, then, that the pay penalty is not a result of differences in formal education between those groups of women and white men. One partial explanation for these wage disparities is occupational segregation, by which women of color are more highly concentrated in occupations with low pay, even relative to their education level. However, women of all races and ethnicities also often earn less than men in the same occupation (not shown in the chart), an indication of potential pay discrimination.

For more on occupational segregation and on gender pay gaps by occupation, see Jessica Schieder and Elise Gould, Women’s Work” and the Gender Pay Gap: How Discrimination, Societal Norms, and Other Forces Affect Women’s Occupational Choices—and Their Pay, Economic Policy Institute, July 2016; Emily Carew and Valerie Wilson, “Latina Equal Pay Day: Latina Workers Remain Greatly Underpaid, Including in Front-Line Occupations,” Working Economics Blog, Economic Policy Institute, October 20, 2021; Valerie Wilson, “Black Women Face a Persistent Pay Gap, Including in Essential Occupations During the Pandemic,” Working Economics Blog, Economic Policy Institute, August 2, 2021.

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Income, poverty, and wealth

Racial and ethnic disparities in median household income have been largely persistent across time: Inflation-adjusted median household income (2024 dollars), by race and ethnicity, 1972–2024

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In the United States, households of different racial and ethnic backgrounds bring in significantly different amounts of income and have done so for decades. At the median, Black, Hispanic, and American Indian and Alaska Native (AIAN) households earn the least on an annual basis, while Asian and white households earn the most. It is notable, though, that in 2023, Black households had the highest household income on record and experienced the largest increase in income between 2020 and 2023. Significant gaps in employment opportunities (shown in Chart 13) and lower wage levels (shown in Chart 15) translate into lower incomes among Black, Latino, and AIAN households. Household income is also a function of the number of earners in a household. Though not shown here, past EPI research found that in the pre-pandemic economy, about a third of Black nonelderly households (where the head of household is age 18–64) had two or more earners, compared with nearly half of white and Hispanic nonelderly households. This racial disparity in the number of household earners is not just a function of how many working-age adults live in the household, or family structure, but is another measurable consequence of the persistent 2-to-1 ratio between the Black and white unemployment rates (shown in Chart 13). As income inequality in the United States has increased in general over the past 50 years, disparities between the least and most well-off groups have continued to persist and, in some cases, have grown.  

For more on earners per household by race, see Elise Gould and Valerie Wilson, Black Workers Face Two of the Most Lethal Preexisting Conditions for Coronavirus—Racism and Economic Inequality, Economic Policy Institute, June 2020. For more on increasing income inequality, see Elise Gould, “Decades of Rising Economic Inequality in the U.S.,” testimony before the House of Representatives Ways and Means Committee, Washington, D.C., March 27, 2019.

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Black and AIAN households are more likely to have the lowest annual incomes—under $25,000 per year in 2024: Share of households within given income range by race and ethnicity, 2024

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This chart extends beyond the data on median or midpoint of household income shown in Chart 17 to provide a more detailed look at where different groups fall across the entire household income distribution. In 2024, 22.9% of Black households, 23.3% of American Indian and Alaska Native households, 15.1% of Hispanic households had annual incomes under $25,000, compared with just 11.4% of white households and 9.3% of Asian households. This $25,000 figure is well below the 2024 official poverty threshold for a family of two adults and two children ($31,812). Conversely, 29.3% of Asian households and 17.8% of white households had annual incomes at or above $200,000—the highest income category—compared with only about 6%-10% of Black, AIAN, and Hispanic households.  

Poverty threshold data can be found in the U.S. Census Bureau’s Poverty in the United States: 2024 data tables, published September 09, 2025 

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Persistently elevated AIAN, Black, and Hispanic child poverty rates have thwarted progress reducing overall child poverty in the U.S.: Child poverty rates, by race and ethnicity, 1974–2024

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A cruel and unfortunate reality of structural racism in the U.S. economy is that even in the “best” of economic times, Black, American Indian, and Alaska Native (AIAN), and Hispanic children experience much higher rates of poverty than white children. In 2024, 30.5% of AIAN children, 25.4% of Black children and 20.2% of Hispanic children lived below the official poverty threshold, compared with just 8.2% of non-Hispanic white children 6.4% of Asian children. While child poverty has fallen significantly for Black, Hispanic, and Asian American children over the past 40 years, Black and Hispanic child poverty rates remained over 20% in 2024. Additionally, in 2024, AIAN children had the highest rates of child poverty at over 30 percent (30.5%). This large and persistent disparity in child poverty combined with the fact that Black and Hispanic children have become an increasing share of the underage 18 population over time (see Chart 1 and Chart 4) has resulted in very little change in the overall child poverty rate since 1974. Given the long-term effects of exposure to poverty in childhood, addressing these persistent disparities must play a role in our approach toward building equity and moving the needle on child poverty.

For more on the long-term effects of exposure to poverty in childhood, see Kerris Cooper and Kitty Stewart, “Does Money Affect Children’s Outcomes? An Update,” CASEpapers (203), The London School of Economics and Political Science, July 2017; Randall Akee et al., “Parents’ Incomes and Children’s Outcomes: A Quasi-Experiment,” American Economic Journal: Applied Economics, January 2010.

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Poverty rates are higher among AIAN, Black and Hispanic working-age adults: Poverty rates for age 18–64, by race and ethnicity, 1974–2024

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While poverty across the working-age population (ages 18 to 64) is lower than that for children (see Chart 19), disparities by race and ethnicity follow a similar trend, with American Indian and Alaska Native (AIAN), Black, and Hispanic adults more likely to be impoverished than white and Asian adults. Poverty is a measure of economic deprivation, and among working-age adults in particular, reflects disparities in unemployment, wages, and income. Life circumstances, such as severe disability and major illness—which can also limit earned income or quickly deplete any available savings—also contribute to poverty for this age group. The racially coded misrepresentation of poverty as some kind of moral or cultural pathology has hindered the political will needed to sustain and strengthen vital income supports that have proven effective in fighting poverty.  

For more on the misrepresentation of poverty as a cultural pathology see William “Sandy” Darity Jr., “Revisiting the Debate on Race and Culture: The New (Incorrect) Harvard/Washington Consensus.” Du Bois Review: Social Science Research on Race 8, no. 2, 467–476. For more on the vital income supports that would lessen poverty see Asha Banerjee and Ben Zipperer, “Social Insurance Programs Cushioned the Blow of the COVID-19 Pandemic in 2020,” Working Economics Blog, Economic Policy Institute, September 14, 2021.

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There are large racial disparities in poverty at older ages (65 and older)—likely reflecting differences in retirement preparedness and/or lifetime income disparities: Poverty rates for people ages 65 and older, by race and ethnicity, 1974–2024

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The poverty seen among older Americans in the chart is most likely the result of a lifetime of low earnings and a lack of retirement preparedness. While research shows that Social Security plays a critical role in keeping poverty rates among older Americans lower than they otherwise would have been (not depicted in the chart), older Black, Hispanic, and American Indian and Alaska Native (AIAN) adults still have relatively high poverty rates. Older Asian Americans are also more likely to live in poverty than older white Americans. Additionally, older Asian Americans have higher poverty rates than younger Asian Americans (see Chart 19 and Chart 20). This is likely due to a larger share of older Asian Americans having worked comparatively few years in the United States, or in jobs where they were unable to accumulate the necessary years for Social Security eligibility, leaving them less able to take advantage of work-based social safety net programs like Social Security.

For more on the causes of poverty among older Americans and the capacity of Social Security to lift older Americans—particularly women and people of color—out of poverty, see Kathleen Romig, Social Security Lifts More People Above the Poverty Line Than Any Other Program, Center on Budget and Policy priorities, April 2022. For more on the economic condition of the older Asian American population, see Victoria Tran, “Asian American Seniors Are Often Left Out of the National Conversation on Poverty,” Urban Wire (Urban Institute blog), May 31, 2017.

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Racial wealth disparities are stark and persistent, reflecting a history of exploitation and exclusion: Median family net worth by race and ethnicity, selected years from 1989 to 2022

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The chart shows sharp racial and ethnic disparities in net worth observed across time in the United States. Though not shown in the chart, these disparities reflect the differences in lived economic experiences between white, Black, Hispanic, and Asian families. Wealth can be accumulated both within and across generations, such that a high net worth can result from the benefit of prime earning years with 1) relatively few employment disruptions, 2) access to wealth-building savings and investment vehicles, 3) relatively few serious negative health shocks, and 4) well-timed wealth transfers from parents and grandparents.  The typical white household has many times the wealth of the typical Black or Hispanic household due to 1) their privileged position in the American labor market, which grants them access to more consistent and higher-quality employment opportunities, 2) their more limited exposure to the health risks brought on by poorer living conditions and discrimination, and 3) their history of access to wealth-building opportunities from which other groups have been excluded. 

In 2022, the Survey of Consumer Finances reported household wealth data for the Asian American population for the first time. Asian household wealth far outstrips that of other households in 2022, though this statistic should be couched with appropriate context: Asian Americans are an incredibly diverse group with varying economic circumstances related to, among other things, immigration history and country of origin; moreover, the SCF oversamples households that are likely to be wealthy. Further disaggregation of wealth data by immigration history could be useful in illuminating wealth disparities within the Asian American population.  

For more on the systemic barriers to Black wealth building see Natasha Hicks, Fenaba Addo, Anne Price, and William Darity Jr., Still Running Up the Down Escalator: How Narratives Shape Our Understanding of Racial Wealth Inequality, The Samuel Dubois Cook Center on Social Equity, 2021. For more on the barriers to Hispanic wealth building see Dedrick Asante-Muhammad, Alexandra Perez, and Jamie Buell, “Racial Wealth Snapshot: Latino Americans.” National Community Reinvestment Coalition, September 17, 2021.

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Health

Racial disparities in life expectancy reflect the cumulative disadvantage of living as a minority in the United States: Women’s and men’s life expectancy at birth, by race and ethnicity, 2022

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Racial disparities in life expectancy have been documented as far back as statistics on life expectancy have been recorded in the U.S, with clear and persistent distinctions between privileged groups and disadvantaged groups. That is, rather than groups shifting in their ranking of life expectancy randomly across time, there are distinct patterns in which groups live longer lives than others. In general, Black and AIAN women and men live much shorter lives than white and Asian women and men. 

In 2022, Asian American women and men had the longest life expectancies, at 86.3 years and 82.3 years respectively. AIAN women and men had the lowest life expectancies, at 64.5 years and 71.3 years respectively. This massive gap in life expectancy approaching two decades can be attributed to several factors, many of which are structural and rooted in economic disparity. In recent years, life expectancy gains have disproportionately gone to those in the highest income categories, who are disproportionately white and Asian (see Chart 18). Alongside the history of white supremacy and anti-Black racism in the United States, these economic roots of also help to explain persistent the persistent Black-white gap in life expectancy. That Black-white gap has fluctuated somewhat over the past decade, shrinking due to the impact of opioid-related “deaths of despair” on lowering white life expectancy, and reopening as COVID-19 related mortality disproportionately impacted Black and brown communities. 

Hispanic women and men tend to live longer than white women and men, though that life expectancy advantage has been shown to diminish with subsequent generations of U.S.-born Latinos. This suggests that there may be something uniquely deleterious about living as a minority in the United States.

For more on gaps in life expectancy, effects of the opioid crisis, and Hispanic life expectancy see Congressional Research Service, The Growing Gap in Life Expectancy by Income: Recent Evidence and Implications for the Social Security Retirement Age, CRS Report R44846, July 6, 2021; Helena Hansen and Julie Netherland, “Is the Prescription Opioid Epidemic a White Problem?” American Journal of Public Health 106, no. 12 (December 2016), 2127–2129 (doi: 10.2105/AJPH.2016.303483); Osea Giuntella, “The Hispanic Health Paradox: New Evidence from Longitudinal Data on Second and Third-Generation Birth Outcomes,” SSM – Population Health, vol. 2 (December 2016), 84–89 (doi.org/10.1016/j.ssmph.2016.02.013).

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The Affordable Care Act significantly reduced uninsured rates across racial and ethnic groups, but disparities remain: Uninsured rates by race and ethnicity, 2008–2024

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The Affordable Care Act (the ACA or “Obamacare”) expanded health insurance coverage to middle- and low-income Americans, which disproportionately benefited those groups with the least access—Hispanic Americans and American Indians and Alaska Natives (AIAN), and to a lesser extent Black Americans. Despite the marked improvement in health insurance coverage rates since the implementation of ACA, disparities between groups remain stark, with Hispanic and AIAN uninsured rates double Black rates, and approaching four times as high as the uninsured rates of white and Asian American and Pacific Islanders (AAPI). Early diagnosis and treatment are essential to minimizing the severity of chronic illnesses, and regular health care is important for promoting better overall health. The lack of health insurance often results in a choice to delay receiving health care until one’s condition is critical, contributing to racial disparities in health outcomes and life expectancy.

For more on how the ACA expanded health coverage, particularly to certain groups, see Samantha Artiga, Latoya Hill, Kendal Orgera, and Anthony Damico. “Health Coverage by Race and Ethnicity, 2010–2019,” Kaiser Family Foundation, July 16, 2021; Jesse Cross-Call, Medicaid Expansion Has Helped Narrow Racial Disparities in Health Coverage and Access to Care, Center on Budget and Policy Priorities, October 2020.

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Black mothers are far more likely to die from pregnancy-related causes than are white and Hispanic mothers: Pregnancy-related deaths per 100,000 live births by race and ethnicity, 2023

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Maternal mortality rates are a stark indicator of racial disparities in public health in the United States. Black women are over twice as likely to die from a pregnancy-related cause as white women, almost three times as likely as Hispanic women, and almost four times as likely as Asian women. Although not shown in the chart, these racial disparities persist regardless of a woman’s social or economic status. Health status and differential access to quality prenatal care play a major role in maintaining these disparities, as does structural racism more generally. To adequately address these disparities in maternal health outcomes, we must confront racism and bias in the U.S. health care system and the implications for how health care providers and personnel communicate with and treat patients.

For more on the causes and solutions to Black maternal mortality, see “Working Together to Reduce Black Maternal Mortality,” Centers for Disease Control and Prevention, April 6, 2022.

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AIAN population 1-year estimates and 3-year rolling averages, select charts

Appendix

Appendix

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A recent EPI report surveyed trends in labor force participation in the United States in recent decades. Besides presenting basic facts, the report also reviewed the research literature on the determinants of these trends, and the effects of policy changes. This policy brief focuses on one theme from the report: the need for patience when crafting a response to labor force participation trends. This need for patience applies to two main aspects of crafting policy:

Patience in diagnosing determinants of labor force participation trends

A previous wave of research in labor force participation in the mid-2010s came to erroneous and overly pessimistic conclusions simply because it examined a period when the economy was still cyclically depressed. The labor market still had excess slack from the collapse in aggregate demand that caused the Great Recession of 2008–2009. Once this slack was mostly wrung out of the labor market by the late 2010s (and the mid-2020s), many key measures of labor force participation began improving (with a substantial lag). An analogy to the mistake of trying to diagnose structural trends in the economy when it was still plagued by cyclical weakness would be trying to assess how effectively a marathon runner had been training for the past year by timing a race run when they were still recovering from a bad flu.

Patience in allowing for reasonable lags between the implementation of policies and positive results from those policies

As noted above, labor force participation rates are some of the last macroeconomic variables to recover fully from a cyclical downturn—responding with a considerable lag even to short-run changes in the macroeconomy. Further, because labor force participation is positively linked to workers’ skills and credentials, durably boosting economywide participation rates requires a broad and long-lived investment in these skills and credentials. This obviously takes time. In fact, the most promising interventions to raise labor force participation in the long run are likely significant investments in the health and education of today’s children. The payoff to this investment (even in narrow labor force participation terms) is significant and large but will obviously take a substantial amount of time to fully realize—even decades—as childen grow to adulthood and participate in the labor force.

Patience in diagnosis

Economic researchers are often interested in disentangling structural from cyclical effects on various outcomes. For example, in 2000 the unemployment rate averaged 4%, and in 2010 it averaged 9.6%. Researchers might want to know how much of the higher unemployment rate in 2010 was driven simply by the economy being in a different phase of the business cycle in 2010 versus how much was driven by long-running structural forces on the labor market that were unrelated to the business cycle. In theory, drivers of long-running structural trends might include changes in technology that displaced workers or changes in the age structure or educational attainment of the population.1 In regard to 2000 and 2010, however, all of the difference in unemployment rates between those years can be accounted for by cyclical factors: In 2000 the economy was booming with strong aggregate demand, and in 2010 the labor market was in recession and economywide spending was extremely weak. 2

In practice, the easiest way to disentangle structural from cyclical factors in driving trends in economic variables is to look at changes in these variables from business cycle peak to business cycle peak, essentially measuring outcomes only when something close to full employment had been reattained. In the 2010s, many researchers made premature declarations about structural trends about U.S. labor force participation because they did not wait until a business cycle peak was reached to compare with past peaks. This led to misleading conclusions.

The long tail of the Great Recession and why it led to pessimistic forecasts of labor force participation

In 2008, the United States entered what was then its worst economic crisis since the Great Depression—often referred to as the “Great Recession.” The unemployment rate rose to 10% in 2009 and remained above its 2007 average for the next decade. Despite clear evidence that economic growth remained demand-constrained and that the labor market was characterized by substantial slack even as late as 2015, a number of studies were published in the 2010s, aiming to assess structural trends in labor force participation. When these studies included post-2008 data points and assumed these data points were indicative of long-run structural trends, a notably pessimistic picture of labor force participation emerged.

This pessimism was driven by two large considerations, one true and one overstated. The true consideration was that the U.S. population is aging steadily over time, and demographic pressures were always going to see a rising ratio of retirees to active labor force participants. The overstated consideration concerned likely future labor force participation declines among prime-age workers (adults between the ages of 25 and 54). Recent decades had seen a long-running decline in prime-age male labor force participation, a recent stagnation of prime-age female labor force participation since 2000, and a sharp drop in both after 2007.

The confluence of these trends led many of the studies from the 2010s to project a future with a substantially smaller labor force. For example, one of the most influential of these mid-2010s papers (Aaronson et al. 2014) forecast that the overall labor force participation rate in 2019 would be 61.8%, and that in 2022, it would be 61%. In fact, 2019 saw an overall labor force participation rate of 63.1%, and in 2024 it was 62.6%. These are significant differences: Every 1 percentage point increase in labor force participation implies roughly 2.75 million more adults in the workforce, so these projections essentially lowballed the size of the labor force in recent years by close to 4 million workers.

It is certainly true that demographics—particularly population aging—are putting steady and predictable downward pressure on overall labor force participation rates. But the degree to which prime-age labor force participation was on a steep downward trend after 2007 was overestimated. And a large part of this overestimation was simply due to trying to infer structural determinants of labor force participation in the 2010s when the economy remained cyclically depressed. For example, Hall (2014) began his comments on the Aaronson et al. (2014) paper with the following (emphasis added):

The substantial decline in labor-force participation in recent years has raised the important question: How much of this decline is the result of the slack labor market from the Great Recession, and how much comes from other, structural forces? As the unemployment rate has returned to normal, a concern has developed that some of the people now classified as out of the labor force are, effectively, unemployed, but they are not included in the standard unemployment count because they do not satisfy its fairly exacting standards for classifying people as unemployed.

But the unemployment rate in 2014 had decisively not “returned to normal.” It averaged 6.2% over the year compared with the 4.6% average for 2007 (which, itself, was not particularly low). The Aaronson et al. (2014) paper included a figure (Figure 13 in their paper) that also showed what their projections for future labor force participation would have been if they had simply ignored the post-2008 data. These projections were far closer to what actually occurred in the period after their paper was written.

In short, by incorporating the 2010s data that was infected with cyclical weakness when they were trying to estimate a structural trend, their projections were too pessimistic. As Figure A shows, in 2016—a year that saw the overall unemployment rate dip below 5% for the first time in 8 years—prime-age labor force participation began rapidly recovering and continued recovering as overall unemployment rates fell further. By 2024, after years of extremely strong post-pandemic labor markets, labor force participation rates had actually regained the levels of the late 1990s. The evidence here is that there was little in the way of structural downward pressure on labor force participation; it was all driven by excess unemployment.

The best course of action for those who want to use the most timely data and do not want to have structural trend estimation marred by cyclical effects is simply to wait until a full business cycle has run its course and measure from peak to peak. This does not fully neutralize all cyclical effects (some business cycles end even before the economy has reached full employment), but this degree of patience would help a lot in correctly diagnosing trends.

The misdiagnosis in the mid-2010s about the likely trend of future labor force participation could have had serious repercussions. The state of labor force participation is a key variable when trying to assess what the level of potential gross domestic product (GDP) is. If one estimates this potential GDP as being too low relative to its true level, policymakers will stop aiming to boost aggregate demand and will settle for a level of actual GDP that is quite a bit below its true potential. This, in turn, will keep many potential workers from ever finding jobs, and the resulting too-slack labor market will fail to generate acceptable levels of wage growth. In turn, the federal budget deficit will be too high as tax collections hover below what could have been achieved if genuine full employment had been reached.

Patience in waiting for prescriptions to have an effect

In 2024, the biggest observable correlate with labor force participation is educational attainment. Labor force participation rates of workers with a college degree, for example, are 15.6 percentage points higher than for workers with a high school diploma.

Figure B

To the degree that a significant portion of this educational gradient in labor force participation reflects the causal influence of greater educational attainment in improving participation rates, this implies that measures that raise educational attainment would lead to higher labor force participation. This educational gap in labor force participation is often underrated as a source of economic inequality, and hence, also underrated as a possible margin along which educational investments might boost living standards.

For example, it is well known by now that greater educational attainment leads to higher annual (and lifetime) earnings. What is often underestimated is how often this premium is calculated conditional on working. But labor force participation for college graduates is 14.4 percentage points greater (or roughly 25% higher) for workers with a college degree, relative to those with a high school diploma. The National Center on Education Statistics (NCES 2024) reports that workers with a bachelor’s degree have annual earnings that are 59% higher than those with a high school diploma. But if we account for nonparticipation of college and high school workers (essentially assigning zero earnings to the share of each group not participating in the labor force), this would raise the annual earnings gap to roughly 105%, and almost a quarter of it would be accounted for simply by the higher labor force participation rates of college graduates. 3

Investing in children has large beneficial effects but will take decades to realize

The need for patience stems from the obvious fact that investments to boost educational attainment of the labor force will take considerable time: Colleges (including community colleges) and other forms of workforce development require mobilizing resources, and those being trained and educated need time to absorb new skills.

Further, the biggest payoffs to upfront investments in the name of boosting economywide labor force participation will come from investing in children—and particularly in early childhood. Investments in high-quality pre-kindergarten, for example, have very high social rates of return in large part because the children receiving these investments grow up to have higher earnings and stronger labor force attachment than other children do. But these benefits take considerable time to develop. For example, Lynch and Vaughul (2015) document that the annual payoff from a large investment in high-quality pre-kindergarten in year 1 of the investment is roughly 2% as high as the payoff in year 20. This is true even after accounting for the some considerable “real-time” effects of investing in early childhood education—like the boost to parents’ labor force participation when affordable, high-quality child care options are available.

Other research shows that investments in children’s health (including their nutritional health) also have high payoffs in terms of greater labor market success when they become adults. For example, Hoynes, Schanzenbach, and Almond (2016) found that children’s access to food stamps (or Supplemental Nutrition Assistance Program (SNAP) benefits) led to higher rates of high school completion and higher labor market earnings. Bailey et al. (2024) similarly found that access to SNAP increased their measured human capital as adults. Miller and Wherry (2019) found that infants who gained access to Medicaid in utero via their mothers’ prenatal coverage also had increased high school graduation rates. Brown, Kowalski, and Lurie (2020) found that eligibility for Medicaid during childhood increased college enrollment rates and taxes paid as adults.

The earnings effects of exposure to both Medicaid and high-quality early childhood education (ECE) are large. Brown, Kowalski, and Lurie (2020) find that each year that a child is covered by Medicaid adds 0.5% to their earnings as adults. This implies Medicaid coverage over an entire childhood would raise future earnings by as much as 9%. Lynch and Vaghul (2015) find that exposure to high-quality ECE can raise earnings of affected children by 25%–40%. If the total earnings effects of a large investment in children today were earnings that were 40% higher decades from now for children exposed to these greater investments, this necessarily implies a large effect on labor force participation. For example, if a quarter of these earnings effects were driven by higher labor force participation rates and just a tenth of U.S. children were exposed to these higher investments, this would imply a boost in labor force participation for this cohort’s lifetime of over a percentage point. The earnings effects of these interventions would provide a very substantial offset to their upfront fiscal costs. 4

Finally, a common finding across this literature is that effects are largest when they begin when children are young—even in utero. This implies that policymakers hoping for a payoff in labor force attachment from raising investments will need to display a lot of patience. The payoff might only begin in 10–20 years, and the full payoff could well take over 50 years. Patience is not a widely recognized virtue in U.S. policymaking, but it is one that could pay off greatly, should it be practiced in the form of investing today in children’s improved health, nutrition, and education.

Acknowledgments

The author thanks Joe Fast for research assistance and Grace Park for editing. This project was made possible by financial support from the Peter G. Peterson Foundation.

Notes

1. For example, if higher educational attainment causally increases labor force participation rates (something discussed in section two of this brief), then an increasing share of workers having college degrees should boost labor force participation over time. Another example going in the other direction concerns the potential negative causal effect on labor force participation of a spell of incarceration—if such a spell leads to lower labor force participation after re-entry, the large rise in the number of Americans with a spell of incarceration in their past would lower labor force participation rates overall.

2. Evidence of this can be seen in the fact that unemployment rates by 2018 and 2019 were actually lower than they were in the late 1990s and 2000s. Evidently there was no permanent structural shift keeping unemployment from falling back to these levels.

3. We can calculate the average relative earnings per member of the population (rather than per worker) by multiplying the 1.59 relative earnings advantage of those with a college degree by 1.28—which is the ratio of the prime-age employment-to-population ratio of workers with at least a college degree relative to the rest of the workforce. This gives 2.05, for a 105% relative earnings advantage. Since we know that 28% of this advantage is due to the higher employment-to-population ratio, we know that this is over a quarter of the advantage. Finally, if we do the same exercise but use the ratio of prime-age labor force participation rather than employment-to-population ratio, this gives us a relative earnings measure of 2.00—which indicates that 2.00/2.05 of the total effect of higher relative employment is driven by higher labor force participation of college workers rather than by lower rates of unemployment—still over a quarter of the entire advantage.

4. Lynch and Vaghul (2015) find this for early childhood education, and a Congressional Budget Office (CBO) working paper (Ash et al. 2023) finds that allowing Medicaid to offer “continuous eligibility” to children—allowing children to remain on Medicaid for 3 years, even after they may no longer quality for it based on current income tests—could boost future earnings enough that higher taxes could finance between 49% and 197% of the upfront cost of this policy change.

References

Aaronson, Stephanie, Tomaz Cajner, Bruce Fallick, Felix Galbis-Reig, Christopher Smith, and William Wascher. 2014. Labor Force Participation: Recent Developments and Prospects. Brookings Papers on Economic Activity. The Brookings Institution, Fall 2014.

Ash, Elizabeth, William Carrington, Rebecca Heller, and Grace Hwang. 2023. “Exploring the Effects of Medicaid During Childhood on the Economy and the Budget.” Congressional Budget Office Working Paper 2023-07, November 1, 2023.

Bailey, Martha J., Hilary Hoynes, Maya Rossin-Slater, and Reed Walker. 2024. “Is the Social Safety Net a Long-Term Investment? Large-Scale Evidence from the Food Stamps Program.” Review of Economic Studies 91, no.3: 1291–1330. https://doi.org/10.1093/restud/rdad063.

Brown, David W., Amanda E. Kowalski, and Ithai Z. Lurie. 2020. “Long-Term Impacts of Childhood Medicaid Expansions on Outcomes in Adulthood.” Review of Economic Studies 87, no. 2: 792–821. https://doi.org/10.1093/restud/rdz039.

Bureau of Labor Statistics (BLS). 2025. Online Data Retrieval Tool from the Current Population Survey Database–Labor Force Participation Rates for Workers Between the Ages of 25 and 54, Overall and by Educational Attainment. Accessed September 2025.

Economic Policy Institute (EPI). 2025. “Number of Labor Force Participants.” [web], State of Working America Data Library. Published 2025.

Gould, Elise, Sarah Jane Glynn, Hilary Wething, and Josh Bivens. 2025. Good News and Bad News About U.S. Labor Force Participation: Many Headwinds from the 2010s Are Gone, but We’re Not Investing Enough in the Future. Economic Policy Institute, September 2025.

Hall, Robert. 2014. Comments on Stephanie Aaronson, Tomaz Cajner, Bruce Fallick, Felix Galbis-Reig, Christopher Smith, and William Wascher. 2014. Labor Force Participation: Recent Developments and Prospects. Brookings Papers on Economic Activity. The Brookings Institution, Fall 2014.

Hoynes, Hilary, Diane Whitmore Schanzenbach, and Douglas Almond. 2016. “Long-Run Impacts of Childhood Access to the Safety Net.” American Economic Review 106, no. 4 (April 2016): 903–934.

Lynch, Robert and Kavya Vaghul. 2015. The Benefits and Costs of Investing in Early Childhood Education: The Fiscal, Economic, and Societal Gains of a Universal Prekindergarten Program in the United States, 2016–2050. Washington Center for Equitable Growth, December 2, 2015.

Miller, Sarah, and Laura R. Wherry. 2019. “The Long-Term Effects of Early Life Medicaid Coverage.” Journal of Human Resources 54, no.3: 785–824. https://doi.org/10.3368/jhr.54.3.0816.8173R1.

National Center on Education Statistics (NCES). 2024. Annual Earnings by Educational Attainment. Condition of Education. U.S. Department of Education, Institute of Education Sciences. May 2024.

Key takeaways

• The last decade marks a shift in the prime-age labor force participation rate (LFPR). It moved away from a long-term decline toward rebounded participation in the wake of strong labor markets. Current prime-age LFPR is now back to its 2001 level, erasing much of those losses. Key conclusions from this: Full-employment labor markets are needed to keep LFPRs strong, and long-term structural determinants of LFPR growth cannot be accurately diagnosed during times of cyclical labor market weakness.
• Since 1979, key drivers of the decline in men’s labor force participation included the following:
  • extended periods of excess unemployment rates
  • the labor market scarring effect of mass incarceration
  • the decline of historical sources of employment for noncollege men like the manufacturing and military sectors
  • increased opioid usage
• During the strong labor market in the late 2010s and following the tremendous recovery from the pandemic recession, noncollege men and Black men have seen substantial increases in    labor force participation.
• Women, by contrast, experienced historical gains in labor force participation throughout the 1980s and 1990s but then their participation stalled out in the early 2000s —and began falling behind relative to peers in OECD countries. In the U.S., insufficient support for balancing paid work and family responsibilities has been a limiting factor in further increases in women’s labor force participation. However, increases in workplace flexibility, with the rise of hybrid or remote work following the pandemic, may have boosted labor force participation, particularly for women with caregiving responsibilities.

Policy recommendations for maintaining and improving gains in labor force participation:

• In addition to policies that prioritize tight labor markets, policies should target the following for adults:
  • reductions in opioid use
  • reductions in incarceration rates
  • improvements in policies that support parents and caregivers
  •  substantial improvements in the pay and working conditions of jobs of the future (like caregiving jobs) to attract and retain workers
• Investments in today’s children are crucial for boosting the labor force participation of future generations, such as safety net policies that promote long-term health and educational investments. The future labor market benefits of investing in children are so strong in the long run that they may even be fiscally self-financing.

Executive summary

Labor force participation is both a key input and a consequence of strong economic growth. While there are many reasons some do not participate in the formal labor market—school, family caregiving responsibilities, retirement, work-limiting disabilities—a strong labor market with high employer demand for workers is a necessity to give as many willing workers as possible a chance for employment.

In an aging population in which college attendance is far more common than it used to be, demographic trends have a strong influence on the overall labor force participation rate. Few people think that it’s a problem that many older Americans choose to enjoy retirement or that many younger adults are enrolled in school rather than searching for work. What is, however, a potential problem is many prime-age workers—those between 25 and 54—are dropping out of the job search and work. To assess the extent of this problem, this report focuses primarily on prime-age labor force participation, the share of the population between 25 and 54 that is working or looking for work. This measure rose sharply from the mid-1970s to the mid-1990s. After that, it was flat for a period, then fell during the mid-2010s, most notably following the Great Recession. Over the last 10 years, participation has rebounded strongly and is now back to its 2001 level, erasing much of those post-2000 losses.

The rise in participation before 2000 was primarily driven by women as they increased their education, delayed family formation, and chose to participate in the paid labor market, driven in part by greater opportunities to access higher-paying previously male-dominated professions. The rise in participation over the last decade improved outcomes for both men and women, as strong employer demand led to workers entering or returning to the labor market. By 2024, women’s participation hit an all-time high, and men’s participation rate is back to its 2010 level.

Changes in labor force participation over the last nearly five decades varied by gender, but also across various demographic groups. While changes within demographic groups were the most important drivers of overall trends, there were notable differences between groups. For instance, those without a college degree—particularly men—experienced steeper declines in participation. And education upgrading (increasing the share of the population with a college degree) over the long term did little to offset that weakness. Loss of jobs in areas that traditionally were large-scale employers of noncollege men, such as manufacturing and the military, is undoubtedly related to reduced opportunity and participation in the labor force for those without a four-year college degree.

Black men, in particular, experienced notable declines in participation before the strong labor market over the last 10 years returned their participation to its 2000 level. The quadrupling of incarceration rates through the 1980s and 1990s disproportionately impacted Black men, making it harder for them to secure employment because of both the labor market scarring effects of incarceration as well as labor market discrimination.

Across peer countries in the OECD, prime-age labor force participation didn’t fall off to the same extent for men as it did in the U.S. and continued to rise for women over time. Insufficient support for balancing paid work and family responsibilities in the U.S. has been a limiting factor, particularly for women’s labor force participation. A body of international evidence indicates that larger investments in those areas—such as child care and paid leave—have the potential to help boost participation. Recent increases in work flexibility following the pandemic, such as hybrid or remote work, may have aided the entry or reentry of workers with caregiving responsibilities.

Policy choices–both of commission and omission—can affect the future growth of labor force participation, but outside of immigration, the effects will be comparatively modest relative to historical swings in labor force participation. Strengthened public care can increase labor supply, particularly for women. Poor health, pain, and opioid use have been linked to lower participation, so improving population health and the provision of health care could increase labor force participation. Further, investments in today’s children, through programs that provide health care, early education, and food security, can also pay dividends in terms of future labor force participation.

A strong economy and high-quality jobs are strongly related to labor force participation. When the labor market is tight, workers come back in search of better opportunities. Even with the pandemic job losses, the tight labor market over the last decade has all but erased the declines in the 2000s when excess unemployment and slow job growth kept would-be workers on the sidelines.

Introduction

The rate at which people participate in the U.S. labor force—which includes people who are working, as well as those who are unemployed but actively looking for work—has enormous implications for the economy and can serve as a barometer for its overall health.

There is no ideal labor force participation rate, and a society in which 100% of the population is in the labor force is not only unrealistic, but also undesirable. For example, high labor force participation could reflect a strong economy, or it could reflect a lack of access to social safety nets that force the very old and people with work-limiting disabilities into the workforce in order to survive. Falling labor force participation rates could be the result of a recession or other negative event like a global pandemic or could be caused by an aging population with many retired people or increased educational opportunities that delay entry into the labor force among younger cohorts.

Because there is no obvious ideal labor force participation rate, policymakers should think less about particular targets to hit for this rate and should instead aim at removing barriers that stand in the way of willing workers and their ability to search for and secure a decent job. While there are good reasons to not participate, such as gaining education or skills, harmful barriers could include macroeconomic slack in labor markets or more structural barriers like discrimination or insufficient societal investment in workers’ health and skills or insufficient support for balancing paid work and family responsibilities.

Labor force participation that is high due to few barriers between willing workers and the ability to find decent jobs is a key ingredient to a healthy, stable economy. This relationship moves in both directions: A healthy economy is one that sees few barriers to willing workers finding jobs, and growing labor force participation is also a key component of economic growth. When the number of people in the labor force increases, it boosts production and leads to higher consumption.

The overall labor force participation rate in the United States is lower now than at its peak in 2000, largely because the population is aging and members of the baby-boom generation have retired. Participation among younger people has declined over time, raising concerns among some economists and policymakers. But the direction of these trends has not been consistently negative, and there is evidence from the last decade that earlier patterns were less durable than predicted.

This report provides an overview of prime-age labor force participation over the last 45 years, summarizes prior research on possible drivers behind the changes over time, and highlights when and how patterns have shifted over the last decade, concluding with policy recommendations that the data suggest could be most helpful to support a continued upward trajectory.

Overall trends in labor force participation

The prime-age labor force participation rate is the share of the civilian noninstitutional population between ages 25 and 54 that is working or looking for work. We focus on this measure to remove those who may be more likely to be in school or retired. As educational attainment has increased over time, a larger share of the population may be out of the labor force for longer (primarily affecting the population younger than 25). At the same time, the population has aged, and a growing share of the population has moved into retirement. Removing those under 25 and over 54 from our analysis removes those mostly demographic changes in labor force participation. Unless otherwise stated, all analysis in this report will include only the U.S. population 25 to 54 years old and will, therefore, be referred to as the labor force or the labor force participation rate (LFPR).

In this report, our primary data set is the basic monthly Current Population Survey. For most analysis, we have a consistent series from 1976 to 2024 and use that entire period, when possible, to display trends. For consistency when decomposing changes over periods of time, we start with 1979 because it is the first business cycle peak in our data, and we don’t want to capture any cyclical trends that may have impacted the data from 1976. Using endpoints for analyses that are at different points of the business cycles can cloud conclusions on structural changes in the labor market. This is what happened with much of the research on labor force participation rates from the mid-2010s when the economy was still suffering employment losses in the aftermath of the Great Recession.

Prime-age labor force participation increased year over year throughout most of the post-World War II era for which we have data. Between 1976 and 2024, the prime-age labor force participation rate rose 8.8 percentage points from 74.8% to 83.6%. As Figure A demonstrates, there was a sharp rise in labor force participation from 1976 to the mid-1990s when it stabilized somewhat, then fell until the mid-2010s. With the notable exception of the pandemic recession, labor force participation has been on the rise for the last 10 years.

Figure A

Labor force participation rates by gender

The overall trends in prime-age labor force participation are valuable in understanding the overall story of the labor market, but they mask some stark differences between participation rates for men and women. Figure B shows that men’s labor force participation is consistently higher than women’s throughout the entire period. What’s most striking is the rise in participation overall through the 1990s was entirely driven by women. There are a number of cultural and socioeconomic factors behind that rise in women’s participation as women increased their college attendance and graduation rates while narrowing the gender gap in college majors, delayed marriage and childbirth, and acquired more market-relevant skills. Combined, these shifts led to greater opportunities for women to enter previously highly male-dominated occupations and earn higher wages (Goldin 2006). Both men and women experienced declines in participation from around 2000 to the mid-2010s, and then both groups experienced a rise since then, though stronger for women.

Figure B

Labor force participation rates move with overall labor market strength

Labor force participation rates tend to decline under weak economic conditions, like recessionary periods. But when the 2008 recession began, prime-age participation had still not fully recovered losses from the early 2000s, and LFP continued to fall for both men and women after the recession ended and the economy started expanding again. The majority of the decline in prime-age labor force participation occurred in the years after the 2008 recession, when prime-age LFP fell by 2.2 percentage points over the course of six years.

A significant body of research was released in the mid-2010s that highlighted the long-term fall in labor force participation, particularly among men, but the last 10 years have shown us a notable reversal in trend as participation for both men and women have been on the rise. While prime-age women are now experiencing their highest labor force participation rates on record, men’s have stopped their downward movement and risen 1.1 percentage points since their low point in 2014 (except in the pandemic recession).

The strength of the labor market over the last 10 years has meant more and better opportunities for potential labor market entrants. There have been two distinct periods over the last 45 years in which a growing economy has led to more broadly shared prosperity: the late 1990s and the last 10 years. Table 1 maps changes in labor force participation in those particular time periods against unemployment rates. Then, we summarize those two periods of time into two categories. The stronger labor market is defined by 1995–2000 and 2014–2024, while the weaker labor market is defined by the remaining 30 years since 1979.

In the good times, the unemployment rate averaged 4.7%, and labor force participation increased 0.3 and 0.1 percentage points per year, on average for women and men, respectively. In the bad times, women’s labor force participation continued to rise, but was largely driven by the structural increases in opportunities in education and reduced barriers to entry for higher-paying professions that characterized the 1979–1995 period. Men’s participation fell 0.2 percentage points in these times of weaker opportunities and lower wage growth when the overall unemployment rate averaged 6.7% (Gould 2020). Since 2000, periods of high unemployment have been associated with declines in both male and female labor force participation.

Table 1

Periods of higher unemployment for much of the last 45 years appear to be related to lower participation rates, particularly among men. But, as the women’s labor force participation rate stabilized in 2000, the trends have been similar for both men and women. The weaker labor market between 2000 and 2014 meant losses in participation, as workers saw fewer opportunities for themselves in the labor market. Though delayed, the labor market expansion in the lead-up to the business cycle peak in 2019, and in the strong bounceback of the last four years, has coincided with greater labor market participation for new or returning workers.

Mechanically, when workers see fewer opportunities and leave the labor force, the unemployment rate will fall as people who may have been classified as unemployed are now out of the labor force and, therefore, not counted. To the extent this is happening, even the higher unemployment rates in the bad times may be overstating labor market strength or undercounting weakness.

Since men’s and women’s labor force participation rates differ greatly in terms of their absolute levels across the entire period in question, we will conduct separate analyses for women and men. We caution readers to note the change in scale between figures for women and men when comparing trends. Women’s low participation in the 1970s requires a wider range; when men’s are narrowed to the range of interest, it can appear to amplify changes. While there were large losses over the entire period for men, they may appear larger than they are when compared with women’s wider labor force experiences.

Labor force participation rose for all racial/ethnic groups among women, while white and Black men experienced the largest declines

Figure C illustrates prime-age labor force participation rates for women (on the left) and men (on the right) for four groups: Hispanic of any race, white non-Hispanic (white), Black non-Hispanic (Black), and other (non-Hispanic). Other (non-Hispanic) is mostly Asian and Pacific Islander women and men; however, a series for this group doesn’t date as far back as 1976. Among women, Hispanic women have the lowest participation rates, while white and Black women have the highest. White women experienced the sharpest rise in participation through the 1970s, 1980s, and 1990s, and all groups experienced a lack of progress or a softening in participation in the early 2000s. Except for the dip in the pandemic recession, all groups experienced a resurgence in participation over much of the last decade.

Figure C

Over the entire period, Black men had the lowest labor force participation rates, and their declines were the sharpest for much of the last 45 years, never recovering fully in each recovery until the most recent period. With the exception of losses in the pandemic recession, Black men experienced a significant increase in participation over the last decade. Now, their labor force participation rate is the highest it has been in nearly 25 years. White men also experienced declines until the mid-2010s, but their participation rate stabilized and remains just shy of their pre-pandemic levels. Hispanic men experienced milder declines over the entire period and an uptick since the pandemic recession.

Though we do not show a figure for labor force participation rates by nativity (and the data only go back to 1994), it’s worth noting that among women, the participation rate of noncitizens is much lower than that of native or naturalized women (See Appendix Table 1). Among men, the largest fall in participation occurred among the native-born though 2014 but then rose over much of the last 10 years, except during the deep pandemic recession. Non-native men, either naturalized or noncitizens, did not experience large declines in participation, but their presence in the U.S. is often tied to the availability of work so their denominator—the population of each of these groups—also ebbs and flows with the strength of the labor market.

Over the last nearly five decades, the prime-age population has shifted from over 80% to about 55% white non-Hispanic, a drop of about 28 percentage points (EPI 2025a). While the Black share of the prime-age population rose about 4 percentage points, the largest gains were among the Hispanic share, increasing about 16 percentage points between 1979 and 2024 (EPI 2025a).

Given differences in labor force levels by race and ethnicity and the changing composition of the population by race and ethnicity over time, it is useful to decompose the overall change in the labor force into its component parts: the change in population share (or the between effect) and the change in labor force participation within groups (the within effect). Figure D shows these two effects, on the left for women and on the right for men.

Figure D

Compared with the changes due to the changing composition of the workforce, the changes within groups loom much larger. For women, the changing composition pulls down participation in part because Hispanic women were a growing share of the population with lower participation rates, compared with the falling share and higher participation rates of white non-Hispanic women. The rise is due to within-group increases in participation over the entire period.

Among men, the changing composition of the workforce played a small role, though likely driven by a falling population share of white men with higher participation rates in general. The drop in participation rates within each race/ethnic group played a much larger role over the 45-year period.

Labor force participation rate fell sharply for men with less than a four-year college degree, while participation for women with a college degree is at its highest ever

Labor force participation rates for different groups by educational attainment vary but follow the same general pattern for men and women, respectively. Both men and women with lower levels of educational attainment, shown in Figure E as noncollege—less than a four-year bachelor’s degree—exhibit lower levels of labor force participation throughout the last 45 years. For women, the noncollege participation tracked college participation, though their rates notably continued rising into 2000, while college participation peaked in 1997 (before the current period). Then, noncollege women’s participation dropped off in the 2000s and rose only mildly over the last 10 years. After softening for several years, labor force participation for women with a college degree is now at an all-time high.

Figure E

The labor force participation rate for men with and without college degrees has declined over time, but unevenly. Men without a four-year college degree experienced large declines between 1979 and 2014, a fall of 8.2 percentage points. They experienced some gains in the expansion of the late 2010s but were harmed more in the pandemic recession. While their participation rate is now back to their 2019 level, the increase hasn’t put a huge dent in the losses they suffered in the 35 years following 1979.

The reduction in labor force participation for noncollege men over time has been considerably greater than for men with a four-year degree. Technology has reduced employment for some types of workers, especially in manufacturing and jobs made up of routine tasks, while boosting employment for other kinds of work, and there is evidence that middle-skilled or middle-wage occupations have declined and have been replaced with a combination of low- and high-skilled jobs (CEA 2016).1

The decline in jobs that are available to workers with lower levels of formal education—or perhaps more accurately, the decline in the types of jobs these men have traditionally had access to, such as those in manufacturing—may make men more likely to leave the labor force. The decline in routine manual-labor jobs—skilled and semi-skilled jobs in production, maintenance, and material moving occupations, which are concentrated in manufacturing but are common in many other industries as well—has been significant and was accelerated by the 2008 recession.

From 2000 to 2017, routine manual-labor jobs as a share of all nonfarm employment fell by nearly 5 percentage points (Valletta and Barlow 2018). There is a correlation between routine manual-labor jobs and prime-age labor force participation, and in states where the drop was larger, there tended to be corresponding larger declines in participation. Controlling for other state-level economic conditions does not alter the relationship, indicating that the share of routine manual-labor jobs is not a proxy for other broad changes such as changes to the unemployment rate. The reduction in the routine manual employment share from 2000 to 2017 is estimated to have reduced the prime-age participation rate by approximately 1.3 percentage points, slightly more than half of the actual 2.3 percentage point decline in prime-age LFP (Valletta and Barlow 2018).

More specifically, the share of men’s employment in the manufacturing sector has fallen to less than half of what it was in 1979. As shown in Appendix Table 3, men’s share of employment in combined durable and nondurable goods manufacturing was 28.5% in 1979, but by 2024, these shares were reduced to 12.8%. To be clear, women’s participation in manufacturing jobs also declined substantially over the period, dropping from 17.9% to 6.3% of women’s employment; however, given that these jobs made up a smaller share of women’s overall employment composition, the loss was felt less by women than by men.

Additionally, the debate over falling male labor force participation often does not mention an important and heavily male economic sector that has shrunk enormously in terms of the opportunities it provided for those who might otherwise have lower-than-average participation rates: the military.

Figure F shows the overall decline in men’s labor force participation alongside the decline in total military employment scaled to the male noninstitutional prime-age population. To be clear, these are not true shares because our measure of the prime-age population is limited to the noninstitutional population, which excludes those in military service. However, the decline in military employment has meant that millions of noncollege men who might have lower-than-average opportunities in the civilian economy can no longer find work in the military. Throughout the mid-1960s through the 1990s, the share of prime-age men in the military dramatically decreased, from a high of 14% in 1967 leveling out at just under 4% of the prime-age male population in the 2000s.

Figure F

Educational upgrading played a small role compared with within-group changes in labor force participation

As with the composition of the population by race and ethnicity, there were large shifts in the educational attainment of men and particularly women between 1979 and 2024. As shown in Appendix Table 2, the share of women with a college degree rose 30.3 percentage points, while the share of men with a college degree rose 14.3 percentage points. Even though women started out with a smaller share of college graduates, today they are more likely to have a four-year degree relative to men. Given that overall labor force participation is far higher for college degree holders, all else equal, we would expect participation rates to have climbed over the 45-year period. While not the same as the labor force participation rate, prime-age women’s increased educational attainment is estimated to have contributed 2.7 percentage points to their employment rate between 2000 and 2023 (Arnon et al. 2023).

There is evidence that pursuing postsecondary education may be delaying labor force entry, at least for some populations. While most college students are younger than prime age, about one-third of students enrolled at Title IV institutions in the fall of 2023 were age 25 or older, and one-quarter were ages 25 to 39 (NCES 2024). Research comparing prime-age men between millennial and baby-boomer generational cohorts found that school attendance explains a roughly a third of millennial men under 30s’ lower labor force participation, but that this effect has virtually no impact by age 40 (Bengali, Duzhak, and Zhao 2023). And when millennial men are separated by education, labor force participation for those with a high school diploma or less is relatively flat from age 25 to 40, while it increases with age for those with a college degree or more, suggesting that additional educational attainment may play a role in delaying eventual entry into the labor market.

In Figure G, we examine the role that changing education composition played in the changes in labor force participation. As the shift in educational attainment was twice as large among women, it’s not surprising that it played a large role in lifting women’s participation rates overall. But the increases in participation within education groups were even more important since 1979. For men, the declines in participation within each group played an outsized role in explaining declines in labor force participation. As we saw in Figure E, these losses were more acute among noncollege men.

Figure G

Labor force participation among married women rose quickly, as unmarried women saw little change

Participation rates for men and women by marital status display a strikingly different pattern, as shown in Figure H. Married men are more likely to work than unmarried men, while unmarried women are more likely to work than married women. Unmarried women always exhibit relatively high levels of labor force participation, and that has changed little over much of the last few decades, except for mild rising and falling in business cycles. Married women, however, experienced a sharp rise in participation from just over a half (52.3%) to three-quarters (75.8%), currently at their highest level of participation on record.

On average, married men are about 9 percentage points more likely to participate in the labor force than unmarried men. That gap has been relatively consistent over the last 45 years, though unmarried men are more subject to swings in the labor market.

Figure H

Over the last nearly five decades, marriage rates have declined for both men and women, falling by about a quarter overall (see Appendix Table 2). All else equal, the decrease in marriage rates for women would pull up overall labor force participation for women. Figure I illustrates this decomposition. The shift toward unmarried status pulled up women’s participation but depressed men’s, as unmarried women are more likely to work than unmarried men, but unmarried men are less likely to work than married men. Rising participation, especially among married women, was a major factor in the rise of participation among women. Men’s falling labor force participation over the 1979–2024 period is explained by both falling participation among married and unmarried men and falling married rates.

Figure I

Labor force participation among women with young children rose, while men’s labor force remained tied to aggregate labor market conditions

While a small percentage of prime-age workers overall report they are not in the labor force due to family and care responsibilities, family structure and caregiving have strikingly disparate impacts on men’s and women’s participation. Care for children is a significant driver of this difference, as mothers are more likely than fathers to be primary caregivers. Mothers have lower participation rates than similarly aged women without children, even after controlling for demographics and education (Kahn, García-Manglano, and Bianchi 2014).

Participation rates for women with young children tend to lag participation rates for women overall and have not grown at the same rate (see Appendix Table 1). Women with children under age 3 have lower participation rates than women with children under 5, although the gap between these two groups has closed slightly since the early 2000s. Women experience a significant and sharp decline in labor force participation after having their first child. Compared with one year prior to having their first child, mothers are 18 percentage points less likely to be in the labor force in the quarter they give birth, and it takes an estimated two years after the birth of their last child for LFP to recover to roughly the same rate as pre-motherhood (Sandler and Szembrot 2019).

Some of this is likely due to personal preferences and cultural norms around caregiving, but there is also evidence that suggests high prices for child care contribute significantly to lower maternal labor force participation. Previous studies have found a positive relationship between access to child care and the mother’s LFP, although the size of the effect varies across studies (Morrissey 2017). More recent data suggest a close to a 1-to-1 relationship between the price of care and employment; as child care prices increase by 1 percentage point, a mother’s probability of employment declines by 0.9 percentage points, and the relationship is even stronger in states with traditional gender norms (Collins et al. 2021).

Factors thought to have influenced prime-age labor force participation between 2000 and the mid-2010s

The majority of the decline in prime-age labor force participation occurred in the years immediately after the 2008 recession, when the participation rate fell by 2.2 percentage points over the course of six years. This prompted a wave of research and subsequent news coverage aimed at understanding the drivers behind this shift. Labor force participation rates tend to decline under weak economic conditions like recessionary periods. But when the recession began, the prime-age LFP had still not fully recovered losses from the early 2000s, and it continued to fall for several years for both men and women after the recession ended and the economy started expanding again.

The longer-term trends indicated that there were factors exerting downward pressure on prime-age participation beyond the business cycle. Estimates on how much of the change in LFP was caused by cyclical factors vary, ranging from one-sixth to about two-thirds (Shierholz 2012; CEA 2016). But while point estimates varied, there was widespread agreement that structural factors contributed significantly to falling labor force participation after 2007.

Many of the factors identified, such as declining opportunities for men without four-year college degrees and stalled parental and child care policies, have already been discussed. A wide range of other potential causes has also been hypothesized to be behind the reduction in prime-age participation, with an overall focus on the experience of men, given their steeper declines.

Poor health, pain, and the opioid epidemic

The number of prime-age adults who report they are not in the labor force due to poor health or disability has increased over time and is the primary reason for nonparticipation reported by men (Tüzeman and Tran 2019). Prime-age women overall report their health as better and their well-being as higher compared with men, and women’s self-reported health does not vary significantly by labor force status. In contrast, prime-age men who are not in the labor force report worse health indicators compared with working men (Graham and Pinto 2021).

Racial and ethnic disparities in health are well documented (NASEM 2017), but in contrast to decades of findings that people of color experience disproportionate health challenges, white men, among prime-age men not in the labor force from 2010 to 2016, reported worse health, lower well-being, and more pain than men of other racial groups. Among these white men, overall low scores were driven by those with lower educational attainment and those at the older end of the prime-age range, especially those ages 45 to 54. Because their health was so much worse than similar men who are working, this suggests that poor health may be the cause of their nonparticipation rather than its effect (Graham and Pinto 2021).

The opioid epidemic has also been linked to declining labor force participation, although it is difficult to assign causation or separate cause from effect due to a lack of reliable data. Opioid prescriptions increased significantly beginning in the late 1990s and peaked in 2012 (Chai et al. 2018) with 17.8 billion opioid analgesic pills dispensed that year alone (Woods et al. 2021). While the overall decline in prime-age labor force participation predates the opioid epidemic, there is evidence opioid use may have contributed to the trend.

A number of studies show that increases in the use of opioids are associated with negative labor market outcomes, including lower labor force participation, although effect sizes vary (Maclean et al. 2020). One widely cited report found that labor force participation fell more in counties with higher opioid prescription rates. After controlling for race, marital status, age, education, manufacturing jobs, and census region, increased opioid prescriptions are estimated to account for as much as 0.6 percentage points of the decline in prime-age male LFP and 0.8 percentage points of the decline for women—or roughly 20% of the total decline from 1999 to 2015 (Krueger 2017). Subsequent research found an opposite pattern by gender, estimating that a 10% increase in the local opioid prescription rate is associated with a 0.53 percentage point decline in prime-age participation for men and a 0.10 percentage point decline for prime-age women (Aliprantis, Fee, and Schweitzer 2023).

Social Security Disability Insurance

Along with increased self-reported poor health, pain, and opioid use, growing incidence of disability benefits has also been proposed as a cause of falling prime-age labor force participation in the 2000s and 2010s. Social Security Disability Insurance (SSDI) has been an important component of the social safety net since benefits began in 1957. Reforms were made to the disability screening process in the 1980s, and researchers have posited that, coupled with an increase in the real value of benefits, this led to the subsequent large increase in enrollment, with the number of workers receiving SSDI benefits tripling from 1980 to 2013. Some went so far as to suggest that many of the applicants may be making fraudulent claims (Autor and Duggan 2006). Although SSDI benefits replace only a fraction of a disabled worker’s prior earnings and disabled beneficiaries are more than twice as likely to live below the poverty line (CBPP 2025), some researchers hypothesized that SSDI benefits would reduce the incentive for people with some remaining work capacity to stay in the labor force.

Estimates on how much increased SSDI receipt has contributed to declining labor force participation for prime-age men vary but generally account for very little of the total change (CEA 2016). SSDI is suggested to have a particularly chilling effect on LFP for men with lower levels of education since benefit receipt has grown more for prime-age adults without a college degree, a group that has also seen larger declines in participation (Burk and Montes 2018). But research comparing data on SSDI and participation rates between 1975–1984 and 2008–2017 found that increases in disability benefits explain almost none of the decline in LFP for men with less than a high school education and only very small shares of the drop in LFP for prime-age men with only a high school diploma—0.01 percentage points of the decline for men ages 25–34 and 35–44, and 0.3 percentage points for those ages 45–54 (Binder and Bound 2019).

Incarceration rates

The number of people incarcerated in the U.S. quadrupled from 1978 to 1998 (BJS n.d.), and young Black men are disproportionately likely to be impacted. The rise in incarceration has cross-cutting effects on measured labor force participation. Because the surveys that estimate participation do not include the incarcerated population, if those currently incarcerated would be likely to have lower-than-average labor force participation rates in the noninstitutional labor market, a rise in incarceration can actually boost measured participation by removing this population from the denominator.

However, if a spell of incarceration causally reduces the probability of labor force participation because it makes an individual’s connections to the labor force more tenuous (being in an institution categorically means one is not in the labor force) or because skills and work experience can depreciate over time, then a growing stock of people in the market with a spell of incarceration in their history could lower overall participation through these scarring effects. Further, people with a history of incarceration are more likely to experience labor market discrimination (Burk and Montes 2018).

Spells of incarceration are estimated to have accounted for at least a quarter of the decline in LFP among all Black men between 1979 and 2000, and over one-half of the decline in participation rates among Black men ages 25–34 without a high school diploma (Holzer, Offner, and Sorenson 2005). More recently published research found that having received a criminal charge in their youth significantly increased the number of weeks prime-age men spent out of the labor force up to 26 years later. However, the data used in this research may be overestimating effects since it cannot account for reasons why someone is not in the labor force, including school attendance or because of later incarceration (Ellsworth 2017).

While not specifically measuring effects on prime-age labor force participation, additional research quantifies the way prior convictions—which may or may not result in incarceration—impact future employment. Having been convicted of a felony is estimated to have reduced the employment rate for all men in 2008 by 1.5 to 1.7 percentage points, and by 6.1 to 6.9 percentage points for men without a high school diploma (Schmitt and Warner 2011). Later research using state-level modeling estimated that every 1 percentage point increase in the share of the adult population with a felony conviction is associated with a 0.3 percentage point increase in the rate of nonemployment—including unemployment and being out of the labor force—for adults aged 18 to 54 (Larson et al. 2022).

Leisure activities

As previously discussed, prime age women are much more likely to leave the labor force to undertake family responsibilities, and men rarely report this as the reason for their nonparticipation. But regardless of the reason for their nonparticipation, there is also no evidence that men ultimately use the time they may have otherwise used for labor market activities on household work. Time-use data show that prime-age men not in the labor force spend twice as much time on leisure activities compared with other men, but only slightly more time on housework and caring for children (Krause and Sawhill 2017).

From 2000 to 2015, total market hours worked fell more for younger men ages 21 to 30 than for men ages 31 to 55, and younger men’s detachment from the labor market increased. Computer and video game technology advanced over this same period, which increased the appeal of this leisure time, and younger men significantly increased their time spent gaming. While recognizing other factors such as declining demand for younger men’s labor, researchers have hypothesized that video and computer games are a potential factor that contributed to the reduction in the labor supply of younger men, estimating that increased gaming technology was responsible for up 38% to 79% of the differential in work hours reduction between younger and middle-aged men (Aguiar et al. 2017).

Subsequent research confirms that time spent playing video games increased among men in the 2000s (Krueger 2017; Gray 2019). The increase in time spent gaming was concentrated among men under 30, and nonworking young adult men spent more time playing computer and video games than their working peers did. However, total electronics leisure time was flat over this period because time spent on gaming was generally offset by decreased time watching television or movies, not by reduced job search or labor market activity. And while young men who had recently exited the labor force spent more time gaming than employed men did, they spent less time compared with men who had been out of the labor force longer, undercutting the hypothesis that gaming was the reason for their exit (rather than a consequence of it). Overall, the data suggest that shifting cultural norms have made it more socially acceptable for slightly older and non-employed men to spend time playing video games, not that young men were leaving the labor force in order to devote more time to gaming (Gray 2019).

Real and relative wages

Real hourly wages (adjusted for inflation) for prime-age men without a college degree were meaningfully lower in 2015 compared with the early 1970s, while real wages for men with degrees increased over the same time—although the decline is not consistent throughout the entire period, and real wages for all educational groups did increase in the late 1990s (Binder and Bound 2019).

While an individual’s personal level of pay is important to labor market decisions, there is also evidence that men’s relationship to other men’s wages may have a meaningful impact on their beliefs about the financial returns on the time and effort invested in work and subsequent labor supply. Data from 1980 to 2019 show that noncollege prime-age men are more likely to leave the labor force when their earnings decline relative to other prime-age men. Increases in real earnings may not be enough to offset the effect of inequality; it’s the comparison to what other similar-ages men are paid that seems to matter most. The relationship with women’s wages is weaker, and white non-Hispanic men are driving the relationship, indicating that the LFP of historically privileged groups may be more sensitive to changes in relative economic standing. This decline in relative earnings for noncollege prime-age men is estimated to have contributed to 44% of the decline in labor force participation over this period (Wu 2022).

Additional recent research comparing wages in men’s birth states found a positive relationship between the wages paid to other men starting in an individual’s boyhood and their eventual labor force participation when they reach prime age, even after controlling for labor market conditions and demographic variation. The study found that a $0.33 increase in the average experienced aggregate lifetime hourly wage of men raised the probability of prime-age labor force participation by 10 percentage points. The effects persisted even when men moved states, and were stronger within racial categories with an effect twice as strong for Black men compared with white men. Racial decompositions found that white men were most influenced by the wages of other white men, while Black men were influenced by both Black and white wage trajectories (Levin and Vidart 2025). The data suggest that lifetime wage experiences, and what men see other similar men being paid throughout the life course, may shape beliefs about the returns on work, which in turn, influence labor force participation. This may help explain why men’s LFP continued to decline in the 1990s when real wages rose.

More recent changes to the economy 

The research outlined above was largely conducted using data from the years immediately after the 2008 recession, often with endpoints before prime-age labor force participation started recovering in the late 2010s. Labor force participation declined dramatically in 2020 but rebounded faster than predicted, continuing the upward trend in place before the pandemic. Between 2020 and 2024, the prime-age labor force grew about two-and-a-half times faster than the prime-age population (EPI 2025b). And as of 2024, prime-age men’s participation had regained its 2010 level, while women’s hit a historic high.

Research on the drivers of the rapid recovery and longer-term prime-age LFP increases is ongoing, but there are indicators that the single most important factor might simply be the state of macroeconomic slack. The 2010s saw prolonged and large output gaps that persisted for almost a decade after the business cycle peak in 2007. The more recent post-pandemic recovery was far faster, with output gaps essentially erased 18 months after the previous peak.

Since 2015, when prime-age participation started to recover, researchers have found a consistent procyclical relationship between changes in state unemployment rates and prime-age LFP, a relationship that is not present for business cycles between 1990 and 2014. The wage gains experienced by low-wage workers have been larger during the recent economic expansions compared with earlier periods, and since this groups tends to be more responsive to changes in labor market conditions, it is possible that higher wages for workers at the lower end of the wage spectrum drove labor force participation rates up (Prabhakar and Valletta 2024). However, as wage growth has slowed, this procyclical rise has likely cooled for now.

Prime-age women’s labor force participation fell more than men’s in the early months of the pandemic, declining by 3.4 percentage points compared with men’s decline of 2.8 percentage points, although women’s LFP recovered earlier and more consistently than men’s in 2023 (EPI n.d.). Maternal employment and labor force participation were also deeply impacted by the closure of in-person schooling and child care, more so than for fathers and women without children (Landivar et al. 2023). As a result, in contrast to studies done in the 2010s, much of the post-pandemic research has focused on the labor market experiences of women and mothers.

Labor force participation for mothers whose youngest child was under age 5 hit a record high of 71% in September 2023 (Aron-Dine, Bauer, and Powell 2025). There are a number of factors that could have influenced this outcome, including increased access to telework, as mothers with preschool-aged children are the most likely group of prime-age workers to telework, or this could be the result of the procyclical factors previously discussed.

Earlier analysis found that prime-age women contributed the most to the rebound of the overall labor force participation rate post-pandemic, and among all prime-age women, it was mothers with children under 5 who increased their participation the most from 2019 to 2023. However, this seems to be largely because their participation rate, which was already lower than the rate for all prime-age women and mothers of older children, declined the least among mothers in the labor market collapse period (April–May 2019 to April–May 2020). During the recovery period (April–May 2020 to April–May 2023) prime-age women without minor children had a larger impact on the net change in the labor force participation rate, holding population constant. (Bauer and Wang 2023).

Analysis by the Council of Economic Advisers on the impact of the Biden-Harris administration’s $24 billion in child care stabilization funds, which were issued as subsidies to child care providers, estimates a 2–3 percentage point increase in the labor force participation rate for mothers of children under 6 as a result of the funds (CEA 2023). Labor force participation rates stabilized around the time the funds expired, and after that point, growth in LFP for mothers of young children followed the same patterns as those of other women, lending support to the hypothesis that increased child care funding was driving earlier increases. However, these estimates only control for the expanded child tax credit and state unemployment rates, with no control for increases in telework. Telework increases have also been hypothesized to affect all groups of women similarly, but that finding differs by data source. Analysis using Current Population Survey data shows prime-age parents are more likely to telework than workers without children (Aron-Dine, Bauer, and Powell 2025), while others using Census Pulse Survey data found non-mothers were more likely to telework in the first half of 2023 (Bauer and Wang 2023).

Prospects for labor force participation going forward and how policy can affect them

There are many reasons for comparative optimism about prime-age labor force participation going forward, driven by a partial reversal of a number of pressing social challenges. For one, the low points of the 2010s seem to have been significantly driven simply by excess macroeconomic slack. To the degree such prolonged periods of slack can be avoided going forward, labor force participation rates should avoid similar large slumps. For another, the incarcerated population in the United States has fallen significantly in the past 2 decades. To the degree that the future will see fewer workers scarred by a spell of incarceration, this should boost labor force participation. Further, the high point of the opioid epidemic seems to have passed, and rates of addiction are falling, removing another key headwind to labor force participation.

All of these potential tailwinds to labor force participation are obviously contingent on policy decisions—both economic and social. Further, a number of other margins that will affect labor force participation also will be largely driven by policy. Below, we highlight a number of determinants of labor force participation in coming years and assess how policy can increase or reduce their effect.

Efforts to reduce opioid use further may increase labor force participation

Although the exact effects are challenging to measure due to a lack of comprehensive data, there is some evidence suggesting that the increased use of opioids contributed to declining labor force participation in the late 2000s through mid-2010s (Aliprantis, Fee, and Scheitzer 2023). Since that time, a number of laws at the state and national levels have been enacted in response to the opioid crisis. Federally, the Comprehensive Addiction and Recovery Act of 2016, the 21st Century Cures Act, and the Substance Use Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities Act are intended to lessen the demand and supply of opioids while reducing the harms of opioid use disorder (CBO 2022). These efforts are multifaceted but include strategies such as providing funding to states to invest in prescription drug monitoring programs, increasing budgets for public health services to prevent and treat substance use disorders, and developing treatment alternatives to incarceration.

Tracing the impact of these laws is difficult, in part due to the effects of the pandemic, which contributed to increased opioid use, misuse, and deaths in 2020 (CBO 2022). However, post-2020 some measures have markedly improved. The overall rate of opioid dispensing has declined by roughly 20% since 2019, and opioid deaths involving prescription drugs have declined since their peak in 2017 (CDC 2024; NIDA 2024b). Emergency room visits for suspected nonfatal overdoses related to all opioids also declined over this time period (CDC 2024). At the same time, overdose deaths from any drug and those involving any opioid (not just prescription drugs) continued to increase through 2022 before declining in 2023, although they remain elevated by historical standards (NIDA 2024a).

It is too early to know if these measures will continue to trend downward, but there does not seem to be a simple, straightforward, ongoing connection between opioid misuse and labor force participation. Overdose rates are not a perfect proxy for misuse, but deaths from synthetic opioids increased dramatically after 2014 and remain very high, largely caused by illicitly manufactured fentanyl (NIDA 2024c). This occurred at the same time that prime-age labor force participation has also been increasing. It is possible that there are more complex relationships developing between opioid misuse and LFP, particularly as the opioid crisis changes over time.

Reducing the labor market scarring of incarceration

For Black men in particular, incarceration presents a uniquely challenging obstacle to gaining employment and rejoining the labor force (Pager 2003; Williams, Wilson, and Bergeson 2019; Holzer, Offner, and Sorenson 2005; Ellsworth 2017). At least 1 in 5 Black men will experience incarceration at some point in their lives (Robey, Massoglia, and Light 2023). These results suggest that any successful policy effort to reduce incarceration and recidivism rates would be highly supportive of labor force participation. While recent ban-the-box policies (such as those that do not require job applicants to disclose their criminal history for most jobs) have had mixed results in their ability to promote overall employment (Rose 2021), Bailey et al. (2024) found that children in households that received food stamps had a reduced likelihood of being incarcerated as adults later in life by 0.5 percentage points, suggesting that meeting families’ basic needs can do more than just improve health.

More promising than the ban-the-box policies is California’s 2011 policy to redistribute the costs of sending an adult to prison to the governing locality that makes the decision to incarcerate. This policy is associated with a reduction in the prison population of 50,000 between 2009 and 2019, suggesting that public financing policy can play a surprisingly effective role in supporting labor force participation (Pfaff 2024). The law, AB 109 or colloquially referred to as realignment,” mandated that nonviolent, nonsexual, and nonserious offenders were required to serve sentences under county supervision. Prior to the law, prosecutors, who are paid by the county, were incentivized to prosecute offenses to their highest conviction to get offenders sent to prison, which was paid for exclusively by the state. This redistribution of costs significantly curtailed prosecutors’ incentives to seek higher sentences for less serious offenses and as a result, reduced incarceration rates in California substantially.

Job quality matters to attract workers into the labor market, particularly into some of the fastest-growing occupations

A key headwind for men’s labor force participation in the past few decades has been a slowdown in job growth in sectors like manufacturing and mining that traditionally provided relatively high wages for workers without a college degree. Much of the change in the composition of employment is largely outside the purview of policymakers—but policy can have some effect on the margins of this employment composition. More importantly, how changing employment composition translates into changes in wages or perceived opportunities for different population groups is highly contingent on policy.

Occupational segregation is the tendency for one gender to more likely work in certain occupations than another. For instance, men are more likely to work in manufacturing and construction, while women are more likely to work in education and health care (industrial sectors are provided in Appendix Table 3, but the same phenomenon exists in occupations). Gender stereotypes, such as the idea that women are better suited to caregiving or that men are naturally better at physically demanding tasks, can constrain people’s options and make them more or less likely to pursue traditionally gendered jobs (Palffy, Lehnert, and Backes-Gellner 2023).

Occupational segregation is driven by social and cultural forces that compel women into caring professions (Schieder and Gould 2016). While many people do have choices about which jobs to apply for, accept, or reject, these decisions are made within the context of larger social and cultural influences. Occupational choices are shaped by a lifetime of experiences, including the expectations children are raised with, educational experiences, hiring practices, and norms and beliefs about family roles and the division of household labor held by employers, co-workers, and society. These norms and expectations impact women’s as well as men’s occupational “choice.” As we’ve shown above, the loss of both manufacturing and military jobs in the U.S. came at a cost to men in particular. On the flip side, the growth in jobs in health care will disproportionately benefit those more likely to work in health care, in this case, women.

Figure J illustrates the occupations expected to gain the most jobs, in percent terms, between 2024 and 2034 (BLS 2025a), as well as the share of women in those four occupations in 2024. The industries shown are expected to grow at least twice as fast as the average rate of 4%.

Three of the four fastest-growing occupation groups are dominated by women. The fastest-growing occupation group over the next 10 years—health care support occupations—is expected to grow by 12.4% and is comprised of jobs that pay lower-than-average wages. The median wage in health care support occupations is about three-fourths the median wage overall ($37,000 versus $49,000). Currently women make up about 84% of workers in health care support occupations. Low pay is both a cause and effect of occupational segregation. Jobs in which women are overrepresented tend to provide lower pay and fewer benefits than male-dominated occupations do, and wages tend to fall in occupations as the share of women increases (Levanon, England, and Allison 2009).

Figure J

For workers of any gender to enter those faster-growing occupations, those jobs need to be better. That means better pay, better working conditions, and better benefits. Stronger labor standards, such as a higher minimum wage and overtime protections, can improve those jobs and make them more appealing to a broader range of workers. Increased unionization can also improve pay in those jobs. On average, workers in unionized jobs are paid about 12.8% more than workers in nonunion jobs (EPI 2025c) A key reason jobs in manufacturing could support a middle-class lifestyle was the high unionization rates. There’s no reason currently low-paid health care support occupations couldn’t enjoy such conditions. The number and share of unionized workers in health care support jobs has recently increased, and their wages are higher than those of their nonunion counterparts (BLS 2025b; BLS 2025c).

Labor force participation is more resilient in peer countries

Figure K compares the United States with the OECD average prime-age labor force participation, 1976–2024, women on the left and men on the right. While they display similar overall trends at the endpoints—upward for women and downward for men—there are some notable differences. In the OECD countries, men’s participation also fell between 1976 to the early 2000s, but the losses tapered off quickly, and today, participation remains around its 2000 level. In the United States, men’s participation continued to drop, most notably during the Great Recession and prolonged recovery before starting its upward climb as the economy expanded.

While it is the case that many of our peer countries in the OECD also experienced downturns, particularly in the Great Recession, their labor force participation rates did not fall as far, largely due to different policy responses. Policies such as work sharing and time banking that provide support for workers to stay on the payroll helped blunt the impact of the Great Recession in places like Germany, which saw its unemployment rate tick down at the same time the rate in the U.S. more than doubled (Baker 2018).

Figure K

Women’s labor force participation never stopped its upward rise in the OECD average, even while it softened in the United States following 2000. The steep gains in participation in the U.S. tapered off significantly, while it continued to rise in the OECD until today. The policy environment around work for women is quite different, particularly in Western European countries, which have stronger family leave and child care supports.

There is meaningful evidence that the lack of work-family policies and relatively sparse care infrastructure in the U.S. depresses women’s labor force participation. In 1990, out of 22 OECD countries, the U.S. ranked 6th for women’s prime-age labor force participation, but by 2010 had fallen to 17th place. The lack of family-supportive policies in the U.S., such as paid parental leave and publicly provided child care, can explain 29% of the decline in the U.S.’s ranking of female LFP relative to other OECD countries (Blau and Kahn 2013).

In the subsequent 15 years, the gaps between policies in other OECD countries and the U.S. have typically widened. Compared with other high-income OECD countries, the U.S. is now even more of an outlier on nearly every workplace policy that could help boost labor force participation among workers with family responsibilities.

Since 2010 the total amount of paid parental leave available to two parents in OECD countries has increased from an average of 58.1 weeks to 64.6 weeks (OECD 2024). Yet the United States remains an extreme outlier and is one of the only countries in the world that does not guarantee workers the right to any form of paid parental leave. Across the other 37 OECD countries, mothers are eligible for an average of more than one year (53.5 weeks), and fathers are eligible for more than three months (13 weeks) of paid leave.

Families in the United States also pay more on average for child care than families in other OECD countries. In the U.S., a single parent paid the average wage would need to spend 40% of their wages to pay for center-based care for two toddlers—about 5 times the cost burden (8%) for the OECD, on average (OECD n.d.). And while net costs increased for U.S. families, they declined in most other OECD countries, with the overall OECD average dropping from 15% to 8% between 2004 and 2023.

The cost burden is much greater in the U.S. compared with other countries where child care fees are similarly high or higher because the U.S. does not provide meaningful benefits like child care allowances or fee rebates to help families reduce their financial costs. While there are tax credits that allow some working parents to write off child care expenses, not all families qualify, and the overall impact on net costs is minimal.

The share of GDP the United States spends on early childhood education and care has declined since 2010, while the OECD average has increased (OECD Social Expenditure Database n.d.). In 2021, the last year with complete data on all 38 OECD countries, U.S. spending (0.3% of GDP) was less than half the OECD average (0.7%).

Policies related to remote work and workplace flexibility—such as the ability of workers to alter their start and stop times—were not part of the original analysis conducted by Blau and Kahn (2013). However, flexibility and remote or telework options have been identified as important policies to support labor force participation, particularly among mothers post-2020. As of April 2024, 25 of the 38 OECD countries had laws in place allowing workers to request flexible schedules, remote work, or both (World Bank 2024).

The 2019 Work-Life Balance Directive2 created a right for workers in the European Union to request flexible work arrangements, including remote work, to better coordinate work with family caregiving responsibilities. The law does not guarantee that employers will grant approval to every request, but they are required to seriously consider requests for flexibility and must provide reasons for refusing requests. In the United Kingdom, workers’ rights to request flexible work arrangements were expanded through the Employment Relations (Flexible Working) Act 20233. Workers in the U.K. now have a legal right to request flexibility starting from their first day of employment rather than having to wait 26 weeks before making the request as they did previously.

In the United States, workers do not have an explicit legal right to request remote work or workplace flexibility, and employers are not required to consider such requests when they are made. Although the data are not conclusive, there are indications that increased access to telework during and after the pandemic enabled greater labor force participation, including among mothers of young children. Broadening access to flexibility and remote work would likely further increase entry or reentry into the labor force among workers with caregiving responsibilities, as well as supporting continued participation for current workers.

Investing in children is a long-run strategy to increase labor force participation in the future

Previous sections noted the sharp increase in college attainment among the U.S. population in recent decades and also noted that college graduates saw much slower rates of declines in labor force participation than noncollege workers did. The public sector has supplied the majority of financing for higher education in the United States for the entire post-World War II period. In short, the boost to labor force participation (and economic growth generally) supplied by higher education was a policy choice.

Policy choices about how prepared future generations will be to participate in the labor force are not just confined to education spending (though that is obviously important as well). Investing in children by supporting their basic needs such as food, medical care and child care has been shown to have demonstrable long-term effects on health and economic sufficiency. These, in turn, support attachment to the labor market. Early childhood is a sensitive period, and investments in children tend to have large benefits as they age (Cunha and Heckman 2007; Heckman 2008). Additionally, a stronger welfare state raises the income and resources of a child’s family (Ruhm and Waldfogel 2012). Importantly, these benefits tend to outweigh the costs of the program or any potential impacts on the parents (Aizer, Hoynes, and Lleras-Muney 2022).

Long-term studies have tracked children in households with access to food stamps (SNAP), early childhood education, and Medicaid to assess the impact of these programs on these children as adults. With respect to food stamps, Hoynes, Schanzenbach, and Almond (2016) found that access to food stamps for households with children led to statistically significant improvements in measures of metabolic health when they were adults. Moreover, researchers found positive impacts of receiving food stamps on economic sufficiency (high school completion, use of food stamps, and earnings), with statistically significant increases among adult women who receive food stamps. Bailey et al. (2024) linked the 2000 Census and 2001–2013 American Community Survey to information from Social Security to examine how SNAP program rollouts from 1961–1975 impacted children as adults. They found that children with access to food stamps before age 5 have better outcomes as adults in the form of increased economic self-sufficiency (3% standard deviation increase), human capital (6% SD increase), quality of neighborhood residence (8% SD increase), and a 1.2-year increase in life expectancy. 

Several studies have also documented the long-run impact of Medicaid with implications for labor market participation. Miller and Wherry (2019) studied infants who gained access to Medicaid in utero via their mother’s prenatal coverage. They find that infants with prenatal coverage had lower rates of chronic health conditions as adults, fewer hospitalizations, and increased high school graduation rates. Thompson (2017) examined the long-term impact of Medicaid access and found that each additional year of Medicaid eligibility during childhood improved overall adult health (self-score evaluations) and reduced chronic conditions and asthma prevalence as adults. Given that disability and chronic health conditions are some of the main reasons that individuals stay out of the labor force, these studies show that access to Medicaid as a child can promote the conditions that would lead to labor force attachment.

Finally, Brown, Kowalski, and Lurie (2020) use tax data to estimate the long-term impact of Medicaid eligibility in childhood on a variety of outcomes measured at ages in early adult life. They find that eligibility for Medicaid during childhood increased college enrollment rates, delayed fertility, reduced mortality, and reduced dependence on EITC benefits, and led to higher tax payments among adults, suggesting that access to Medicaid has the long-term benefit of improved economic self-sufficiency and employment.

While the U.S. doesn’t have a national pre-K early-childhood program, studies of individual programs show promising results. Chicago’s Child-Parent Center Education Program preschool was linked to higher educational attainment and socioeconomic status, a higher likelihood of health insurance coverage, and lower rates of justice-system involvement and substance abuse (Reynolds et al. 2011). Michigan’s HighScope Perry Preschool program was linked to fewer arrests, higher earnings, and higher educational achievement and attainment (Schweinhart 2005), and careful cost- benefit analysis estimated that every dollar invested at age 4 yields a return of $60–$300 by age 65 (Heckman et al. 2010). Additionally, studies of state-introduced universal kindergarten programs in the 1960s and 1970s found that this additional early childhood education increased both educational attainment for some groups of students (Cascio 2009, 2010; Dhuey 2011); and labor market outcomes in the form of weeks worked and wages (Dhuey, 2011), suggesting that early childhood education interventions can support labor market attachment.

Studies in Europe have documented the impact of pre-K and early childhood care on long-term outcomes. In Denmark, researchers found that early increased preschool density was positively associated with completed schooling, particularly for daughters of less educated mothers, and later adult earnings (Bingley and Westergaard-Nielsen, forthcoming). In France, researchers found that the large-scale universal preschool program increased test scores, high school graduation rates, and adult wages, with larger effects for children from disadvantaged backgrounds (Dumas and LeFranc 2010). In Norway, an expansion of subsidized child care led to increased educational attainment (more years of schooling, higher rates of college attendance) and labor market participation (Havnes and Mogstad 2011).

The role labor force participation rates play in the economic future of the U.S.

Labor force growth is a key element of economic growth more generally. At the most basic level, growth in overall gross domestic product (GDP) over brief periods of time can be proxied as the sum of the growth rates of the labor force and of labor productivity—with productivity defined as the amount of output generated in an average hour of work in the economy. Given this, every percentage point rise or fall in the growth rate of the labor force translates one for one into a corresponding change in overall GDP growth.

In coming decades, the question that matters more than any other for projecting labor force growth for the U.S. economy is the pace of net immigration. For example, the Congressional Budget Office projects that the U.S. labor force will grow by just under 7% from 2025 to 2035 (CBO 2025a). But if the influence of immigration flows is removed, this growth will fall to just 0.5% over the entire next 10 years.4

There is no realistic scope at all for changes within U.S.-born labor force participation rates to fundamentally change this and lead to significant increases in the labor force over the next decade. Most importantly, the U.S.-born population is aging fast. Over the next 10 years the share of the U.S. adult population over the age of 65 will rise by another 4 percentage points (to over 27%). Given the gap in labor force participation rates for workers aged 65–74 and those under the age of 65, this translates into a reduction in the overall labor force participation by roughly a full percentage point over the decade—a powerful headwind to growth.5

In theory, the CBO has taken some account of the fact that major headwinds to growth in prime-age participation rates over the past decade or so should likely reverse (or at least, dial down) in the next 10 years. These headwinds include excess labor market slack, the stock of prime-age adults with some spell of incarceration in their past, the prevalence of opioid addiction, and the steady shrinkage of military employment scaled against the civilian workforce. If none of these past headwinds to labor force participation were taken into account in CBO projections, their reversal could conceivably add 1–2 percentage points to prime-age labor force participation rates over the next 10 years. But, again, this doesn’t come close to rivalling the potential effects of changes in net immigration, and CBO has likely accounted for a number of these influences in their projections, at least in part.6

If one of the more ambitious long-run strategies for boosting future labor force participation highlighted in the previous section was undertaken (large investments in child health, nutrition, and education for example), these effects could conceivably add another percentage point to labor force participation rates, but only at a quite long time horizon (well over 10 years).7

One upshot of the dominance of immigration flows in conditioning future labor force growth and the continued downward pressure on labor force growth imposed by the aging of the U.S.-born population is that anybody promising large increases in GDP growth in coming years without calling for higher rates of immigration will have a very hard time fulfilling this. Again, every percentage point decline in the growth rate of the labor force subtracts a percentage point from GDP growth, and changes in labor force growth in the coming decade will be driven near entirely by immigration inflows.

Of course, GDP growth is (roughly) the sum of growth in the labor force plus the growth of productivity. In theory, a slower growth rate of the labor force could be overcome by a surge in productivity growth, and overall GDP growth could still rise. However, productivity growth over the past century in the U.S. economy has fluctuated with a relatively narrow band—essentially between 1% and 2% annually. Since the 1960s, spells of productivity growth over 2% have been rare—just the late 1990s and early 2000s. It is theoretically possible that we are in a stage currently where technological change will accelerate and productivity growth will surge to the higher bands of its historical experience, but this is very hard to bank on. Promises of future growth surges from other technological changes (like robotization in the 2010s) yielded real, but quite modest, productivity growth.

But while productivity growth is unlikely to generate historically fast GDP growth in coming decades, it is the most relevant part of the growth equation to focus on. A higher GDP driven by a larger labor force does not necessarily raise living standards. It is productivity growth alone that makes a country richer over time in the most relevant sense—providing the potential for higher living standards per person.

By far the most substantive way that differing rates of labor force growth can affect Americans’ economic future is through the tax and transfer system. The federal government in the U.S. has historically taken on the role of ensuring adequate income in retirement for all citizens by running social insurance programs—Social Security and Medicare—through the nation’s fiscal system. Very roughly speaking, current workers are taxed to provide benefits to current retirees. As the share of the population that is retired rises relative to the stock of current workers, this means a higher share of workers’ output needs to be devoted to providing income for retirees.

This need not imply any pronounced economic pressure. Productivity growth means that even if a rising share of workers’ incomes is devoted to social insurance for current retirees that workers’ net-of-tax income levels can still rise steadily over time. But this demographic angle of the large social insurance programs run by the federal government does pose potential political challenges. These political challenges could well be lessened by policy decisions that keep the ratio of current workers to current retirees higher than it otherwise would have been—and here is where issues of labor force participation could matter.

Conclusion

Labor force participation is both an input and a consequence of a healthy economy. While there is no ideal labor force participation rate that policymakers should target, they should target any barriers that are keeping willing workers from being able to actively search for work. These barriers include too-slack labor markets stemming from macroeconomic policy failures; labor market discrimination; insufficient investment in workers’ health, skills, and credentials; and a failure to make investments needed to enable parents with young children to also participate meaningfully in the labor market.

Outside of immigration, however, the changes to labor force participation that can be leveraged by even quite ambitious policy changes will be relatively small and will not meaningfully change the trajectory of the U.S. macroeconomy over a decade or so. This does not mean they are not worth doing, instead it means that policymakers should be realistic when claiming that future economic growth can be boosted by increasing growth in the U.S. labor force.

Appendix

Appendix Table 1

Appendix Table 2

Appendix Table 3

Acknowledgments

The authors thank Katie deCourcy and Stevie Marvin for research assistance and Grace Park for editing. This project was made possible by financial support from the Peter G. Peterson Foundation.

Notes

1. We should note that this change in employment shares by skill- or credential-grouping does not predict at all accurately any related change in wages. In short, one can believe that changing employment shares by occupation—even those driven by technological changes—fail to move relative wages or inequality in any significant way, and that non-relationship between employment and wage changes by occupation is validated in the data (see Mishel, Schmitt, and Shierholz 2013).

2. Council Directive 2019/1158, 2019 O.J. (L 188), 79–93.

3. The Employment Relations (Flexible Working) Act 2023, c. 24 (UK), https://www.legislation.gov.uk/uksi/2024/438/made.

4. Authors’ analysis is based on information in CBO 2025a, b. The size of the over-19 labor force over the next decade is provided directly in CBO 2025b. This data also provide the share of growth in the over-19 population that is accounted for by immigration. To obtain the counterfactual growth, we just removed the portion of growth associated with immigration each year and recalculated the level of the labor force for each year in the next decade.

5. Numbers in this paragraph are based on authors’ analysis of data in CBO 2025a, b. CBO 2025b reports that the share of the over-64 population will rise as a share of the total adult population by almost exactly 3 percentage points between 2025 and 2035. Currently, the LFPR for workers between the ages of 65 to 69 is almost exactly 30 percentage points lower than for workers between the ages of 55 to 64. Multiplying these together (which gives 0.9%) should give a very rough sense of the downward pressure on labor supply stemming from aging.

6. Schmitt and Warner (2011) estimated that the scarring effect of incarceration could reduce the employment-to-population ratio of men by between 0.6 to 2.6 percentage points by 2008. Given that the stock of incarcerated men has fallen by roughly 20% since its highest point (and a bit more than this as a share of the population), this penalty going forward could have been reduced by 0.15 to 0.6 percentage points. In regard to opioids, given estimates that rising opioid use throughout the 2000s could have reduced labor force participation rates by as much as 1 percentage point, any leveling off of this could remove a powerful headwind to labor force growth, and any affirmative reduction in the incidence of opioid addiction should, in theory, potentially boost labor force growth.

7. Most estimates of the effect of early childhood investments—whether it be early education, health, or nutritional investments—report the effect on earnings of exposed children when they become adults. Assuming a package of investments in today’s children were able to boost their earnings by 5% when they became adults (which seems plausible given that early childhood educational investments alone have been estimated to increase annual earnings of exposed children by over 20%, and the share of today’s children not currently receiving high-quality early childhood education is estimated to be over half of all children (see Lynch and Vaughul 2015)). If increased labor force participation accounted for a fifth of this total earnings effect (as opposed to lower unemployment rates, higher hours worked during a year, and higher hourly wages), then a range of estimates would indicate that these investments could boost the adult labor force participation rates of today’s children by roughly a percentage point. It seems plausible that increased labor force participation could, by itself, explain a fifth of projected future earnings. For example, annual earnings of workers with a college degree are roughly 60% higher than with only a high school degree. This 60% difference can be very roughly expressed as the sum of differences in labor force participation, unemployment rates, hours worked per year, and average hourly earnings. Labor force participation rates for workers with a bachelor’s degree or greater are roughly 12% higher than for workers with only a high school diploma , which is roughly a fifth of the total difference in annual earnings. 

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All children deserve to attend welcoming and well-funded schools where they can learn and grow, regardless of race, disability, or income. But funding for public schools, where nearly 90% of all U.S. students learn, is at a near crisis point. The Trump administration’s goals, which are taken right out of Project 2025, seem to be to defund public education to the point that it doesn’t work, then offer private school vouchers as a solution to a manufactured problem. In this post, we highlight five ways public education is on fire in the United States and the damage this will do to students’ abilities to learn and thrive. Instead of cutting funds, lawmakers should invest in public schools, one of the best tools we still have to build a prosperous, equitable country.

Alarm level 1: COVID-19 relief funding for public schools is winding down. In some cases, the administration is ending it prematurely

This academic year (2024–2025) marks the end of the financial support schools were receiving to address the impacts of the COVID-19 crisis, the Elementary and Secondary Schools Emergency Relief III funds (ESSER III). The COVID-19 pandemic, and the changing learning environments that ensued, meant that schools needed funds to address the significant academic, social, emotional, physical, and mental health needs of their students. This funding was distributed in recent years with the last distribution, ESSER III, worth a total of $122 billion allocated to districts around the country. Many students, especially those living in poverty, have not recovered from pandemic-related learning loss. The end of this funding means that districts will now have fewer resources to help students get back on track. Rigorous research has demonstrated that this federal aid to public schools was highly successful, with measurable improvements to student outcomes in states and districts where more aid was spent. Taking the educational challenges imposed by the pandemic seriously would mean recognizing the high value this aid has provided.

However, in late March, the Trump administration canceled extensions that had been granted to states to spend remaining ESSER funds. Effectively, districts are losing out on the funding allocated to them in the form of COVID-19 relief funds. Canceled extensions represent almost $3 billion in lost funding that had already been committed to tutoring services, reading interventions, building improvements, and more. Clawing back these funds jeopardizes improved academic outcomes for many students and their ability to learn in healthy and safe environments. The administration’s refusal to reimburse school districts for funding that has already been spent could force them to cut teaching and other staff positions to make up the cost, ultimately harming students.

Alarm level 2: The administration is lawlessly dismantling the Department of Education and attacking inclusive schools

The winding down and clawing back of ESSER funding are simultaneously occurring at a time when President Trump signed executive orders to (1) dismantle the Department of Education and “return the funding to the states” and (2) regulate curriculum taught in the more than 13,000 public schools in the country.

One order directed Secretary of Education Linda McMahon to shut down several functions of the Department of Education (ED) and send them back to the states. Prior to this order, the White House had directed the ED to lay off 1,300 employees, a directive that is currently in litigation. The other order resulted in a “Dear Colleague letter” from Secretary McMahon demanding that states certify that they will not engage in “illegal DEI practices” as a condition of receiving the federal funds (This order is also currently in litigation.). As it stands, much of the Department of Education funding goes directly to state and local school systems. The ED provides targeted funding to public schools for special education through the Individuals with Disabilities Education Act and supports high-poverty districts through Title I grants. These grants make up for shortfalls in funding that high-poverty districts experience when they get funding from local sources.

To be clear, closing the Department of Education, and reappropriating major funding programs requires an act of Congress, and it is local school districts who have control over what is taught in schools—not federal regulators. Thus, while these executive orders have the potential to inflict a lot of damage, it’s unclear whether these orders can proceed without running afoul of federal laws. If these orders result in delays in funding distributions or outright cuts, students could experience declines in academic achievement, exacerbating existing racial and income disparities and limiting students’ long-term opportunities. If President Trump acts outside of his authority to slash the agencies’ work, the guardrails will essentially be pulled off this funding, which is extremely effective at redistributing funds based on district need. President Trump says he’ll return money to states for them to distribute it, potentially creating a situation where states have to compete for funds. This would create a patchwork in public funding for public schools, one in which some districts risk falling even further behind.

Alarm level 3: Lawmakers are pushing a mounting wave of voucher programs, an increasingly large cost to state-funded education

While many school districts struggle to maintain basic education funding, school privatization efforts are continuing throughout the country, and states like Arizona, Florida, and Ohio are notorious for the budget-breaking cost of universal voucher programs.

Figure A shows the current cost of voucher programs as a share of K–12 education funding in states where over 5% of the budget is currently going to school voucher programs. In the current school year (fiscal year 2025), voucher costs make up anywhere from 5% for states with early voucher programs to upwards of 25% of the entire public education budget for states with mature programs.

Figure A

Because statewide private school voucher programs are funded with state dollars, voucher spending is shown as a proportion of state education funding rather than state and local funding. On average, about 46% of funding for K–12 schools comes from state revenue sources. In states with voucher programs, private schools divert state dollars that could otherwise be available to public schools. For now, local funding for public schools is protected from diversion to voucher programs, although some states with voucher programs are also threatening this source of public school funding by cutting or eliminating property taxes.

Vouchers degrade the quality of education for students who use them

Time and time again research has shown that vouchers harm academic outcomes. Causal studies across three states and Washington, D.C., demonstrate negative effects on test scores for students who use a voucher to switch from public to private school. These test score declines can persist over two years or more and are comparable or worse than declines due to COVID-19 and Hurricane Katrina. Meanwhile, students who leave private schools and return to public schools have experienced increased academic achievement. While some may argue that test scores from the National Assessment of Educational Progress indicate that private school students fare better academically than their public school peers, this is more a reflection of the parents’ socioeconomic status and education level than the impact of private schooling on students. Research also suggests vouchers do not reliably improve high school graduation and college attendance rates. Because of these reasons, lawmakers looking to improve student outcomes should not pursue vouchers.

School vouchers have costs for students who remain in public school

In addition to the direct costs that the state incurs for school vouchers, school districts experience an additional cost when they lose students to private school: the cost of providing the same level of education for fewer students in public education. This cost is entirely borne by the students who remain in public education, even though they affirmatively did not make the choice to take up vouchers. When students leave public schools with a voucher, the school districts must still pay the same amount for costs that can’t immediately adjust to declines in enrollment, such as cooling/heating and utilities. These required payments for a district’s fixed costs mean that districts will have even less to spend on the costs that can adjust due to changes in enrollment. What this means is that public school students who remain in public school will have less funding allocated to them for adjustable costs like teaching, curriculum development, and pupil support services due to other students taking up voucher programs. (To calculate this cost for your district, see EPI’s fiscal externality calculator).

Alarm level 4: National voucher proposals threaten public schools throughout the country

Beyond state voucher programs, Congress is considering national voucher proposals. This would enlarge the scope of vouchers beyond Republican-controlled states. The Educational Choice for Children Act, or ECCA, (H.R. 817, S.292) is a proposal to create a national voucher program. The program would divert over $10 billion per year in tax dollars to private schools and families who homeschool. The bill would do this by establishing a new dollar-for-dollar tax credit for individuals and corporations that make charitable contributions to organizations that give scholarships— or vouchers—for students to attend private schools. Donors who give corporate stocks would receive more back in tax cuts than the after-tax value of the stocks if they had sold them. 

Beyond vouchers harming student educational outcomes, the program itself would be extremely expensive. The bill proposes that Congress allocate $10 billion in tax credits for the voucher programs. But that doesn’t even account for the cost of voucher programs to public schools. The sponsors of the bill estimate that ECCA would provide vouchers for 2 million students. Given that at least two-thirds of students who take up vouchers previously attended private school, we can estimate that 666,667 voucher recipients will come from public school, which is about 1.4% of total public school students. Using our fiscal externality calculator, we estimate that students who remain in public schools would lose an average of $151 per pupil, and public school systems would lose a total of $6.225 billion dollars due to a national voucher scheme.

Alarm level 5: Tax cuts reduce available revenue for public schools

Many states are following a recent trend of reducing revenue available for schools through sweeping tax cuts. Corporate and personal income tax revenue represents about half of state tax revenue, which, in turn, funds about half of K–12 education budgets. From 2021 through 2024, 28 states passed personal or corporate income tax cuts, which will result in hundreds of billions of dollars in lost revenue by 2028, and more states are considering or have passed income tax cuts in 2025. At the same time, some states are cutting and attempting to eliminate property taxes, which account for over a third of revenue for K–12 education on average. States that want to invest in opportunity and long-term economic prosperity and to help their students continue recovering from pandemic-related learning loss should reverse this harmful trend.

Conclusion: What would happen if we boosted public school funding instead?

Given the real and damaging threats to public school funding, we conclude by asking what students actually need to succeed. Growing evidence over the last decade shows that public schooling in the United States simply needs more resources to deliver even better student achievement—not some radical disruption in how it is delivered and by what institutions.

For example, research has shown that school finance reforms between 1972 and 2010 led to a 10% increase in school spending for 12 years, which increased high school graduation rates, wages and family incomes in adulthood for children from districts with the spending increase. Others have similarly found that a $1,000 increase in per-pupil spending for low-income districts would reduce the test score gap between low- and high-income school districts within a state by nearly 40% of the baseline gap.

Increasing funding, rather than withholding federal aid or using public dollars to pay for private schooling, is the path forward for public schools. Public schools have fallen short in many communities because of lawmakers’ choices to underfund them. But the only education system that can fulfill the promise of equal opportunity for all children, regardless of race, disability, or income, is a fully funded system of public schools. Lawmakers interested in building prosperous communities should invest in public schools rather than defunding and privatizing them.

Set at an adequate level, the minimum wage is one of the strongest policy tools for improving the economic security of low-wage workers, and an effective tool at lowering poverty. Yet instead of addressing this massive hole in our economy’s social safety net by working to raise the minimum wage, congressional Republicans are pushing policies like imposing work requirements on safety net programs and cutting Medicaid. Supporters of these proposals characterize them as tools to incentivize work and protect the dignity of work, but these policies fail to account for the nature of low-wage work in our economy. Instead, they stand to deepen hardship for low-income workers with no economic upside for working people or the larger economy.

The minimum wage and the federal poverty line

When the minimum wage was created as part of the Fair Labor Standards Act in 1938, the policy was intended to protect the nation from “the evils and dangers resulting from wages too low to buy the bare necessities of life.”1 The federal wage floor is clearly not fulfilling this objective anymore because of a historically long period of inaction by Congress. The last time Congress increased the federal minimum wage was in July 2009, meaning that as prices have risen over the last 15 years, the value of the minimum wage has fallen by 30%. Figure A shows how annual earnings for a full-time minimum wage worker fall short of the poverty line for a household of any size.

Figure A

This comparison severely understates the economic vulnerability of these workers and their families. This is because the federal poverty guidelines—which are used at the federal, state, and local level to determine eligibility for public programs like Medicaid and SNAP—are informed by the Census Bureau’s official poverty measure (OPM), a reductionist measure of poverty. The OPM relies solely on a multiple of the current cost of the minimum food diet from 1963 to calculate the poverty line and identify the poor. The Census also publishes a more expansive measure of poverty known as the supplemental poverty measure (SPM), which accounts for the cost of a broader basket of items including food, clothing, shelter, utilities, internet and telephone, but this latter measure does not inform the poverty line used to determine eligibility for public programs.

As Figure B demonstrates, the share of workers in poverty is significantly higher when we rely on the SPM instead. By this measure, more than 10 million workers (7.0%) between the ages of 18 and 64 failed to earn enough to avoid economic deprivation in 2023, the latest year for which these statistics are available, whereas the OPM captured only 4.5% of all workers.

Figure B

The discrepancy between the federal minimum wage and the real experience of workers throughout the country has led 30 states and Washington, D.C., to increase their minimum wage above the federal level. In the 20 states still using the federal minimum, 11.8 million workers earn less than $17 per hour, more than 1 in 5 workers in those states. Those states are disproportionately located in the South. The stagnation of the federal minimum wage allows Southern policymakers to maintain low wages in their economies. Southern workers have lower earnings even when adjusting for cost-of-living differences between regions. In part due to wage-suppressing policies like a low-minimum wage, Southern workers experience greater poverty than those in other regions.

Increasing the minimum wage boosts earnings and reduces poverty

The federal minimum wage is a powerful tool in fighting poverty in the U.S. The best economic research has consistently shown that increasing the minimum wage lifts earnings for low-wage workers, with little to no impact on employment. Research shows that increasing the minimum wage decreases poverty by increasing the incomes of low-income families, even accounting for decreases in public benefits as families earn more from higher wages. In analysis of legislation introduced in 2021 to gradually increase the federal minimum wage to $15 an hour, EPI concluded that the policy would lift between 1.8 to 3.7 million individuals out of poverty, including up to 1.3 million children. 

Despite persistent opposition from the business lobby and obstruction from conservative policymakers, raising the minimum wage remains popular among the public, and some legislators keep raising the call for federal action on this issue. Recently, members of Congress led by Sen. Bernie Sanders (I-Vt.) and Rep. Bobby Scott (D-Va.) once again reintroduced the Raise the Wage Act, which would gradually increase the federal minimum wage to $17 an hour. This would raise wages for more than 22.2 million workers, 4.2 million of whom live in households below the poverty line.

By contrast, Republican policies will make it harder for workers to escape poverty

While the minimum wage has been left to wither, Republican budget proposals in 2025 will either erode other elements of the social safety net or make them much harder to access. Republicans seek to cut Medicaid and ratchet up work requirements on both Medicaid and SNAP.2 This will harm low-income workers and their families, as these social programs help improve the living standards of millions of workers who don’t earn enough to avoid economic hardship. In 2023 alone, social programs that rely on the poverty guidelines kept more than 7 million individuals out of poverty.3 

Republicans have framed cutting benefits and expanding work requirements as a way to encourage people to work. The justification for these proposals is that generous Medicaid and SNAP benefits should be pared back because they encourage recipients to depend on government assistance instead of working. This seems to overlook the fact that two-thirds of non-elderly Medicaid enrollees and more than 85% of working-age adults who receive SNAP do work.

This conservative philosophy is an old idea that is deeply wedded to racist stereotypes about Black families being users of welfare programs. However, evaluating these proposals on their economic merits shows that they will increase hardship for low-wage Americans without creating economic benefit. Medicaid cuts at the levels proposed by Republicans would reduce incomes of low-wage families significantly, including a 7.4% reduction in income for families in the bottom 20% of the income distribution. Medicaid is also a vital investment in low-income children, who grow up healthier and with better education and income outcomes because of the Medicaid support they receive. Research suggests that Medicaid pays for itself through this investment in poor children. Cutting Medicaid will likely reduce these children’s educational achievement and wages earned over their lifetimes.

Similarly, research shows that adding work requirements to benefit programs is a punitive choice with no upside. Studies of work requirements on Medicaid and SNAP find little to no increase in employment outcomes in places where the policies have been implemented. What these policies do achieve is to make it harder for individuals to access the benefits they are eligible for.

A reason why work requirements are ineffective is that they do not account for the precarious nature of low-wage work. Unpredictable scheduling practices are pervasive in low-wage jobs, including cancelled shifts and short notice changes to shift schedules. Low-wage workers also frequently change jobs in an effort to find better-paying work. The scheduling unpredictability and level of turnover in many low-wage jobs can make it difficult for workers to fulfill the consistent work-hour requirements needed to satisfy work requirement policies. Work requirements effectively act as cuts to existing beneficiaries and limit new participants who have little control over the labor market conditions associated with low-wage work.

Conclusion

The minimum wage is a powerful tool for increasing the economic security of low-wage workers. Yet Republican lawmakers have repeatedly denied increases in the federal minimum wage and are now pursuing a tax and budget plan that would cut Medicaid and limit access to safety net programs to finance tax cuts for the richest Americans. If it goes into effect, the combination of tax cuts and Medicaid cuts would effectively lower incomes for workers in the bottom 40% of the income distribution while boosting incomes for the top 1%. These cuts are also likely to harm people and children of color, who are disproportionately more likely to rely on Medicaid. 

If lawmakers were serious about lifting families out of poverty and enabling them to fully participate in the labor force, they would be enacting policies to raise wages and expand access to good-paying jobs. By keeping wages low and making it more challenging to access benefits, lawmakers are seeking to deprive low-income households of the resources they need to thrive.

1. S. Rep. No. 884 (75th Cong., 1st Sess.), p. 4

2. Supplemental Nutrition Assistance Program (SNAP, formerly known as the food stamp program) is a crucial safety net program providing benefits so that low-income people in the United States can purchase food. SNAP has work requirements for most beneficiaries ages 16–59 who are able to work. In addition, there are more stringent work requirements for able-bodied adults without dependents (ABAWDs).

3. These individuals lived in households that qualified for SNAP benefits, housing subsidies, free or reduced-priced school meals, or cash assistance from the Temporary Assistance for Needy Families (TANF) program.

The Trump administration occasionally makes vague promises that it will maintain current federal resources flowing to public schools, but this is far from assuring. Today’s federal education aid is extremely well-targeted toward high-need districts, and even if the level of federal aid to states is maintained, it’s not clear that it would remain as well-targeted.  

Currently, a large share of ED funding goes to high-poverty districts through Title I funds and to special education programs through IDEA programs. These resources are crucial in offsetting the differences in local funding between low- and high-income districts, which rely heavily on property taxes. Over the last few years, COVID relief packages bolstered federal funding to public schools, with the Elementary and Secondary Schools Emergency Relief fund helping districts recover from learning losses that occurred during the pandemic. In 2022, districts received 14.0% of their total revenue, on average, from federal sources.   

To get a sense of how this federal money is distributed, we used data from the National Center for Education Statistics to calculate district-level estimates of federal revenue shares, the amount of Title I and IDEA funds each district receives, and the federal funding equivalent in the number of full-time teachers and staff. The results of this analysis can be seen in Figure A. Federal funding is crucial for schools across the country—the median amount a school district receives is $2.69 million. Federal funds make up less than 20% of a district’s budget in most cases (82%), but some districts rely on the federal government to contribute a much larger share of their funding—as much as 71.4% at the highest end.

Figure A also shows that because federal funds are meant to make up for state and local funding shortfalls, they are unevenly distributed across the country. Of the 25% of districts that receive the highest shares of revenue from the federal government, 54.6% are in the South. Removing this financial support would do disproportionate harm to children who live in a region that already severely underfunds public services. Additionally, states like Arizona and South Dakota would see massive cuts to their public schools, especially those that primarily serve Indigenous students. Nine of the 10 districts nationally with the highest revenue shares of federal funding are on or near reservations. These cuts would exacerbate racial inequity across the country.

Key to ED’s efficacy is that the revenue is redistributive: 51% goes to the third of schools with the highest neighborhood poverty while 18% goes to the third of districts with the lowest neighborhood poverty. Figure B shows a scatterplot of the share of federal revenue going to a district and the number of children with family income below the poverty line. The scatterplot is basically a straight line: As neighborhood poverty in a district increases, so too does the share of federal funding going to support that district. This basic need and special education funding serves children with high learning needs, ensures that there are enough support staff, and—coupled with child nutrition funding—helps students get proper nutrition in addition to receiving a quality education. Given the strong relationship between neighborhood poverty and federal funding, it’s hard to imagine a different program being more effective at reducing inequalities in education than the Department of Education’s spending priorities in K–12.

Given the unprecedented nature of these administrative actions, many organizations have responded with useful fact sheets and data tools showing how dismantling the Department of Education would affect their states (see here, here, here, and here). These data tools all tell a consistent story regarding the significance of federal aid, but they tend to differ in some details. For example, our district-level estimates include all federal funding that supports education, including grants beyond the Department of Education, such as grants from the United States Department of Agriculture (USDA) that support child nutrition. We break down these overall estimates into separate categories for Title I funding and IDEA funding, which directly come from ED, so that users can see the role each program plays in overall funding. This choice means that our total federal funding estimates are larger than other estimates that strictly use ED grants from the FY2023–FY2025 president’s budget. Further, our district-level estimates, which are from FY 2021–2022, exclude charter schools and very small districts (districts with fewer than 100 students), whereas some other fact sheets and data tools don’t make this exclusion.  

Finally, we use data from the National Center of Education Statistics, which are retrospective and rely on actual spending data reported to the Census Bureau. Other data sources on district school funding include the federal Department of Education’s budget service or state-level education departments. Often, these other sources will include more timely data but are based on grants that are allocated rather than already spent. While there are differences between what these various sources report about federal aid to districts for education, the differences tend to be quite small. 

Overall, the Trump administration’s policy choices could have devastating consequences on children’s well-being and educational success. Further, these choices are in line with a broader Republican attack on public education, which often begin with criticisms about curriculum decisions (complaints about excess focus on diversity, equity, and inclusion measures and left-wing “indoctrination” are all too common.) But behind these culture war complaints lies a quieter, yet devastating, attack on the basic level of resources available to public schools to educate children. The larger Republican goal—pursued at both federal and state levels—seems clear: starve funding to K–12 schools.

To illustrate the difference in benefits depending on household income, the range would extend between less than $0.35 per day for the poorest households to $860 per day for the top 0.1%. For the bottom 20% of U.S. households, extending these provisions would give them an average of less than $0.35 per day. For households in the second income fifth, the benefits would be $1.20 per day, and for the middle 20% of the income distribution, the benefits would be $1.80 per day. Yet for the richest 1% of households, the benefits would jump to $165 per day, while the top 0.1% would see benefits of $860 per day.

Besides being unfairly distributed, the cost of the overall tax cut is large enough to put huge stress on other parts of the economy, no matter how it’s paid for.1 The most damaging way to pay for this would be to enact large cuts in spending programs that provide benefits to economically vulnerable families. Last week, House Republicans approved a budget resolution calling exactly for these types of cuts, including $880 billion in cuts that will inevitably fall on Medicaid, the program that provides health insurance for low-income Americans who cannot otherwise afford it.2

Medicaid is, by far, the largest program in the federal government aimed predominantly at alleviating poverty.3 In 2024 it provided health insurance coverage for over 80 million people each month. The juxtaposition of prioritizing lower taxes for the richest families while proposing steep cuts to the nation’s largest program aimed at alleviating poverty could not be more clarifying for the economic debate in front of us.

The benefits of extending expiring provisions to the TCJA are easy to summarize. They will boost incomes trivially for the large majority of families but significantly for the richest households, leading to greater income inequality. The costs of Medicaid cuts are a bit harder to summarize because they are so broad and will cascade far into the future. The summary of what these cuts will do is clear. They will greatly increase hardship and misery for already struggling families, they will reduce opportunities in the future for kids who will grow up less healthy and poorer because their families lack access to Medicaid, and they will put enough strain on the nation’s overall economy that they will make a future recession far more likely.

In the rest of this report, we provide some data and texture on the channels through which Medicaid cuts will damage present and future prospects for economic security:

• Medicaid cuts will substantially reduce incomes for families in the bottom 40% (the bottom two-fifths) of the income distribution. For the bottom fifth, $880 billion in Medicaid cuts over the next decade would translate into Medicaid benefit reductions equal to 7.4% of their money income. For the second fifth, these cuts would equal 1.7% of their money income. (Money income is defined as income received from wages, interest, dividends, rents, Social Security, unemployment insurance, Supplemental Security Income, and pensions.) Medicaid cuts will easily swamp the meager benefits these families might get from TCJA extensions.
  • This is true for every state in the country. Medicaid cuts will squander most of the meager benefits from the TCJA extension even for families in the middle fifth of the income distribution.
• Medicaid cuts will lead to worse health and financial outcomes for young adults. Recent Medicaid expansions included in the Affordable Care Act (ACA) provided one of the only robust safety nets available to childless young adults in the U.S. These expansions led to better health and financial security—and notably reduced medical debt.
• Medicaid cuts will have terrible effects for health systems and health outcomes in rural parts of the United States.
  • Rural hospitals have significantly lower operating margins than others and rely disproportionately on payments from Medicaid to remain in business. The financial viability and closures of rural hospitals have been clearly worse in states that have yet to accept the expansions to Medicaid under the ACA.
    • For example, operating margins for rural hospitals are 2.0% in states that accepted the ACA Medicaid expansions but just 0.3% for others.
    • Three-quarters of rural hospital closures since 2010 have come in the minority of states that did not accept the ACA Medicaid expansions.
  • Rural towns and counties rely overwhelmingly on Medicaid to provide health insurance coverage—particularly for children. Medicaid covers over 50% of children in small towns and rural areas in six states: Arizona, Arkansas, Florida, Louisiana, New Mexico, and South Carolina.
• If Medicaid cuts lead to children being left out of its protections, the cuts will result in worse outcomes when those children grow up: lower educational attainment and lower earnings as adults.
  • Past Medicaid spending provided not just contemporaneous benefits to recipients but also proved to be an extremely good investment—leading to a future workforce that was healthier and had stronger labor force attachments.
  • Medicaid cuts that deprive children of access to health coverage could actually cost the federal budget money on net in the long term, as these children would grow up to earn less in wages, pay less in taxes, and be more likely to receive other public benefits.
    • Various forms of likely Medicaid cuts have been shown to forfeit between half and 266% of their deficit-reduction benefits once the spillover effects on children’s health and outcomes as adults are factored in. In short, many cuts will increase budget deficits in the coming decades.
• Finally, if the full $880 billion cut to Medicaid occurs and is put into effect in the next year, this will suck enormous amounts of purchasing power out of the economy. This, in turn, would leave us far more vulnerable to other potential recessionary shocks in the years ahead.
  • The Federal Reserve will be forced to cut interest rates simply to keep the unemployment rate from rising.
  • While lower interest rates might sound good to some, these cuts will constitute the Fed wasting nearly half of its current policy ammunition for fighting recessions simply to absorb the policy combination of lower taxes for the rich and lower incomes for the bottom half. This cannot be a good use of the Fed’s resources.

Medicaid and household incomes

The Congressional Budget Office (CBO) periodically releases valuable data highlighting the level and sources of household incomes in the United States (CBO 2024b). An extraordinary finding in this data is the importance of resources transferred to households in the form of Medicaid health insurance coverage. For example, on average between 2017 to 2021, CBO estimates that the value of Medicaid coverage for families in the bottom fifth of the income distribution (the poorest 20% of U.S. families) averaged 70% of their total money income (money income defined as income received from wages, interest, dividends, rents, Social Security, unemployment insurance, Supplemental Security Income, and pensions).

This obviously reflects both the low incomes of these families and the crushing expense of health care in the United States. There is a widespread belief (not universal, but widespread) that the United States should maintain a social contract ensuring that people should not be denied health care simply because they lack income. Medicaid (along with Medicare) is how this social contract is realized in the United States. The tangible, monetary value of maintaining this social contract to low-income American families is often easy to underestimate.

Medicaid is also crucially important for families in the second fifth of the income distribution (those with higher incomes than 20% of households but lower incomes than 60%). Medicaid constitutes roughly 12% of their money income. Even the middle fifth of families receive Medicaid benefits equal to almost 6% of their money incomes. This appearance of Medicaid benefits in households that appear as if they might make too high of an income to qualify reflects in part the volatility of incomes and the churn of households into and out of poverty and qualification for anti-poverty programs in the United States.4 Families that start a year with very low income can qualify for Medicaid, but then if they find high-quality employment for the rest of the year, their incomes can pull them out of the bottom fifth and the amount they receive from Medicaid trails off as their incomes rise to the point that they are no longer eligible for it. 5

A cut of $880 billion to Medicaid—currently on the table in the House Republican budget resolution—would constitute a cut of roughly 10.7% to the program’s projected spending going forward (CBO 2024a). Given the current contribution that Medicaid makes to household incomes in the bottom 40% of the income distribution, we can calculate how much this cut would reduce these incomes. Further, using estimates of the gains to household income stemming from extending the TCJA, we can compare how much the bottom 40% of the income distribution would lose from proposed Medicaid cuts as opposed to how much they’d gain from TCJA extensions.

As Figure 1 shows, the net loss for households in the bottom 40% of income distribution from this policy package is enormous. What is particularly striking is that the proposed Medicaid cuts that would do so much damage to the bottom 40% would only pay for roughly 20% of the total cost of the TCJA extension (Treasury-OTA 2025). More damage would have to be done elsewhere to finance these TCJA extensions.

Figure 1

Table 1 provides an estimate of how much the proposed Medicaid cuts would cost the bottom two-fifths of household incomes in every state (The methodology for Table 1 is provided in the appendix.). It also includes estimates for the middle fifth of U.S. households, who also suffer a smaller, but not trivial, cut to their household incomes as well.

Table 1

Medicaid cuts will lead to worse health and financial outcomes, particularly for young adults

These extraordinarily steep Medicaid cuts greatly increase the risk that individuals will experience health and financial disruptions that can have serious consequences. Medical bills are one of the largest causes of unpaid debt collections and bankruptcy (CFPB 2014; Himmelstein et al. 2019). For low-income Americans, who often lack the savings or access to the credit necessary to buffer against a financial emergency, these bills and the medical debt they leave behind can be particularly burdensome (Pew Charitable Trusts 2015). By providing access to health care at a reduced cost, Medicaid improves finances directly by making health care more affordable. Since 2010 when the ACA was enacted into law, personal bankruptcy has steadily fallen year over year, and by 2016, fillings were only 50% what they were in 2010 (St. John 2017). 

Careful research design methods have also shown that Medicaid expansions passed under the ACA directly led to increased financial security for low-income adults. Using survey data developed by the U.S. Department of Treasury and the FINRA Investor Education Foundation, researchers found that the share of low-income adults with unpaid medical bills declined by twice as much in states that decided to expand Medicaid relative to states that did not (Sojourner and Golberstein 2017). 

Moreover, this same study found that low-income adults’ satisfaction with their financial situation increased more in Medicaid expansion states. Other studies have also linked Medicaid expansion to key measures of financial well-being. Using credit report data, researchers found that households living in low-income areas in states that accepted the Medicaid expansion had a lower amount of unpaid balances in collections by $65–$88. Those who gained new Medicaid coverage had a much steeper reduction in unpaid balances (of $1,140) (Hu et al. 2018). A Federal Reserve study found that, for counties with high uninsurance rates, Medicaid expansion led to less debt being sent to collection agencies, compared with similar counties in non-expansion states (Dussault, Pinkovskiy, and Zafar 2016). In short, the Medicaid expansions put into law by the passage of the Affordable Care Act clearly boosted the economic security of lower-income adults, making their lives less stressful.

Medicaid can also reduce health care costs indirectly by lowering the barrier for access to care, which improves health and can prevent more expensive care being needed down the line. This is particularly important for young adults, a group that receives notably stingy benefits from the current U.S. system of income support and social insurance. Prior to the ACA, young adults were often caught in limbo in their ability to secure decent health insurance coverage, having aged out of their parents’ plans but not being able to land a job with health care benefits. 

For example, in the years prior to the ACA, nearly 30% of young adults were uninsured, and this uninsurance meant they often put off care: 76% of uninsured young adults reported cost-related access problems, such as not filling a prescription; skipping a medical test, treatment, or follow-up; having a medical problem but not seeing a doctor or going to a clinic; not seeing a specialist when needed; and delaying or not getting needed dental care (Davis 2010). Consequently, young adults too often didn’t seek care until a medical problem became severe, a delay that can lead to bigger costs down the line. In that same survey, 59% of uninsured young adults reported having a medical bill problem or outstanding debt. 

In 2010, the ACA allowed young people to remain on their parents’ plans until they were 26. This change reduced uninsurance by 10.6 percentage points among young adults with middle-income parents; and by 9.1 percentage points among young adults with high-income parents. However, it wasn’t until the ACA Medicaid expansions took effect in 2014 that that low-income adults’ rate of uninsurance declined by 8.9 percentage points, as well (McMorrow et al. 2015). This ability to access affordable care had a substantial impact on their health.

Medicaid is crucial for the health of rural hospitals and communities

For rural areas, Medicaid is a lifeline both for the community and the health care institutions supporting that community (including by providing jobs). Rural areas are characterized by unique health and health insurance challenges, including lower access to job-based coverage, greater prevalence of self-employed jobs (such as farming and contracting), lower incomes, and a greater share of people with a disability (CBPP 2013).

Before the ACA was passed, rural residents made up a disproportionate share of the uninsured. The coverage provisions of the ACA —including Medicaid expansion— disproportionately benefited rural residents. Between 2013 and 2015, the uninsured rate among nonelderly rural adults dropped by 7 percentage points in states accepting the ACA Medicaid expansions compared with 4 percentage points in non-expansion states (Cross-Call et al. 2017). 

Children and families across all states rely on Medicaid, but coverage is greatest in the South and the West. Figure 2, reproduced from the Georgetown University McCourt School of Public Policy, displays Medicaid and the Children’s Health Insurance Program (Medicaid for kids) coverage for children in rural and metro areas throughout the country for 2023 (Georgetown University CCF 2023). In six states—Arizona, Arkansas, Florida, Louisiana, New Mexico, and South Carolina— Medicaid/CHIP covers over half of all children in small towns and rural areas.

Figure 2

Across the country, Medicaid remains a dominant source of coverage for children in rural areas. On the East Coast in New York and Pennsylvania, Medicaid covers 43.5% and 37.3% percent of rural children, respectively. For Missouri and Nebraska in the Midwest, Medicaid/CHIP coverage for children in rural areas is 38.3% and 32.7%, respectively. On the West Coast in Alaska and California, Medicaid/CHIP coverage for children in rural areas is 39.4% and 49.1%, respectively. In the South, the contrast between Texas and Louisiana is stark, with Texas remaining a holdout from the ACA Medicaid expansions and Louisiana having accepted these expansions in 2014. In Texas, Medicaid/CHIP coverage for kids in rural areas was 33.1%, but in Louisiana, Medicaid/CHIP coverage was almost two times as high at 57.7%.

Rural hospitals also substantially benefited from Medicaid expansion. Rural hospitals serve areas with higher rates of uninsured patients and, as a result, have higher rates of uncompensated care, which increases financial stress on these hospitals. Following the expansion of Medicaid, in 2019, researchers found that operating margins (a summary measure of financial viability for hospitals) in rural hospitals were larger relative to those in states that didn’t expand Medicaid. Median operating margins in rural hospitals that expanded Medicaid were around 2.0% compared with 0.3% for states that did not expand Medicaid (Levinson, Godwin, and Hulver 2023). Given how low operating margins in rural hospitals can be, access to Medicaid can be the difference in rural hospitals being able to stabilize their balance sheets instead of operating at a loss and eventually having to close down.

The threat of rural communities losing their hospitals entirely is not idle. Between 2010 and 2021, 136 rural hospitals closed down throughout the United States. Of these, nearly three-quarters (74%) were in the minority of states that had not accepted the Medicaid expansion provisions of the ACA (AHA 2022).

Medicaid is a powerful investment in the future of the workforce, and cuts might even cost the federal government in the long run

Since the goal of Medicaid is to ensure access to health care and long-term care for low- and moderate-income Americans and people with disabilities, it is not surprising that the program has positive effects on participants’ health and financial well-being and provides critical funding to providers who serve vulnerable populations. But Medicaid is not just a transfer of current resources. It is also an investment in the future workforce with notably high rates of return. When kids have access to Medicaid, they have better educational and labor market outcomes, which translate directly into higher lifetime tax payments out of higher wages. This, in turn, significantly blunts the long-run net cost of Medicaid benefits for children. Additionally, kids with access to Medicaid will become healthier and higher-wage adults who are less likely to draw on health and disability programs as adults, providing another budget offset to Medicaid investments.

Goodman-Bacon (2021) analyzed differences between states in welfare-based eligibility for Medicaid after its enactment in 1965 to estimate long-run effects of childhood eligibility on health, employment, and receipt of government benefits. The study estimates that access to Medicaid in childhood reduced the number of older adults receiving disability benefits by 1.2 million between 2000 and 2014. In all, the study concludes that Medicaid “saved the government more than its original cost and saved more than 10 million quality adjusted life years.” (Goodman-Bacon 2021). If future Medicaid cuts symmetrically reverse these effects, then estimated cost savings will be significantly eroded in future years and decades as kids deprived of access to health coverage grow up more likely to suffer from lower educational attainment, higher rates of disability benefit receipt, weaker labor force attachment, and lower wages (and resulting tax payments). It is the definition of a penny-wise, pound-foolish approach to budgeting.

Similarly, Brown, Kowalski, and Lurie (2020) documented positive effects of childhood Medicaid participation on the health, education, and earnings of young adults. These lead to higher tax payments and lower receipt of the Earned Income Tax Credit (EITC), with taxpayers recouping 58 cents of each dollar spent on Medicaid participation in childhood, not counting potential cost savings in future years or from reductions in spending on health benefits or on means-tested benefits other than EITC (Brown, Kowalski, and Lurie 2020).

These “fiscal externalities” from changing Medicaid coverage are well known and validated by Congressional Budget Office (CBO) findings. In a recent paper, they found that common methods proposed to cut Medicaid spending would save the federal government far less money than is commonly thought (Ash et al. 2023). These methods include making Medicaid a “block grant” to states. (This money would be entirely under state control with capped funding.) For example, the CBO found that various permutations of block-grant proposals for Medicaid could see between 51% and 266% of total static “savings” reversed by the fiscal externalities identified above. That is, if cuts fall heavily on children and these cuts lead to them growing up and earning less money in the labor market and being more likely to draw on other benefit programs later in life, the short-run budget savings could easily be entirely reversed in the long run.

One political challenge to making these research findings salient is timescale. The savings from more-generous Medicaid coverage (or the dissavings from Medicaid cuts) take decades to occur. Incorporating these important research findings into near-term budget debates requires policymakers to prioritize being good stewards of the future rather than responding to short-term demands.

Medicaid cuts would make the U.S. more vulnerable to recession

A Medicaid cut of $880 billion would be macroeconomically significant. All else equal, it would represent a drag on economic growth of about 0.5% (which would, in turn, increase unemployment by about 0.3 percentage points—leaving about 550,000 people involuntarily jobless). This drag on growth would occur due to reduced economywide spending. As people skipped going to the doctor, this would reduce spending on medical care, and for those who continued going even with less generous Medicaid coverage, their out-of-pocket costs would rise and crowd out spending on other items.

The depressing effects of Medicaid cuts on economywide spending generally are very large relative to other policy interventions. Previous research has highlighted that changes to Medicaid’s benefits translate powerfully into large changes in household spending. The reason for this is obvious. Families that qualify for Medicaid do very little saving, essentially living paycheck to paycheck. When their income rises or falls, this translates instantly into higher or lower spending by this group. One particularly high-quality assessment of the effect of changing Medicaid spending on the macroeconomy found extraordinarily large “multipliers,” increments to economic growth and employment stemming from Medicaid change (Chodorow-Reich et al. 2012). These multipliers were as large as 2, meaning that each $1 cut from Medicaid translated into a $2 reduction in overall gross domestic product (GDP), stemming from the reduced household spending spurred by the Medicaid cut.

Further, the tax cuts that these Medicaid spending cuts would help finance would do little to counteract this drag. It is well known that tax cuts that disproportionately raise incomes at the top of the distribution do little to boost economywide spending (Bivens and Fieldhouse 2012). The reason for this weak boost is that spending by richer households responds less to changes in current income. These households save a good part of their income, so giving them more income means lots of this boost “leak” away from spending.

All this means that a policy package combining lower taxes mostly on high-income households with Medicaid spending cuts would have noticeable effects in reducing economywide spending. All else equal, this would show up as higher unemployment and slower growth. Over the next few years, it is true that the Federal Reserve would have the ability to counteract this drag on growth by pulling down the interest rates it controls (the federal funds rate). But it would have to pull down rates by about half of their current value, going from roughly 4.25% to closer to 2.5%. This would constitute a significant draining of the Fed’s capacity to counteract other recessionary shocks, should they appear. Essentially, the Fed would be forced to spend almost half of its anti-recessionary ammunition simply to accommodate a policy package of lower taxes for the richest households combined with steep spending cuts for the most vulnerable. This policy package is ugly enough on fairness grounds, but the fact that it also comes with a squandering of readiness for the next recession layers another injury on top of it.

Conclusion

Low taxes for the rich and for corporations is the highest legislative priority of the Trump administration and congressional Republicans. To get there, they are willing to cut federal programs that are utterly vital to the incomes and security of vulnerable families. These cuts will not just cause harm to individual families, they will cascade, leading to hospital closures in rural counties, higher medical debt, lower earnings from future workers who will suffer from poorer health decades from now, and could even put upward pressure on federal budget deficits in the long run. In the very near term, these cuts will make the United States economy far more vulnerable to any recessionary shock. Nothing about this policy package—tax cuts mostly for the rich and benefit cuts for the vulnerable—is good for the vast majority of families in this country.

Appendix

Appendix table methodology

CBO (2024b) provides data on the components of income by household income quintile. We specifically use the CBO series on “households ranked by income before taxes and transfers.” We then construct an estimate of money income that would match the income concepts used in the household income variables of the American Community Survey (ACS). This money income variable from the ACS includes income from the following sources: wages, interest payments, dividends, rental income, pensions, Social Security, Supplemental Security income, unemployment insurance, and other cash transfers (like receipt of Temporary Assistance to Needy Families). The CBO data include all these categories except other cash transfers, which it bundles together with other non-cash transfers. Given how small these other cash transfers are, however, we leave this CBO component out of our analysis and think it cannot affect our estimates much.

When we calculate household money income by fifth in the CBO data and compare it with ACS data, we find a very close match in the second and middle fifth. For the bottom fifth, the CBO money income measure is larger by almost 12%. This is almost certainly driven by the fact that the CBO undertakes many data corrections and adjustments to account for the well-known problem of underreporting of transfer income in household surveys like the ACS. Given that a higher reported income for the bottom fifth actually reduces the importance of Medicaid for this group, using the CBO bottom-fifth income for our anchor in Table 1 is actually conservative, so we continue with it.

Figure A1

We take the value of Medicaid divided by money income for households in the bottom two-fifths nationally from the CBO data. We then assume this national value varies across states in proportion to how their state’s Medicaid enrollment share (Medicaid enrollment divided by state population) compares with the national Medicaid enrollment share. This lets us assign a state-specific value of Medicaid relative to money income for the bottom two-fifths in every state. From there, we can assess the impact of a 10.7% cut to Medicaid by income fifth by state.

Notes

1. See Bivens (2025) for why the current macroeconomic situation means that such a large tax cut will inevitably put downward pressure on living standards for the vast majority of Americans, regardless of how it’s financed.

2. The budget resolution calls for the Energy and Commerce committee to cut spending by $880 billion over 10 years (but cuts to Medicare are ruled out). This committee has jurisdiction over Medicaid, and it is, by far, the single biggest program in their jurisdiction that they have been given permission to cut. It would be near mathematically impossible to spare Medicaid of any cuts given the overall size of the cut.

3. Social Security may well be responsible for keeping more people out of poverty, but Social Security is also a broad-based pension and insurance program. Its broad base provides extremely valuable benefits but also means it directs a smaller share of its resources specifically to alleviating poverty.

4. Other reasons for this might be simple misreporting or the fact that the CBO data are based on households, but Medicaid eligibility is hinged more on family income. One could have multiple families living in one household, with the total income of the household looking too high to qualify for Medicaid but the incomes of the individual families allowing them to qualify.

5. Ideally, they would smoothly qualify for very generous subsidies to purchase health insurance in the Affordable Care Act marketplace “exchanges.”

References

American Hospital Association (AHA). 2022. Rural Hospital Closures Threaten Access: Solutions to Preserve Care in Local Communities. American Hospital Association, September 2022.

Ash, Elizabeth, Wiliam Carrington, Rebecca Heller, and Grace Hwang. 2023. “Exploring the Effects of Medicaid During Childhood on the Economy and the Budget.” Working Paper Series Congressional Budget Office, Working Paper 2023-07, November 2023.

Bivens, Josh. 2025. There Will Be Pain: Continuing Low Tax Rates for the Rich and Corporations Will Hurt Working Families. Economic Policy Institute, February 2025.

Bivens, Josh, and Andrew Fieldhouse. 2012. A Fiscal Obstacle Course, Not a Cliff: Economic Impacts of Expiring Tax Cuts and Impending Spending Cuts, and Policy Recommendations. Economic Policy Institute, September 2012.

Brown, David W., Amanda E. Kowalski, and Ithai Z. Lurie. 2020. “Long-Term Impacts of Childhood Medicaid Expansions on Outcomes in Adulthood.” Review of Economic Studies 87, no. 2: 792–821. doi: 10.1093/restud/rdz039.

Center on Budget and Policy Priorities (CBPP). 2013. Rural America Will Benefit from Medicaid Expansion (fact sheet). June 7, 2013.

Chodorow-Reich, Gabriel, Laura Feiveson, Zachary Liscow, and William Gui Woolston. 2012. “Does State Fiscal Relief During Recessions Increase Employment? Evidence from the American Recovery and Reinvestment Act” American Economic Journal: Economic Policy 4, no. 3: 118–145. http://dx.doi.org/10.1257/pol.4.3.118.

Congressional Budget Office (CBO). 2024a. Budget and Economic Data. https://www.cbo.gov/data/budget-economic-data#3.

Congressional Budget Office (CBO). 2024b. The Distribution of Household Income in 2021. Publication no. 60341, September 11, 2024.

Consumer Financial Protection Bureau (CFPB). 2014. Consumer Credit Reports: A Study of Medical and Non-Medical Collections. December 2014.

Cross-Call, Jesse, Tara Straw, Arloc Sherman, and Matt Broaddus. 2017. House-Passed Bill Would Devastate Health Care in Rural America. Center on Budget and Policy Priorities, May 16, 2017.

Davis, Karen. 2010. A New Era in American Health Care: Realizing the Potential of Reform. Commonwealth Fund Publication no. 1419, June 2010.

Dussault, Nicole, Maxim L. Pinkovskiy, and Basit Zafar. 2016. “Is Health Insurance Good for Your Financial Health?” (blog post). Liberty Street Economics, Federal Reserve Bank of New York, June 6, 2016.

Georgetown University Center for Children and Families. 2023. “Medicaid Coverage in Metro and Small Town/Rural Counties, 2023” [interactive data], Georgetown University McCourt School of Public Policy, 2023.

Goodman-Bacon, Andrew. 2021. “The Long-Run Effects of Childhood Insurance Coverage: Medicaid Implementation, Adult Health, and Labor Market Outcomes.” American Economic Review 111, no. 8: 2550–2593. doi: 10.1257/aer.20171671.

Himmelstein, David U., Robert M. Lawless, Deborah Thorne, Pamela Foohey, and Steffie Woolhandler. 2019. “Medical Bankruptcy: Still Common Despite the Affordable Care Act.” American Journal of Public Health 109, no. 3: 431–433. doi: 10.2105/AJPH.2018.304901.

Hu, Luojia, Robert Kaestner, Bhashkar Mazumder, Sarah Miller, and Ashley Wong. 2018. “The Effect of the Affordable Care Act Medicaid Expansions on Financial Wellbeing.” Journal of Public Economics 163, 99–112. https://doi.org/10.1016/j.jpubeco.2018.04.009.

Levinson, Zachary, Jamie Godwin, and Scott Hulver. 2023. Rural Hospitals Face Renewed Financial Challenges, Especially in States That Have Not Expanded Medicaid. KFF, February 23, 2023.

McMorrow, Stacey, Genevieve M. Kenney, Sharon K. Long, and Nathaniel Anderson. 2015. “Uninsurance Among Young Adults Continues to Decline, Particularly in Medicaid Expansion States.” Health Affairs 34, no. 4: 616–620.
https://doi.org/10.1377/hlthaff.2015.0044.

Pew Charitable Trusts. 2015. The Role of Emergency Savings in Family Financial Security: What Resources Do Families Have for Financial Emergencies? November 2015.

Sojourner, Aaron, and Ezra Golberstein. 2017. “Medicaid Expansion Reduced Unpaid Medical Debt and Increased Financial Satisfaction.” Health Affairs Blog, July 24, 2017.

St. John, Allen. 2017. “How the Affordable Care Act Drove Down Personal Bankruptcy: Expanded Health Insurance Helped Cut the Number of Filings by Half.” Consumer Reports, May 2, 2017.

U.S. Department of Treasury, Office of Tax Analysis (Treasury-OTA). The Cost and Distribution of Extending Expiring Provisions of the Tax Cuts and Jobs Act of 2017. Office of Tax Analysis, January 10, 2025.

Fortunately, there are proven strategies for strengthening standards that protect children, such as youth work permits—which outline the potential hours and work duties for a minor worker. In particular, youth work permits ensure that jobs children start as young as age 14 or 15 come with safe conditions and hours that don’t interfere with their education and development. In this post, we share highlights from new research that sheds light on the effectiveness of youth work permits and suggest how states can strengthen permit programs as legislative sessions begin this month.

States have led the way on protecting youth workers, but youth work permits are under threat

States have historically played an important role in setting child labor standards that exceed minimum federal standards set by the 1938 Fair Labor Standards Act (FLSA). Though the FLSA governs work hours for minors under age 16 and outlines prohibitively hazardous occupations for minors under age 18, many other safeguards—like rest breaks or protections from overnight shifts for 16-year-olds—are absent from federal law and left up to states.

Youth work permits are similarly not required under federal law. The majority of states and the District of Columbia require them in some form (see Figure 1), but they have recently come under attack by right-wing think tanks and industry groups seeking to provide employers unfettered access to cheap labor. Since 2023, eight states have proposed eliminating youth work permits, and three have signed these measures into law.

Figure 1

Proponents of eliminating youth work permits argue that work permits are not effective at deterring violations, are overly burdensome on employers, and take away parents’ right to decide whether to let their child work. However, none of these claims are accurate, as we explain below.

New data show work permits help prevent child labor violations

Recent research using comprehensive data from the Department of Labor’s Wage and Hour Division provides the first quantitative evidence that work permits help prevent child labor violations. Between 2008 and 2020, states that mandated employment certificates saw 15.5% fewer child labor violation cases and 35.2% fewer minors involved in these violations compared with states that had no such requirements (see Figure 2).

Figure 2

The impact is particularly consistent in high-risk industries. In the accommodation and food services sector—which accounts for over half of all child labor violations—states with work permit requirements saw 18.5% fewer violation cases and 34.6% fewer minors involved in violations. Further, states with work permit requirements saw nearly 30% lower civil penalties per minor involved in the violations, suggesting these requirements also help prevent more serious forms of child labor exploitation.

The research also revealed that work permits are particularly important during periods of low unemployment—like we are experiencing today—as employers turn to children to fill job vacancies. When unemployment rates decrease by one percentage point, child labor violations increase by 5.7%.

States with lower per capita income saw more violations, highlighting the need for protective measures in economically challenged areas. The data also showed a significant correlation between the arrival of unaccompanied migrant children and increased child labor violations, emphasizing the importance of maintaining strong protective measures for vulnerable youth.

The research leaves no room for doubt: Work permits are a proven policy tool that help prevent child labor violations. Rolling back these requirements would remove a crucial layer of protection at a time when we’re seeing concerning increases in child labor violations nationwide.

The work permit approval and documentation process plays an essential role

Youth work permits are often simple, one-page forms that are quickly processed by employers and serve important purposes—ensuring a child’s work is safe and age-appropriate, informing parents of their child’s rights and affirming their consent, and aiding in investigations of potential violations. In states like Wisconsin, the work permit process not only serves a documentation function but also generates the revenue needed to investigate potential violations.

To the extent that the processing of youth work permits takes time and effort, the process is deliberate, and when permits are denied, it’s for good reason. In 2023, the Maine Labor Department reported denying about 200 out of 4,700 youth work permit applications because the proposed work duties involved hazardous work that is prohibited for minors.

If youth employment is intended to benefit young workers and not simply their employers, then a system that allows hiring minors with maximum speed and convenience for the benefit of employers seeking cheap labor should not be the goal.  

Work permits ensure informed consent from parents and guardians

The decision about whether a child can or should seek employment rests with families. Work permits formalize parental consent, requiring employers to give parents information about the child’s job in writing and ensuring that a child’s potential work activities are safe and age-appropriate. Eliminating work permits takes away a parent’s right to make an informed decision about whether a job is appropriate for their child.

At the same time, most parents are not experts on workplace health and safety or labor and employment law, and they should not be held responsible for identifying occupations that may be particularly hazardous or illegal for minors. This is why permit systems that engage all stakeholders—parents, schools, employers, and the government—are the best way to ensure compliance with the law and the well-being of children.

State legislators should strengthen—not weaken or eliminate—their youth work permit systems

As state legislators prepare for legislative sessions to begin, they should oppose misguided attempts to eliminate youth work permits and look to strengthen them by making sure the process is clear, accessible, and effective at keeping minors safe at work. Lawmakers can expand their use of the work permit system as an opportunity to educate young workers and their families about their rights and the work duties they cannot by law be asked to perform. State labor agencies can also require employers to receive training on federal and state child labor laws.

In 2024, Illinois strengthened its work permit process in a comprehensive bill that bolstered child labor standards and enforcement, and a similar bill was recently approved in the Michigan Senate and awaits consideration in the House. These bills should serve as a guide to other states on how to modernize their child labor laws and support the rights of young people to access job opportunities that are safe and age-appropriate.

Donald Trump’s Project 2025 proposes allowing states to opt out of federal child labor laws and seeks to make it easier for employers to hire children for hazardous jobs. At a moment when federal standards are likely to come under attack, the responsibility of state lawmakers to raise their own standards is especially urgent.