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.

It is often underrecognized how much population aging is currently reducing the growth rate of the U.S. labor force and will continue to pull it down in coming decades. The share of the population that is over the age of 65 (when labor force participation tends to take a steep fall on average) is rising rapidly. This share was 12.4% in 2007, 17.9% in 2024, and will hit 21.2% by 2035 (CBO 2025b). A recent EPI report (Gould et al. 2025) assessed trends in U.S. labor force participation and reviewed the research literature about their drivers and the potential effects of policy changes on these trends. One upshot of this research literature is that even the most ambitious policies to boost the labor force participation rate of the current U.S. workforce would not materially change these trends.

Any decline in labor force growth necessarily leads to a decline in the rate of growth of gross domestic product (GDP). GDP is the product of the number of hours worked in an economy multiplied by productivity (the average amount of output generated in an hour of work). If the number of work hours falls because the labor force shrinks, this essentially translates one-for-one into slower aggregate growth. Policymakers who do not want to see the pace of GDP growth shrink relative to the past history of U.S. growth really only have one option: allowing larger flows of immigration. Absent this, other policies to boost the U.S. labor force—while they might be wise along many margins—will not restore overall GDP growth to anywhere near its historic pace. In the rest of this policy brief, we lay out some of the larger trends in U.S. labor force growth and the implications of population aging for the future path of the labor force and economic growth.

U.S. labor force growth has slowed a lot in recent decades, and U.S.-Born labor force growth has slowed even more

Figure A shows the average annual growth rate of the overall labor force for a number of historical periods. We pick endpoints for these periods that correspond with business cycle peaks to make sure that sharp cyclical differences are not driving these trends. For two recent periods (2007–2019 and 2019–2024), we also show the average annual growth of just the U.S.-born labor force.

Between 1948 and 1979, labor force growth averaged 1.8% annually. From 1979 to 2007, this pace slowed, but only slightly, averaging 1.4% annually. However, in the two business cycles since 2007, labor force growth averaged just 0.5%–0.6% annual growth. For the two most recent business cycles, we have data on growth in the U.S.-born labor force, and this growth is just 0.3% on average.

Figure A

The fast growth of the labor force between 1948 and 2007 and the slowdown since then can be explained by three big demographic changes: the Baby Boom that saw high fertility rates from the late 1940s to the mid-1960s and then a sharply lower fertility rate since, the steady influx of women into the labor force from 1948 until roughly 2000, and population aging that has seen the share of the over-65 population rise rapidly since 2007. The importance of population aging in driving the much slower labor force growth since 2007 can be seen in many exhibits presented in our previous report (Gould et al. 2025), which highlighted the labor force participation rate of prime-age workers—those between the ages of 25–54. These prime-age participation rates stood at near all-time highs in 2024, meaning that the decline in the labor force was not driven by falling age-adjusted participation rates, but was instead just driven by aging.

Population aging of U.S.-Born workers will accelerate in the next decade

Figure A highlighted that growth in the U.S.-born labor force was even slower than overall labor force growth after 2007. This makes sense given that immigrants tend to be younger than the U.S.-born population and that steady flows of net immigration buoy the U.S. labor force. The drag on overall labor force growth stemming from sharp declines in the U.S.-born labor force over the next decade will likely be quite steep.

The Congressional Budget Office (CBO 2025a) forecasts growth in the overall labor force and GDP for the U.S. economy over the next decade. They are currently projecting annual labor force growth of 0.5% on average between 2025 and 2035. Yet in demographic projections, the CBO (2025b) forecasts that immigration will account for essentially 100% of total U.S. population growth over this time span, and well over 100% of population growth after 2031. Given that 75%–80% of immigration flows are people between the ages of 20 to 64, this means that the U.S.-born population of those between the ages of 20 and 64—the vast bulk of the potential labor force—is forecast to shrink in every year for the next decade.

Figure B highlights this, showing estimates of the population between the ages of 20 and 64 for the years between 2025 and 2035.1 We show the baseline growth of this population, but then also estimate what growth would be if net immigration were halved or were driven by zero (see the data appendix for explanations of how these were calculated). The line showing zero net immigration essentially is the path of labor force growth of just the U.S.-born population.

Figure B

Reduction of net immigration flows would lead to much slower labor force and GDP growth

If we assume that any changes in population levels do not change labor force participation rates, we can make a rough inference about how much any change in immigration levels would affect trends in labor force and GDP growth in the coming decade. (Some more details on this calculation are in the data appendix.)

Figure C shows current forecasts for growth in real (inflation-adjusted) GDP from the CBO and from the Trump administration’s Office of Management and Budget (OMB). The OMB is forecasting far faster growth than the CBO over the next decade. This is true even as the CBO is still projecting immigration flows over the next decade that will be high enough to account for over 100% of U.S. population growth post-2030.

Figure C

Because GDP is simply the product of hours worked and productivity, the Trump administration would have to be forecasting either significantly faster growth in hours worked (proxied by the size of the labor force) or significantly faster productivity growth. But the potential growth of hours worked by U.S.-born workers is essentially driven entirely by demographic trends. Again, Gould et al. (2025) highlight that there is very little scope for even the most ambitious policy efforts to boost labor force participation rates of the current U.S. workforce to raise these by more than a percentage point or two. And even these ambitious and most effective policy changes largely involve substantial investments in today’s children to make them more likely to search for work as adults. This means that the payoff period is well over a decade.

Given this limited scope for policy to boost labor force participation rates, the only other margin along which the labor force could grow is immigration. But the Trump administration is clearly looking to shrink, not expand, net immigration flows. Given this stated policy preference, we also calculate what halving net immigration flows or reducing them to zero would do to CBO’s growth forecasts (for details on how we estimated these, see the data appendix). Very roughly, a halving of net immigration would reduce average annual GDP growth by 0.2 percentage points annually in the coming decade, while reducing net immigration to zero would reduce annual growth by 0.4 percentage points annually.

All of the discussion above implies that the Trump administration forecasts could only be met by faster productivity growth. Figure D shows the implied productivity growth assumptions adopted by the Trump administration versus the CBO. It then shows the implied productivity growth rates for the Trump administration forecast to hold in scenarios in which net immigration flows were halved or driven to zero. It is worth noting that the stated position of the Trump administration to increase deportations to 1 million per year would be (all else equal) roughly consistent with a halving of net immigration flows if these flows returned to pre-2022 levels.2 Finally, Figure D shows the historic average and maximum 10-year productivity growth rates from each full business cycle since 1969.

Figure D

Even the unadjusted forecasts of CBO and the Trump administration imply large differences in productivity assumptions—with the administration assuming productivity growth that is a full percentage point faster (or roughly double the pace) of CBO’s forecasts. For the Trump administration GDP forecasts to hold even in the face of reductions in net immigration flows, the assumptions regarding the pace of productivity growth would have to further increase. In a scenario of zero net immigration, for example, productivity growth would have to reach 2.9% annually to meet the administration’s GDP forecasts. For context, no full business cycle since 1969 has seen productivity growth even close to this fast. The previous maximum was the 2.4% productivity growth that characterized the 2000–2007 business cycle. On average since 1969, productivity growth over full business cycles has averaged just 1.7%. In short, meeting the OMB growth forecasts will be hard enough given current trends in net immigration. If there is any reduction in these trends, productivity growth would have to accelerate to levels not seen in decades.

Conclusion

The pace of overall GDP growth rises and falls essentially one-for-one with the pace of labor force growth. For the next decade, the labor force of the U.S.-born population will likely fall each year. To be clear, this does not necessarily imply great economic hardship. It is the level of GDP per capita that determines a country’s living standards, not its level of overall GDP. (This fact is why, for example, Denmark is considered a very rich country, while Bangladesh is not, despite the latter having an overall GDP that is more than three times as large).

But there are reasons besides its mechanical connection with overall GDP growth for a country to want the labor force to grow steadily. One reason is that a rising ratio of nonworkers to workers can make some social insurance systems (like those that provide retirement income or health care to older workers) more challenging to maintain. Given the value of these systems to the nation’s welfare, anything that makes them easier to sustain would be welcome.

Finally, any policymaker wanting to make large claims about the pace of overall GDP growth that will occur under their watch is obligated to make them consistent with basic facts about labor force growth, potential productivity growth, and the potential effect of policy on each of these. The degree to which labor force growth over the next decade in the U.S. will be quite slow relative to the historic past, and the pretty low possibility that even ambitious policy changes outside of immigration policy can change this is important information in this context.

Data appendix

Figure B

CBO (2025b) provides estimates for growth in the 20–64 population and net immigration overall. The background data included in that report also provide net immigration forecasts each year by age (along with sex and immigration status). Given this, we construct estimates of how much growth in the overall 20–64 population will be driven by net immigration. We then take forecasts of net immigration flows and cut them in half or force them to zero to assess the effect of this in growth of the 20–64-year-old population.

Figure C

GDP growth forecasts in the top two bars are obtained directly from CBO (2025a) and OMB (2025). To obtain the estimate in the bar titled “CBO with net immigration halved,” we make a calculation of how much halving projected net immigration flows would affect labor force growth in coming years. The calculated percentage change in the labor force would, in turn, then change GDP growth one-for-one. We build off the decline in the 20-64 population we estimated above. Because more than 90% of the labor force in any year is accounted for by people between the ages of 20 and 64, we multiply the change in the 20–64-year-old population by 90% to get a sense of how much changes in this population translate into changes in the overall labor force. This calculation implicitly assumes that changes in population do not have any effect on labor force participation rates. For example, if a population changes by 100, and the labor force participation rate of that population is (say) 80%, then the labor force will change by 80.

For the last bar in the figure, we do the same exercise, but this time assuming that net immigration is zero, not just halved.

Figure D

The bar titled “implied OMB forecast given GDP projections” assumes that CBO and OMB use the same forecasts for labor force growth. Given this, the difference in their GDP forecasts must equal the difference in their productivity forecasts. If the OMB ever clarifies just how they obtained their GDP forecasts, we can modify these calculations accordingly. Given the stated intent of the Trump administration to reduce net immigration flows and given the findings in Gould et al. (2025), it seems hard to see how the OMB could justify faster labor force growth forecasts.

For the bar in Figure D titled “necessary productivity growth to hit OMB GDP projection if net immigration was halved,” we use our previous estimate of how much a halving of projected net immigration flows would affect labor force growth and measure the difference between the OMB GDP projection and the CBO GDP forecast that would hold if labor force growth were reduced by a halving of net immigration inflows. For the next bar, we do the same exercise but use the estimate above for how much labor force and GDP growth would be held back by net immigration falling to zero.

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. We use this age range because it is the one provided by the CBO 2025b that is most relevant to potential growth in the labor force in the coming decade.

2. Zipperer (2025) notes that 1 million deportations would be an increase of roughly 670,000 over previous baseline levels. CBO 2025b forecasts that net immigration flows will average 1.2 million between 2025 and 2035. Importantly, this estimate was made before the large increase in resources for immigration enforcement made possible by the passage of the Republican-led budget bill that Trump signed into law in July 2025.

References

Bureau of Economic Analysis (BEA). 2025. “National Income and Product Accounts Table 1.1.6.” Accessed September 2025.

Bureau of Labor Statistics (BLS). 2025a. “Online Labor Force Statistics Database, Current Population Survey.” Accessed September 2025.

Bureau of Labor Statistics (BLS). 2025b. “Total Economy Hours and Employment Spreadsheet” [Excel file]. Accessed September 2025.

Congressional Budget Office (CBO). 2025a. The Budget and Economic Outlook: 2025 to 2035. January 17, 2025.

Congressional Budget Office (CBO). 2025b. The Demographic Outlook: 2025 to 2055. January 13, 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.

Office of Management and Budget (OMB). 2025. FY 2026 Mid-Session Review of the President’s Budget.

Zipperer, Ben. 2025. Trump’s Deportation Agenda Will Destroy Millions of Jobs: Both Immigrant and U.S.-Born Workers Would Suffer Job Losses, Particularly in Construction and Child Care. Economic Policy Institute, July 2025.

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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Who are low-income families of color?

Families in which the household head identifies as

• Black
• Hispanic
• American Indian or Alaska Native (AIAN)
• Asian American or Pacific Islander (AAPI)

With at least one child under the age of 18 living at home

With a total family income below 200% of the federal poverty line (below $64,300 in 2025 for a family of two adults and two children)

Introduction

Low-income families of color live in a permanent cycle of economic insecurity and uncertainty. These families make up a disproportionate share of the nearly 10 million families with children (9.7 million) who are either poor or vulnerable to poverty. As a result of their economic precarity, these families are among the first to experience the painful consequences of a recession. This was evident during the last two business cycle downturns: the Great Recession and the pandemic recession. We find that these two economic contractions dealt a mighty blow to the employment security of these families, triggering a rise in poverty and housing insecurity.

Given the weak policy response to the Great Recession, it took economically vulnerable families of color nearly a decade to recover in nearly all the economic domains we examine, including employment, poverty status, and housing insecurity. While the bold response to the pandemic recession led to a relatively faster rebound in employment, economically vulnerable families of color remain disproportionately burdened by poverty and housing insecurity.

Instead of easing the pain of economically vulnerable families, the Trump-Vance administration and congressional Republicans have been on the attack in the first half of 2025. They have gone after the agencies, laws, and programs that help protect these families from joblessness, discrimination, poverty, hunger, and premature death. In just its first 100 days, the administration deliberately cut the wages of workers, rolled back protections against bias in employment, and hacked away at staffing at agencies that support the well-being of low-income families (like the Department of Education and the Department of Health and Human Services). As if this weren’t enough, the administration and congressional Republicans prioritized dealing a historic blow to Medicaid and Supplemental Nutrition Assistance Program (SNAP). They cut spending on programs that provide desperately needed health care and nutritional support to families by more than $1 trillion (CBO 2025b).

The chaos and uncertainty ushered in by the economic mismanagement of the Trump-Vance administration even led to the first quarterly contraction in economic growth since 2022. With the prospects of another recession rising, the administration has done everything in its power to leave low-income families even more vulnerable to the pain ahead. As we illustrate in this report, economic downturns hit these families the hardest, and while we’ve learned a great deal since 2007 about how to protect them, the administration has chosen not to build upon those lessons. Instead of protecting the strong labor market they inherited, empowering workers to bargain for better pay and working conditions, and strengthening basic needs programs, the Trump-Vance administration is fighting for an economic agenda centered on austerity for the economically vulnerable and subsidies for the rich.

In just a short period of time, the Trump-Vance administration has left low-income families more economically insecure and vulnerable to pain as recession risks continue to rise

Since taking office, the Trump-Vance administration has worked to dismantle the basic protections that help shelter low-income families from even deeper economic insecurity and hardship. This attack on families has taken the form of executive actions undermining civil and workers’ rights. While some of President Trump’s executive orders have been challenged in court, their introduction has altered the policy discourse and the lived experience of low-income families of color throughout the U.S. with an explicitly racist and xenophobic agenda. Beyond executive actions, the Trump-Vance administration and congressional Republicans also passed one of the most sweeping cuts to the U.S. social safety net in recent history, gutting basic needs programs and making Medicaid and SNAP benefits much more difficult for families in need to access (Shierholz 2025). All of this was done to help offset the cost of tax cuts that disproportionately benefit rich households and corporations (The Budget Lab 2025).

Few policy issues have received as much priority in the Trump-Vance administration as their attack on economic justice and initiatives promoting diversity, equity, and inclusion (DEI). In just his first day in office, President Trump rolled back numerous executive actions expressing the federal government’s commitment to racial justice for Black, Hispanic, Native American, and Asian American, Native Hawaiian, and Pacific Islanders (EPI 2025d). President Trump later also rescinded executive actions that identified systemic barriers impeding Black Americans’ opportunity to fully participate in American society on a level playing field (EPI 2025e). Equity in the classroom is also under attack. This was evident when President Trump rescinded an executive order stating that all students should be guaranteed an educational environment free from discrimination, including discrimination in the form of sexual harassment, sexual violence, and on the basis of sexual orientation or gender identity (EPI 2025c). These efforts form part of more than a dozen executive actions signed by President Trump in his first 100 days to roll back years of progress on racial and economic justice (McNicholas et al. 2025).

The Trump-Vance administration is also working to roll back anti-discrimination protections by weakening the Equal Employment Opportunity Commission (EEOC) (Maye and Wilson 2025). Just days into his second term, President Trump dismissed two EEOC commissioners and the agency’s general counsel, years before the expiration of their appointment (Olson and Savage 2025; EPI 2025b). As a result of these dismissals, the commission lost the quorum needed to perform key functions. Trump has also redirected the EEOC’s priorities to focus more on investigating so-called DEI-motivated race and sex discrimination and anti-American national origin bias and discrimination (EEOC 2025; DOJ 2025). Because wages are the primary source of income for low-income families, weaker enforcement of anti-discrimination laws leaves families of color more vulnerable to employment and pay discrimination in the labor market.

The EEOC is not the only federal body that the Trump-Vance administration has weakened to the detriment of low-income families. In March 2025, President Trump signed an executive order that would effectively eliminate the U.S. Department of Education (ED). The U.S. Supreme Court later lifted a lower court decision that had blocked the administration from firing more than 1,300 employees at ED (Sherman 2025). While the merits of the case before the Supreme Court have yet to be decided, the gutting of ED will disproportionately harm children from low-income families of color that benefit from federal funding for under-resourced schools and programs aimed at closing learning and achievement gaps (Dianis 2025; EPI 2025a; Santhanam 2025).

More broadly, ED serves an essential role in helping enforce Title VI of the Civil Rights Act, which prohibits discrimination based on race, color, or national origin in programs or activities that receive federal financial assistance (ED n.d.). Even the U.S. public health infrastructure is now under attack, as the Trump administration is committed to carrying out layoffs at federal health agencies focused on reducing premature and preventable deaths associated with pervasive racial health disparities (Moore 2025).

President Trump’s attacks on federal agencies that are vital to the provision of public goods and services for families are part of a larger war his administration has waged on workers. In his first 100 days, Trump replaced the leadership of the National Labor Relations Board (NLRB)—the federal agency tasked with protecting the most fundamental U.S. labor rights—with members more likely to carry out his agenda to erode workers’ union and collective bargaining rights (McNicholas et al. 2025). This will hurt the ability of workers to form and join unions at work. Unions are vital to working families, as union workers enjoy better wages and working conditions than their nonunion peers (Banerjee et al. 2021).

Beyond executive actions, the main legislative priority of the Trump-Vance administration imposed more than $1 trillion in cuts to basic needs programs in exchange for continuing a tax regime that overwhelmingly favors rich households and corporations (CBO 2025b; Shierholz 2025). Extending the 2017 tax cuts that President Trump enacted in his first term will not just favor the rich disproportionately. On its own, this extension can even suppress economic growth over the long run and leave policymakers with significantly less room to respond to another recession (Bivens 2025b). To help offset the cost of these large tax cuts to the rich, the Republican-led budget reconciliation bill that Trump signed into law adds more stringent work requirements to Medicaid and SNAP on top of historic cuts.

This combination will leave more than 22 million families at risk of losing some or all of their SNAP benefits and strip away health coverage for more than 11 million people (CBO 2025a; Wheaton et al. 2025). These cruel and misguided efforts will disproportionately hurt low-income families of color and children who are more likely than their peers to rely on Medicaid and Children’s Health Insurance Program (CHIP) for health insurance, and SNAP and other nutritional assistance programs to avoid going hungry in the face of growing food insecurity (Cid-Martinez, Moore, and Maye 2025; Cid-Martinez 2025).

In its totality, the policy positions President Trump has advanced in his first 100 days via executive orders and legislative priorities will leave low-income families of color and children much more vulnerable to hardship. In the face of a recession, which is no longer a hypothetical scenario, the consequences would be devastating. The Bureau of Economic Analysis (BEA) reported the first quarterly contraction of economic growth since 2022, and while growth climbed again in the second quarter, the U.S. economy is now growing significantly slower in the first half of 2025 than in the previous year (BEA 2025). And the chaotic economic climate that the current administration has generated with its trade, immigration, and macroeconomic policy management has increased the prospects of a recession (Bivens 2025a).

The fear of an approaching recession increased with the downward revision of employment gains that defined the weak jobs report published in August 2025 (EPI Staff 2025). What we see in the first half of 2025 is an economy being held back by anemic growth and a deteriorating labor market.

The upheaval that this administration has produced leaves low-income families of color exposed to future hardship. Without a bold policy response to recessions and the support of a strong welfare state, these families are hit hardest by economic downturns and sluggish economic recoveries (Bivens et al. 2025). This report sheds light on this reality by examining how the last two recessions impacted the well-being of low-income families, as captured by their employment situation, poverty status, and housing insecurity.

Low-income families of color with children and the last two recessions

What do we mean by low-income families of color with children?

The sample of families included in this analysis are those in which the household head has at least one child of their own, under age 18, living at home. Within these households, there may also be other members who have children under 18. Families of color are broadly defined as those whose household head identifies as Black, Hispanic, American Indian or Alaska Native (AIAN), or Asian American or Pacific Islander (AAPI).[1]

We further restrict this sample to a subset of economically vulnerable, or low-income, families, defined as having total family income below 200% of the federal poverty threshold.[2] To place the poverty threshold in context, the federal poverty line (FPL) for a single individual in the 48 contiguous states (excluding Alaska and Hawaii), and Washington, D.C., is $15,560 in 2025. While the FPL increases by $5,500 for each additional family member, a year-round worker earning the federal minimum wage ($7.25 an hour) can’t afford to keep their family out of poverty in 2025 (Hickey and Cid-Martinez 2025). For the remainder of this report, we will use families (of color) to refer to families (of color) with children, and the terms “economically vulnerable” and “low income” will be used interchangeably.

Drawing a demographic portrait of economically vulnerable families

While our main economic analysis is focused exclusively on Black and Hispanic families due to data limitations associated with the sample size of other groups, this section provides a demographic picture of low-income families of color more broadly. Table 1 shows low-income families by race and ethnicity, using data from the 2023 American Community Survey (ACS). As depicted in Table 1, families of color are generally overrepresented among the 9.7 million families with children that are economically vulnerable. While Black, Hispanic, AIAN, and AAPI families collectively account for 44.4% of all families with children, they represent 61.1% of economically vulnerable families with children. Although white families make up a larger share of low-income families than any other single racial or ethnic group, they are underrepresented among low-income families (38.9%) relative to their share of all families (55.6%).

More than 3 in 10 (32.6%) low-income families are Hispanic and more than 1 in 5 (21.5%) are Black. Together, Black and Hispanic families represent more than half (54.1%) of all low-income families with children, but just over one-third (35.1%) of all families with children. While less than 1.5% of all families are AIAN, they too are slightly overrepresented (1.8%) among the economically vulnerable. AAPI families account for 7.9% of all families and 5.2% of economically vulnerable families; however, the aggregate socioeconomic status of AAPI families hides important differences that become evident when we separate groups by country of origin (Cid-Martinez and Marvin 2023).

Immigrant families also make up a disproportionate share of low-income families and are especially prevalent among low-income Hispanic and AAPI families. Foreign-born families made up 23.6% of all families in 2023, but a higher share (30.5%) of low-income families were immigrant families. Slightly more than 8 in 10 (81.1%) economically vulnerable AAPI families are foreign-born, as are more than 6 in 10 (61.9%) comparable Hispanic families.

Beyond economic insecurity, these families face ongoing threats under Trump’s draconian mass deportation agenda, which the administration and congressional Republicans bolstered with new financing in the budget reconciliation bill that Trump signed into law (Costa 2025; NIJC 2025). These attacks on immigrant families and the immigrant workforce will also have ripple effects on the labor market, costing the U.S. economy nearly 6 million jobs, particularly in construction and child care (Zipperer 2025). All of this will also put upward pressure on food and housing prices (McNicholas et al. 2025).

In terms of family structure, low-income families are generally more likely to be headed by women or a non-married household head.[3] However, one finds noticeable variations in these patterns across racial and ethnic groups. For example, Black and AIAN families are most likely to be headed by women, 78.7% and 69.5% respectively, compared with less than half (43.3%) of low-income AAPI families. There are also differences in marital status. Low-income AAPI families are significantly more likely to be led by a married couple (76.2%), compared with about half of white (50.5%) and Hispanic (51.6%) families. More than one-third of low-income AIAN families and one-quarter of low-income Black families are led by married couples. Apart from Black families, less than 1% of low-income families report having a partner or spouse of the same sex in 2023. Because most low-income Black families are headed by women, attacks on women’s reproductive rights, along with efforts to undermine nondiscrimination enforcement for racial and ethnic minorities, women, and LGBTQ+ individuals, impose additional disadvantages for these families.

Economically vulnerable families are also more likely to have more than one child (under age 18): 67% of low-income families have two or more children, compared with 58.7% of all families. However, among low-income families, there is little variation in the number of children across racial and ethnic groups. For example, about two-thirds of all low-income families has two or more children, and only 12.6% have four or more children.

The share of low-income families with either a disabled child or parent of a child shows considerable variation across race and ethnicity. AIAN families stand out as having the highest prevalence of disability. About 1 in 3 (33.7%) AIAN households has a parent or child with a disability. Similarly, more than one-quarter of white families, and more than 1 in 5 Black and Hispanic households have a parent or child with a disability. AAPI households had the smallest share (16.2%) of households with a disabled parent or child.

The share of low-income families that is a part of intergenerational households varies significantly by race and ethnicity group. More than 1 in 8 (13.1%) AAPI households are multigenerational or intergenerational, followed by 7.8% of Hispanic households and 5.9% of AIAN households. Economically vulnerable white families are the least likely to be intergenerational, as less than 4% have a grandparent in the household.

Table 1

The Great Recession and the pandemic recession: Differences and similarities

As this report contrasts the economic experience of families during two different recessions, it is important to first understand the severity and duration of these events.

By official accounts, the Great Recession began in December 2007 and ended in June 2009, representing the longest economic downturn to impact the U.S. economy in the postwar period (NBER 2010). We assess the severity of the Great Recession by examining the impact that it had on the labor market via the employment situation of workers of color (EPI 2025g). These workers are among the first to lose a job during a downturn of the business cycle. Between 2007 and 2010, for example, the unemployment rate for Hispanic workers more than doubled, rising from 5.6% to 12.5%. Similarly, the unemployment rate for Black workers shot up from 8.3% in 2007 to 15.9% in 2010. While the recession had been declared officially over by 2009, it took nearly a decade for the unemployment rate of workers of color to fully recover. This prolonged suffering was largely due to the anemic policy response that followed the Great Recession, largely characterized by austerity measures at both the federal and state levels (Bivens 2019; Bivens 2011). 

Compared with the Great Recession, the pandemic recession was considerably shorter. Officially, the pandemic recession only lasted two months, from February 2020 to April 2020, making it the shortest economic contraction in U.S. history (NBER 2021). But this doesn’t mean that the impact on workers was less severe. Just between February 2020 and April 2020, the unemployment rate for Hispanic workers more than tripled, and that of Black workers more than doubled.

Unlike previous contractions, the economic impact on women was particularly pronounced (Alon et al. 2021).[4] By April 2020, more than 1 in 5 (20.3%) Latina workers were out of a job and seeking employment, as the unemployment rate of these workers quadrupled between February and April of that year.[5] Similarly, the unemployment rate of Black women more than tripled during this period, rising from 5% in February 2020 to 16.4% in April 2020.[6] The nature of the economic shock explains much of the disproportionate impact on these workers, as the public health crisis and mitigation efforts fell most heavily on low-wage industries and occupations in which women of color are overrepresented due in large part to occupational segregation (Wilson 2020).

Despite the sharp rise in joblessness caused by the pandemic recession, the economic suffering didn’t last as long as during the Great Recession. Within two years, the unemployment rate for Black and Hispanic workers had fully recovered to 6.2% and 4.3% respectively, reaching historical lows (EPI 2025g). Black women and Latinas experienced similar rebounds; by 2022, the unemployment rate for Black women and Latinas (at 6.2% and 4.4% respectively) was among their lowest in recorded history (EPI 2025g). This swift and atypically even rebound was not just a function of a much shorter recession. As we detail later in this report, the swift and bold policy response to the pandemic and the economic contraction that followed was qualitatively different from that of previous recessions in the United States. Rather than the austerity and conditional support provided during the Great Recession, policymakers responded to the pandemic crisis with more generous cash transfers and extended support for unemployed workers and families with children.

Weathering crises: How did the last two recessions impact the employment security, poverty status, and housing insecurity of economically vulnerable families?

In this section, we examine how the Great Recession and the pandemic recession impacted the well-being of low-income families in three domains: their employment security, poverty status, and housing insecurity.

Employment security: Labor market attachment of families and employment rate of parents

One way of assessing the impact that business cycle downturns have on the economic well-being of families is by examining the impact that these events have on their employment security and attachment to the labor market. Since earnings represent the primary source of income for most families, involuntary separation from the labor market is likely to magnify the economic hardship experienced by these households. In this section, we examine changes in the labor market attachment of economically vulnerable families by looking at the share of families with at least one full-time earner and by capturing shifts in the employment rate of parents between the ages of 25 and 54.

Labor market attachment

Given the importance of work for low-income families, the prevalence of full-time employment in the household provides a measure of their attachment to the labor market. On average, more than two-thirds of low-income families had at least one full-time earner before the Great Recession. However, as can be seen in Figure A, differences in attachment existed by race and ethnicity even before the crisis. In 2007, 63.6% of Black families had at least one full-time earner, compared with more than 67.7% of white families and 77.7% of Hispanic families.

The Great Recession, and the weak policy response that followed, left a major dent in the labor market attachment of families. By 2010, the attachment gap between white and Black families had widened, as only 56% of Black families had at least one full-time earner that year, compared with 63.1% of their white counterparts. The share of low-income Hispanic households with at least one full-time earner also fell by nearly 10 percentage points, from 77.7% in 2007 to 68.1% in 2010. Comparatively, the rate of attachment for white families dropped by less than five percentage points during this period. While Hispanic families were more likely to report a stronger attachment to the labor market than their white peers, this advantage declined during the crisis and its aftermath. Overall, Black and Hispanic families took nearly a decade to recover, as their attachment to the labor market remained below the pre-crisis level in 2016. 

Leading to the COVID-19 pandemic and the recession, families of color regained a significant measure of the employment they had lost during the Great Recession. By 2018, for example, 64.4% and 76.6% of Black and Hispanic families respectively had at least one full-time earner. Largely due to the much shorter duration of the contraction and the robust policy response that followed, the pandemic recession had a much more muted impact on the labor market attachment of these families. Between 2018 and 2020, the share of economically vulnerable families of color with at least one full-time earner in the household declined only marginally, by about three percentage points for Black and Hispanic families.

By 2022, the share of Black families with a full-time earner had rebounded to 68.1%. This figure was nearly identical to the attachment rate for white families in the same year, and it represented the highest rate for Black families since 2007. While that number declined in 2023, it was still higher than in most years since 2007. On the other hand, by 2023, Hispanic families continued to lag considerably behind their 2007 peak.

Figure A

Employment rate of prime-age low-income parents

Examining changes in the prevalence of full-time earners within the household can provide us with a sense of the impact that crises have on the annual labor market attachment of families. But it does not capture monthly changes in the employment situation of parents over the business cycle. This is particularly important in the context of the pandemic recession since it represents the shortest economic recession in U.S. history. To best capture the impact that this economic contraction had on the employment situation of economically vulnerable parents of color, we examine changes in the employment-to-population (EPOP) ratio of low-income parents between the ages of 25 and 54.

In Figure B, prime-age Hispanic parents enjoyed higher employment rates than their Black and white peers before the pandemic. This pattern is also consistent with those shown in Figure A. Leading to the pandemic in January 2020, 94.7% of low-income Hispanic parents between the ages of 25 and 54 were employed, compared with 89.8% of white parents and 88.6% of Black parents, who face the greatest employment disadvantage historically.

As evidenced in Figure B, the gap in employment between Black and white prime-age parents widened during the pandemic recession. Much of this is explained by the disproportionate impact that the pandemic recession had on parents of color. Between January 2020 and April 2020, the employment rate of prime-age low-income Black and Hispanic parents plummeted by more than 32.7 and 27.0 percentage points respectively. By April 2020, only around half (55.9%) of prime-age Black parents had a job. At this point, prime-age Hispanic parents also saw their employment rate drop to a low of 67.7%. While the employment rate of white parents declined by 17.6 percentage points between January 2020 and April 2020, these parents remained about 29% and 7% more likely to be employed in April 2020 than their Black and Hispanic peers respectively. 

While the employment rate of parents of color declined to historically low levels in 2020, the bold policy response to the pandemic recession led to a quick rebound in the labor market. By the end of 2023, 88.9% of prime-age low-income Black parents and 91.6% of their Hispanic peers had a job. The strong recovery of parents of color also helped narrow the racial gaps in employment seen at the height of the pandemic recession in April 2020.

Figure B

Poverty status: Prevalence of poverty and severe poverty

As low-income families are largely dependent on wage earnings to meet their financial obligations, business cycle fluctuations can significantly affect their economic vulnerability. Without a proportional policy response or adequate social protection systems, these families are the first to fall victim to material hardship during an economic downturn. The Great Recession and the pandemic recession exemplify this, as these crises pushed more low-income families of color into poverty and severe poverty. This is evident when we examine changes in the prevalence, severity, and distribution of poverty over time.

Prevalence of poverty

Leading to the Great Recession, economically vulnerable Black and Hispanic families were more likely than their white peers to fall below the federal poverty line (FPL).[7] In 2007, more than half (53.6%) of low-income Black families were poor, relative to 44.6% and 38.7% of Hispanic and white families respectively (see Figure C). The Great Recession and the inadequate policy response to the downturn pushed a larger share of these families into poverty quickly and for a prolonged period of time. By 2010, more than half (51.2%) of Hispanic families fell below the FPL. The poverty rate for Black families continued to rise the following year, reaching nearly 6 in 10 (58.5%) in 2011. The poverty rates of both Hispanic and Black families did not return to pre-Great Recession levels until 2015, more than half a decade later. While racial gaps first widened and then narrowed throughout the crisis and the slow recovery, poverty rates remained much higher among Black and Hispanic families, relative to their white counterparts. 

By the lead-up to the COVID-19 pandemic and the recession that followed, the poverty rates of families of color were lower than they were in 2007, but a large racial poverty gap remained. While less than half (49.3%) of Black families fell below the FPL in 2018, they remained about 30% more likely to suffer material hardship than their white peers.

Largely because of policy, the material situation of families was not impacted as severely by the pandemic recession as it was during the Great Recession. While Hispanic families experienced a marginal increase in poverty between 2019 and 2022, rising by 3.3 percentage points (relative to the larger increase, of 6.6 percentage points, during the previous downturn), the share of Black families that fell below the FPL during this period declined. By 2022, low-income Black families recorded the lowest poverty rate (44.1%) in the entire period between 2007 and 2023. After economic relief measures expired, poverty rates were relatively stable for Hispanic families but had increased for Black families.

Figure B

Prevalence of severe poverty 

Economic downturns don’t just push economically vulnerable families into poverty. Without the support of a strong social safety net, families can fall deeper into economic deprivation when parents lose their jobs during a recession. The parents struggle to make ends meet and provide their children with the resources they need to flourish and to participate in society without shame. This happened far too often during the Great Recession as an increasing share of low-income families experienced severe poverty, with an income below half (50%) of the federal poverty line. To place this figure in context, the severe poverty threshold for the 48 contagious states and Washington, D.C., amounts to $7,825 annually for a single individual in 2025 (HHS n.d.).

Before being hit by the Great Recession, more than 1 in 4 (26.1%) Black families suffered severe poverty in 2007 (see Figure D). At this stage, Black families were about 61% more likely than their white peers to fall among the poorest of the poor. While Hispanic families fared relatively better in 2007 (with a severe poverty rate close to that of white families), disparities quickly widened. By 2010, more than 1 in 5 (21.6%) Hispanic families fell among the poorest of the poor, and an even larger share (30.3%) of Black families experienced similar material hardship, compared with 18% of their white peers. The anemic policy response to the Great Recession left an elevated share of these families under a prolonged state of economic deprivation until about 2015.

The strong policy response to the pandemic recession prevented a large uptick in the prevalence of poverty, especially for Black families, but severe poverty rates rose significantly for families as the material shortcomings of the most vulnerable worsened. Between 2018 and 2020, the share of Black families that fell among the poorest of the poor increased by 4.7 percentage points, from 22.5% to 27.2%. Hispanic families fared slightly better, as the severe poverty rate for these families rose from 15.6% in 2018 to 18.9% in 2021.

While the exposure of families of color to severe poverty fell in 2022, reaching a historic low of 22% for Black families, severe poverty again rose once economic relief measures ended. By 2023, the share of Black and Hispanic families among the poorest of the poor remained above the pre-recession levels of 2018. In contrast, severe poverty among white families had returned to the pre-recession rate by 2023.

Figure D

Housing insecurity: Prevalence and severity

Business cycle downturns that lead to significant job losses don’t just leave low-income families more vulnerable to poverty. Recessions also leave families much more exposed to housing insecurity, irrespective of whether these families own or rent their homes. As we illustrate below, this is because housing represents a significant expense for resource-constrained families. Low-income families of color are particularly vulnerable to even more pain during downturns as they are also forced to contend with an economy that suffers from an obstinate deficit in affordable housing and one in which the housing and lending markets have historically discriminated against them (Moore and Maye 2024).

In this section, we examine the impact that both the Great Recession and the pandemic recession had on the rent and homeownership rates of families of color. We also look at how the cost burden of housing evolved for both renters and homeowners during and after the crises.

Renters and housing insecurity

Given the high economic barriers to homeownership, Black and Hispanic families are generally more likely to rent, relative to their white peers (see Appendix Table 1). But, as homeownership rates declined during the Great Recession, the share of low-income families who rent has increased. Leading to the COVID-19 pandemic, in 2018, more than 80% of Black families and more than 70% of Hispanic families were renters. In contrast, slightly more than half (55.3%) of white families rented their homes that same year. While the share of renters was lower post-pandemic, racial gaps widened in 2023 with Black and Hispanic families being 61% and 36%, correspondingly, more likely to rent than their white peers.

The pandemic and the short economic downturn that followed exacerbated the already precarious position that low-income renters found themselves in after the Great Recession. By 2017, nearly a decade after the Great Recession, the share of economically vulnerable families that spend 30% or more of their income on rent remained above the pre-recession levels of 2007 (see Figure E). In 2018, for example, more than 8 in 10 Black and Hispanic families that rent were housing poor. The strong pandemic recovery did little to shelter these families from the housing affordability crisis in the U.S. that was amplified by the global health crisis (Moore and Maye 2024). By 2023, racial gaps had widened as the share of Black and Hispanic families that spend over 30% of their income on rent climbed above the peaks reached in the aftermath of the Great Recession.

Figure E

The impact of the last two recessions on families fell most heavily on those that spend more than half of their income on rent. While some of these renting families had recovered by the time that the pandemic recession rolled in, the share of Hispanic families experiencing severe housing insecurity remained above pre-recession levels in 2019 (see Figure F). Black families were particularly disadvantaged. Nearly half (48.3%) of low-income Black families spent over half of their income on rent in 2019. The situation quickly worsened for all families, as the strong economic recovery failed to protect these families from the growing affordability crisis in housing. By 2023, a higher share of white, Black, and Hispanic families spent more than half of their income on rent than at any other point since 2007. Low-income Black and Hispanic families remain most disadvantaged, as more than half of these families spend over 50% of their income on rent.

Figure F

Homeowners and housing insecurity

The Great Recession deepened the racial divide in homeownership rates, as families of color were disproportionately touched by the crisis (see Appendix Table 1). The share of low-income Black families that owned their home declined from 21.5% in 2007 to 15.7% in 2016, and from 34.6% to 28.3% during the same period for their Hispanic peers. By 2017, a decade after the start of the crisis, the homeownership rate of families had yet to recover, and racial disparities had widened. At this stage, economically vulnerable white families were 169% and 48% more likely than their Black and Hispanic peers respectively to own their home. 

Despite the steep gaps in homeownership, the pandemic recession didn’t quite lead to a suppression of homeownership rates for families. Partly as a function of younger households transitioning toward ownership, low interest rates, and the generous (albeit temporary) economic relief measures enacted in response to the pandemic recession, the downturn failed to reverse the gains in homeownership that economically vulnerable families of color were already experiencing in 2018 and 2019 (Sanchez-Moyano 2024; Callis 2023).

By 2023, slightly more than one-third (34%) of low-income Hispanic families owned their home, compared with about 3 in 10 (29.7%) in 2018. Black families also experienced gains. During this period, the homeownership rate of low-income Black families increased by 5.3 percentage points, from 16.4% in 2018 to 21.7% in 2023. By 2023, the homeownership of low-income Black and Hispanic families had achieved a near full recovery from both the Great Recession and the pandemic recession. While these achievements in homeownership helped narrow racial disparities, economically vulnerable families of color remained significantly less likely to own their homes in 2023 compared with their white peers.

While owning a home can be an important step toward wealth creation, economically vulnerable homeowners spend a significant share of their income on housing costs associated with mortgage payments, taxes, insurance, and more (U.S. Census Bureau 2004). Leading to the pandemic recession, Black homeowners remained more likely to spend over 30% of their income on housing costs (see Figure G). At this stage in 2019, 65.2% of economically vulnerable Black families who owned their homes were housing poor, compared with fewer than 6 in 10 Hispanic and white families. Despite the economic relief measures that helped economically vulnerable families weather the shock of the pandemic recession, housing insecurity rose for nearly all families. By 2023, a slightly higher share of Black and Hispanic families who owned their homes spent over 30% of their income on housing than in 2019.

Figure G

The impact of the pandemic recession and the increasing cost of housing in the U.S. is even more evident when we examine the situation of low-income families suffering from severe housing insecurity (Moore and Maye 2024). These are homeowning families who spend over half of their income on housing. Despite the short duration of the most recent downturn, the share of economically vulnerable families who face severe housing insecurity climbed by more than four percentage points between 2019 and 2023 (see Figure H). By 2023, Black families remained disproportionately vulnerable to economic pain with a prevalence of severe housing insecurity comparable to the hardship they experienced in the lead-up to the Great Recession. Since 2007, more than 2 in 5 economically vulnerable Black families who own their homes were unable to escape severe housing poverty as a result of having to spend over 50% of their income on housing costs.

Figure H

Lessons learned: Key policy choices that made a difference during the pandemic recession and are still needed to break the cycle of economic vulnerability for families

By nearly every measure of economic well-being examined above, low-income families of color weathered the pandemic recession better than the Great Recession largely because of policy choices. The weak policy response to the Great Recession, centered on austerity at the federal and state levels, contrasted sharply with the bold response to the pandemic recession guided by economic relief measures and public investments. This enabled families to avoid a prolonged separation from the job market and a worsening of their material conditions.

The last two recessions and their distinct recoveries left us with a clear blueprint for action. The economic lessons are not unfamiliar:

• Full employment policies that create tight labor markets also promote economic equity for workers and their families.
• Good jobs are union jobs.
• A strong social safety net helps families avoid unnecessary and scarring economic deprivation.

Breaking the vicious cycle that leaves low-income families more susceptible to hardship during recessions will require a renewed commitment to full employment, stronger worker rights and unions, and a robust welfare state that meets the needs of families and children. While the policies that can accomplish these objectives commonly face political headwinds, actions taken by the Trump administration and Congress will create even worse conditions.

Full employment policies are equity-enhancing policies 

While economists debate the overall rate of unemployment that constitutes full employment, there is less debate about the equity-enhancing effects of a tight or “high-pressure” labor market, one in which willing workers can obtain access to a job and the working hours they prefer (Bivens 2021; Bivens and Zipperer 2018). Sustained periods of low unemployment can effectively boost the earnings of low-wage workers and help narrow persistent racial disparities in a labor market that disproportionately disadvantages the employment situation of workers of color and the economic well-being of their families (Wilson 2023; Bivens 2021). The narrowing of these gaps would not constitute full healing from the legacy and continued expression of structural racism and xenophobia in the U.S. economy, but it would be a step in the right direction. Historical evidence points to increased economic equity via low unemployment and rapid job growth. 

The recent economic recovery from the COVID-19 pandemic and the economic contraction that followed serves as a good example of a policy regime that aimed, in large part, to provide a strong or high-pressure labor market. Unlike the economic recovery from the Great Recession, the rebound from the pandemic recession has been characterized by bold fiscal policies, via much-needed relief and strategic public investments, and more accommodating monetary policy that kept downward pressure on unemployment (Wilson 2023; Bivens 2024; Bivens 2016). The results of this policy regime are unambiguously clear: Workers of color made historic gains over the last five years in both employment and earnings, with Black and Hispanic real wages (adjusted for inflation) growing more than three times faster over the last five years than the four decades prior (Cid-Martinez, Maye, and Marvin 2025).

Instead of providing continuity to the economic regime they inherited, the Trump-Vance administration is pursuing a macroeconomic and trade policy that is sowing economic uncertainty and chaos and has already led to a contraction of economic growth in the first quarter of 2025.

Unions help narrow economic disparities that hurt workers and their families

It is easy to envision growing income disparities that threaten the economic security of working families as endemic features of the U.S. economy. Between 1979 and 2023, for example, the real annual earnings for the top 1% of earners increased by 181.7%, while the earnings for the bottom 90% grew just 43.7% (Gould and Kandra 2024). This economic divide mirrors another increasing gap between economywide productivity and the hourly pay of the typical worker, a gap that is even more pronounced for the typical Black and Hispanic worker (Moore and Banerjee 2021). But none of these trends is inevitable.

Behind these rising inequities one finds a wide range of deliberate policies choices that have weakened labor standards and stripped workers of their ability to bargain collectively for better compensation and working conditions (Mishel and Bivens 2021), including the erosion of union membership since the late 1950s (Bivens et al. 2023b).

Workers of color have been disproportionately touched by the decline of union density in the U.S. economy since they typically receive a larger wage boost from union membership. Compared with the premium of the average worker, the union pay premium is higher for Black and Hispanic workers (Bivens et al. 2023a). Black workers, for example, are more likely than white workers to be unionized (13.1% vs 11.2%), and the wage advantage unionized Black workers receive from being covered by collective bargaining is 12.6% (EPI 2025f; EPI 2025h). This premium is higher than the 11.9% average wage premium for unionized white workers. While Hispanic workers have slightly lower union coverage (9.7%) than white workers, they claim a higher union wage advantage of 16.4%.

Unions can also protect workers from discrimination and improve working conditions. Because private employment in the U.S. is for the most part “at will,” employers can terminate workers for nearly any reason, without providing notice or severance. This power imbalance harms workers of color disproportionately, as they are more likely than their white peers to report unfair dismissals (Bivens et al. 2023a). Unions protect these workers with the provision of “just cause” rights that shelter workers from discriminatory and retaliatory practices and unfair dismissals. Unions also offer workers better employment conditions. This is important for economically vulnerable families who face care needs alongside scarce resources. Unionized workers, for example, are more likely than their nonunion peers to have access to paid sick days and employer-sponsored health and retirement benefits (Shierholz et al. 2024).

Low-income working parents stand to gain the most from union membership. However, few of them belong to a union. Only 8% of prime-age Black parents and 4.9% of Hispanic parents belonged to a union in 2023. Similarly, only 5.3% of economically vulnerable white parents between the ages of 25 and 54 belonged to a union in the same year.

Instead of strengthening the rights of workers to bargain collectively, President Trump has openly embarked on an anti-worker agenda centered on weakening the federal agency tasked with protecting the most basic and fundamental U.S. labor rights, the National Labor Relations Board (McNicholas et al. 2025). These efforts will leave families of color much more vulnerable to discrimination in the labor market and to wage theft and mistreatment at work.

The social safety net expanded in response to the pandemic, which demonstrated that poverty remains a policy choice

The welfare of economically vulnerable families of color and their children is not an insurmountable problem beyond the reach of public policy. This became most evident during the COVID-19 pandemic. The federal government responded to this crisis boldly with an array of economic relief measures, such as economic impact or stimulus payments; with provisional expansions of social programs like the Supplemental Nutrition Assistance Program and the unemployment insurance (UI) program; with temporary enhancements of tax credits, such as the Earned Income Tax Credit (EITC) and Child Tax Credit (CTC); and with increased federal assistance to state and local governments. Overall, these measures kept millions of people out of poverty in 2021 (Banerjee and Zipperer 2022). The economic impact or stimulus checks alone kept nearly 9 million people out of poverty in 2021, including more than 2 million children (Shrider and Creamer 2023).

The expanded social safety net had a notable impact on alleviating the material hardship experienced by families of color. This is most evident when we look at trends in the prevalence of child poverty. For this, we rely on child poverty rates based on the Census Bureau‘s Supplemental Poverty Measure, which accounts for cash and in-kind transfers as well as geographic differences in housing costs. By this measure, the post-pandemic social policy regime looks particularly effective in its ability to reach children of color and to alleviate the human suffering that accompanies deprivation at a young age. Between 2019 and 2021, for example, child poverty rates fell by more than half across nearly all groups, reaching their lowest levels in recorded history (see Figure I). Before the pandemic, more than 1 in 5 Black and Hispanic children fell below the supplemental poverty line in 2019. By 2021, these rates plummeted by nearly 60%, as the Black and Hispanic child poverty rate dropped to 8.3% and 8.4% respectively. The Asian American and AIAN child poverty rates also declined by more than 40% during this period, reaching historic lows of 5.1% and 7.4% respectively in 2021.

Many of the gains in poverty reduction were driven by the expansion of the Child Tax Credit (Gould 2022). Relative to all income transfers in 2021, the expanded CTC drove an estimated 44% of the reduction in child poverty that year (Parolin 2023). The impact was especially pronounced for children of color (Burns and Fox 2022). For example, this expanded credit lifted an estimated 1.2 million Hispanic children out of poverty in 2021. Similarly, more than 700,000 Black children and over 100,000 Asian children avoided falling below the supplemental poverty line in 2021 because of the expanded CTC. The rest of the social policy levers (aside from Social Security) that drove the bulk of the historic reduction in child poverty had also been provisionally expanded under the American Rescue Plan Act (ARPA), including EITC, SNAP, and UI benefits.

Despite the powerful effect these measures had in extinguishing poverty, nearly all the enhanced social safety net measures under ARPA expired by 2022. This purposeful expiration erased the bulk of the gains in poverty alleviation that families and children of color had achieved economically in 2021 (Cid-Martinez and Zipperer 2023). This is evident when we examine how the end of the expanded welfare state impacted the prevalence of poverty for children of color. Between 2021 and 2023, the poverty rates of Black, Hispanic, Asian, and AIAN children had more than doubled, returning to or exceeding 2019 levels (see Figure I). This increase marked an obliteration of the gains achieved in poverty reduction between 2019 and 2021. In fact, by 2023, the poverty rates of all groups were either higher, or no different, than the pre-pandemic estimates of 2019. 

Instead of expanding the CTC to help more low-income parents meet the basic needs of their children and reduce poverty, the Republican-led budget reconciliation bill that Trump signed into law fails to increase benefits for the 17 million children who receive less than the full value of the credit because their parents earn too little to meet the earnings requirement (Maag 2025). The Republican law is also particularly harmful to children of migrant parents, as it revokes the credit eligibility of children that are U.S. citizens if both spouses in a married couple lack a Social Security number (Tax Policy Center 2025). At least one spouse will now need to have a Social Security number in order for a U.S. citizen child to qualify for the CTC. While the Republican law increases the maximum credit from $2,000 to $2,200 per child, no significant changes in the refundability structure and earnings requirement mean that CTC benefits will remain out of reach for the children of the poorest of the poor, while middle- and high-income families continue to receive most of the benefits (Collyer et al. 2025; Crandall-Hollick, Maag, and Jha 2025).

The Republican budget reconciliation bill that the president signed into law also missed an opportunity to break the cycle of economic vulnerability that poor children face with the “Trump accounts.” These new tax-free investment accounts will provide a single government contribution of $1,000 to every child born in the next four years (Hamilton and Pressley 2025). The current administration is also discussing these accounts as a “back door for privatizing Social Security,” a program that helps narrow racial and income disparities, lifting more than one million children out of poverty in 2023 (Price and Mascaro 2025; Morrissey and Bivens 2025; Shrider 2024).

Unlike the more popular Baby Bonds, which require sustained contributions from the federal government throughout childhood with the goal of narrowing the racial wealth gap, the Trump accounts are built on the mistaken premise that low-income families lack an incentive to save when the real issue is that they lack enough discretionary income to put into a savings account (Markoff, Radcliff, and Hamilton 2025). The employment, income, and wealth disadvantages that low-income families with children face leave them in a perennial struggle to access basic necessities like health care, housing, and child care. These families are often an emergency away from falling into poverty or severe poverty. Helping families escape this generational challenge will require more than a new savings vehicle that will further widen the divide between the rich and poor by providing yet another giveaway to rich families.

Figure I

Conclusion

Low-income families of color were disproportionately impacted by the economic suffering that came in the wake of the last two recessions. Both the Great Recession and the pandemic recession worsened the employment security, poverty status, and housing insecurity of these families. In contrast with the Great Recession, policymakers responded to the pandemic with a show of strength that helped families recover their employment and bounce back from poverty significantly faster. But housing insecurity and poverty continue to leave these families particularly vulnerable when the next recession strikes.

While the prospects of a recession continue to rise due to the chaos and uncertainty generated by the Trump-Vance administration, they are deliberately ignoring the lessons of the past. This administration has failed to protect the strong labor market they inherited, has failed to empower workers to bargain for better pay and working conditions, and has failed to strengthen basic needs programs. Instead, the administration is proudly advancing an economic agenda that forces austerity on low-income families, strips away protection from discrimination for people of color, and offers more tax cuts for those who do not need it—the ultrarich. This economic agenda will push even more families into poverty and prolong the pain that follows a recession.

Appendix

Appendix Table 1

Acknowledgments

Support for this research was provided by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the Foundation.

Notes

[1] Our classification of race and ethnicity is mutually exclusive, such that white families are non-Hispanic white, and Black families represent all families in which the head identified their race as Black in combination with other races. Hispanic families include those in which the head identified Hispanic origin, irrespective of race. Among the remaining pool, those who identified as American Indian in combination with other races are listed as AIAN, and respondents who identified as Asian or Pacific Islander in combination with other races (such as Asian and white or Pacific Islander and white) are listed as AAPI.

[2] Total family income is the sum of the individual incomes of each family member. Because unmarried partners are nonrelated household members, the unmarried partner’s total income is not incorporated in the primary family’s total family income. In cases where the income statuses of the household head and the unmarried partner are different, we use the income status of the household head.

[3] Similarly to race and ethnicity, the marital status of the family is informed by the status of the household head, such that married captures respondents who identify as married, irrespective of the presence of the spouse. All other responses are classified as not married.

[4] As we point out below, this disproportionately affected low-income families of color,  which are more likely to be headed by women.

[5] The unemployment rate here is captured by the seasonally adjusted unemployment rate of Hispanic women, 20 years old and over.

[6] The unemployment rate here is captured by the seasonally adjusted unemployment rate of Black women, 20 years old and over.

[7] This federal poverty line is informed by the official poverty measure (OPM) published annually by the Census Bureau since 1967. This measure uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. While the Supplemental Poverty Measure (SPM) is considered to be a more accurate and comprehensive measure because it accounts for government transfers and geographic cost-of-living expenses, including housing, published estimates only go back to 2009 (Shrider 2024). For the purpose of this analysis, we rely on OPM to capture the impact of both the Great Recession and pandemic recession.

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Wilson, Valerie. 2020. “Inequities Exposed: How COVID-19 Widened Racial Inequities in Education, Health, and the Workforce.” Testimony before the U.S. House of Representatives Committee on Education and Labor, Washington, D.C., June 22, 2020.

Wilson, Valerie R. 2023. “Tight Labor Markets Are Essential to Reducing Racial Disparities in the Labor Market and Within the Purview of the Fed’s Dual Mandate.” Journal of Policy Analysis and Management 43, no. 1: 322–328. https://doi.org/10.1002/pam.22545.

Zipperer, Ben. 2025. Trump’s Deportation Agenda Will Destroy Millions of Jobs: Both Immigrants and U.S.-Born Workers Would Suffer Job Losses, Particularly in Construction and Child Care. Economic Policy Institute, July 2025.

In reality, work requirements have nothing to do with getting people into the workforce. While increasing labor force participation and helping people obtain the dignity of work are important goals, people don’t actually need encouragement to do this. The incentive to work is already there: It gives people sufficient income to not live in grinding poverty. People with income low enough to qualify for social safety net benefits need support from policymakers to access programs like Medicaid and SNAP, not new rounds of bureaucratic paper pushing, which is what work requirements mainly achieve.

In a new report, I reviewed the research on work requirements and found that almost none of the alleged employment benefits of ratcheting up work requirements are economically significant. Several studies (here, here, and here) have causally estimated the impact of work requirements on SNAP, the food stamp program for low-income adults, and found no increase in employment following more stringent work requirements policies. With respect to Medicaid, two phone surveys in Arkansas following the 2017 introduction of work requirements found no discernible change in employment. This was in part because an estimated 38%–48% of recipients newly subject to work requirements were already working at the 20 hours per week threshold.

If Mr. Vought was more serious about improving access to work, he would be clear-eyed about the core barriers to work that low-income workers have traditionally faced: weak macroeconomic conditions, the volatile nature of low-wage work, and other barriers to work like caregiving responsibilities.

With respect to macroeconomic conditions, while today’s labor market is extremely strong, this has not been the norm nor is it something we can assume will persist in the future. The United States has spent far too much time with excess unemployment rates in recent decades. This macroeconomic failure is the responsibility of policymakers—individual workers have little control over the macroeconomic situation, yet it determines whether they are able to find regular work at sustaining wages. Employment rates for low-income adults are highly cyclical, rising when the macroeconomic environment is more favorable and overall unemployment rates fall, and falling when overall unemployment rises due to slack job markets. This is a key signal that these workers mostly do not need “encouragement” or “incentives” to work—they need opportunities. When opportunities arise in the form of strong labor markets, these workers flock to them.

In my analysis, I explored the association between number of hours worked for low-income adults and the unemployment rate between 1979 and 2019 to see how excess unemployment was related to work time. Figure A shows that as unemployment increases, the number of available jobs in a given local labor market becomes scarce and workers work fewer hours, suggesting that the jobs low-income adults take are much more tied to aggregate labor market health than to work requirements.

Figure A

Further, jobs available to low-income adults often pay low wages and have scheduling practices (such as little advance notice or time-varying schedules), which decrease the regularity and predictability of work time and can make it hard for workers to maintain consistent work hours needed to satisfy the requirements (by either working 80 hours per month or 20 hours per week). A 2014 study showed that disproportionately large share of workers in low-wage jobs (66% of janitors and housekeepers, 90% of food service workers, 87% of retail workers, and 71% of home care workers) reported their hours varied within the last month, highlighting the pervasiveness of such practices.  

Finally, given that many low-income workers on programs like SNAP and Medicaid have caregiving duties, policies that improve access to care would do much more to increase employment than simply mandating workers to work more. Studies show that when barriers to care are reduced or policies like paid sick leave are passed, women experience economically meaningful increases in their employment. This suggests that if the Trump administration wants to get serious about improving labor market outcomes for low-income adults, policies to support caregiving would be more effective than work requirements at achieving this goal.

In the end, work requirements function as reporting requirements for all recipients, making the process more onerous and burdensome. All recipients, including people with documented disabilities getting Medicaid would have to jump through additional bureaucratic hoops to prove that they’re exempted from work requirements, further risking lapsing or losing their coverage. These burdensome practices and paperwork ultimately lead people to withdraw from programs (see examples for SNAP and Medicaid).

Mr. Vought’s view that work requirements would increase labor force participation and employment is flawed and reflects inaccurate beliefs (or just lack of concern) about the barriers to work for low-income adults.  My analysis shows that there are still plenty of barriers that keep low-income adults out of the workforce, but insufficient incentives are not one of them. When labor market conditions are right, low-income workers do work and earn more than they do when unemployment is high, suggesting that macroeconomic policy has more to do with low-income adults’ ability to work than any work requirement-imposed threat to take away their health care or nutrition assistance. If policymakers were serious about creating opportunities to work, they would pass policies like secure scheduling laws and affordable care policies that would meaningfully reduce barriers low-income adults face in gaining employment.

Introduction

In recent years, Republicans in Congress have embraced proposals to ratchet up work requirements as conditions for the receipt of some federal government benefits. These proposals are clearly trying to exploit a vague, but pervasive, sense that some recipients of public support are gaming the system to get benefits that they do not need, as they could be earning money in the labor market to support themselves instead.1 Essentially, the push to increase work requirements rests on a belief that the prime barrier stopping these beneficiaries from supporting themselves solely through employment is a lack of motivation—since public benefits provide too comfortable a living, and beneficiaries lack the incentive to find paid employment.

However, a careful assessment of the current state of public benefit programs demonstrates that almost none of the alleged benefits of ratcheting up work requirements are economically significant, but that the potential costs of doing this could be large and fall on the most economically vulnerable. The most targeted programs for more stringent work requirements are the Supplemental Nutrition Assistance Program (SNAP, popularly referred to as food stamps) and Medicaid, the health insurance program for low-income people. EPI has surveyed the research literature on work requirements and how they interact with these two programs in particular, and we find that the existing safety net is too stingy and tilts too hard toward making benefits difficult to access. Tightening eligibility by increasing work requirements for these programs will make this problem even worse with no tangible benefit in the form of higher levels of employment among low-income adults.

Key findings:

• SNAP and Medicaid provide in-kind benefits for food assistance and health care to low-income families. These in-kind benefits are not generous enough to support a decent standard of living, and they provide no help with other expenses families have. In short, the incentive for adults to secure steady work at decent pay remains utterly enormous: It is the difference between living in profound material hardship versus having a more comfortable existence.
• Large numbers of beneficiaries of SNAP and Medicaid are children, retirees, or people with disabilities that prevent them from working. While most variants of work requirements seem to only apply to able-bodied adults without dependents or documented disabilities (ABAWDs), the administrative burden associated with these requirements might spill over and reduce take-up (and hence, incomes) for other beneficiaries.
• The primary barrier to work for low-income adults who want steady hours of employment is the state of the macroeconomy—conditions that are far beyond their control. The history of work requirements often casts them as an effort to break a “culture” of nonwork among some communities, with the implicit argument being that many beneficiaries will choose not to work even when steady jobs with decent pay are readily available. The evidence strongly contests this: Low-income adults’ employment surges when overall unemployment is low, and they work more hours and are able to earn more as a result. When unemployment is high, however, low-income adults are often the first to lose their jobs and see large hour declines as well.
• Too many jobs available to adults in low-income families are often characterized by irregular and unpredictable scheduling practices, making it hard for workers to plan for and maintain consistent work.
• More stringent work requirements implemented in the past have largely failed to boost work in significant ways because these requirements do not attack the core problems of weak macroeconomic conditions, the volatile nature of low-wage work, and other barriers to work like caregiving responsibilities.
• Many of the programs that some people might be excluded from by work requirements can meaningfully be thought of as work supports. Some public benefit programs—particularly Medicaid and SNAP—serve as human capital investments that can boost long-run earnings and employment prospects.

In the end, we suspect much public support for enhanced work requirements reflects imprecise thinking about the true costs and benefits of implementing them in the messy world of low-wage work in the United States. Other support might stem from the ethical decision that it’s worse for society to have even a tiny number of “undeserving” people receive public support than it is to have thousands of deserving families shut out of needed benefits because of the onerous administrative burden associated with tightened work requirements. But this is not an ethical decision we share. We think a decent, compassionate welfare state should err on the side of protectiveness rather than exclusion—but this trade-off is the hinge of political decisions around ratcheting up administrative reporting on work requirements.

What are work requirements, and what problem are they supposed to solve?

Work requirements are policies that mandate individuals work and administratively document a certain number of hours as a condition to receive benefits like SNAP and Medicaid. At a broad level, work requirements don’t seem to belong with welfare-state programs like SNAP and Medicaid. These programs were historically established precisely to provide a floor to living standards for people who cannot support themselves through earnings from the labor market. This includes children, the elderly, students, adults with disabilities or primary caregiving responsibilities, or those seeking work who cannot find it.

The implicit claim made by many advocating for strict work requirements on public support programs is that benefits have become too easy to obtain (even for those who could find paid work) and too generous relative to labor market earnings. The claim continues that this excess generosity has incentivized a portion of the population to game the system by choosing to live on public benefits rather than work. The argument, hence, claims that by making it harder for this group to obtain benefits, their incentive to work will be increased, and this will raise employment rates. In practice, existing work requirements are mainly aimed at adults without dependents or documented disabilities—a group that proponents of work requirements think could be working if only the public support systems were not so generous relative to paid employment. 

Before moving on to the flawed assumptions this view makes about the population that work requirements apply to, it is worth noting that there is almost no cash assistance that is unconditional on work and available to that population. Most current policy debates about work requirements center on the Supplemental Nutrition Assistance Program and Medicaid. These programs provide in-kind benefits to purchase food and health insurance to cover medical bills if one needs health care. In short, there is no prospect at all of somebody achieving a remotely comfortable living standard by forgoing work and relying exclusively on the benefits provided by these programs. People need much more than food and health care to live a decent life.

Besides the logical flaw that millions of Americans are voluntarily choosing to not work because public benefits offer such a comfortable alternative, the advocacy of tighter work requirements often makes several flawed assumptions about the labor market trade-offs that individuals affected by work requirements face (often referred to as ABAWDs, short for able-bodied adults without dependents or documented disabilities). Supporters of stricter work requirements assume that existing social safety net benefits are easy to access and provide a generous-enough living standard to be a reasonable substitute for steady work. In addition, these supporters assume that good-paying, steady jobs are readily available and a viable alternative to living on public benefits. This is often false. Our analysis shows that low-wage labor markets in the United States do not readily provide enough hours or wage and salary income high enough to guarantee a minimally decent life for workers. Further, it assumes that ABAWDs have no other health issues or caregiving responsibilities that would prevent them from finding steady work.

This view doesn’t square with reality, both with respect to the trade-offs low-income families face between work and nonwork and the labor market experiences of people who would be affected by work requirements (many of whom already seek work regularly and take it when it’s available). In this report, we document the economic claims (some implicit, some explicit) made by policymakers who support taking away benefits from individuals who fail to meet work reporting requirements, and we show why these claims don’t reflect the reality of low-wage labor markets and the poor and near-poor families who must try to carve out a living from them. We further document the evidence of the impacts of work requirements on individuals and communities. The evidence shows the real harms work requirements can cause and suggests that ratcheting up their stringency would greatly amplify these harms.

Adults without dependents or documented disabilities are most likely to be targeted for work requirements

Today’s work requirements generally target non-elderly adults without documented disabilities who do not have dependents living in the home, a group that work-requirement advocates claim should be able to find steady and income-sustaining work. These ABAWDs make up one-third of the U.S. population (Bauer, Hardy, and Howard 2024). ABAWDs that have incomes less than 200% of the federal poverty line (FPL) are 8.2% of the total population, which would put them in income ranges to be eligible for social safety net programs like SNAP and Medicaid (Bauer, Hardy, and Howard 2024). Table 1 compares the demographic and safety net profile of all adults ages 18 to 59, adults who are SNAP and Medicaid users, and low-income ABAWDs. Compared with all adults, those receiving SNAP and Medicaid are disproportionately likely to be women and nonwhite. They are also less likely to have a college education—only 15% of adults on SNAP and Medicaid have a bachelor’s degree or higher. Finally, adults on SNAP and Medicaid are much more likely to have an elderly person in the household. This population looks very similar to the ABAWD population, in which nearly 50% of low-income ABAWDs are women, and a disproportionate share are Black and Hispanic (18% and 23%, respectively), relative to all adults. More than half of low-income ABAWDs have a high school diploma or less.

Table 1

While ABAWDs might not have documented disabilities that result in benefit receipt or have dependent children living at home full-time, they often experience health challenges and must take on some caregiving duties, each of which could provide a genuine barrier to finding steady work. We find that 21% reported having a disability that affects their ability to find and sustain work, suggesting that adults with genuine health barriers are being swept up in overly stringent work requirements. One potential outcome from a push to ratchet up work requirements for adults with disabilities is increased time spent doing administrative paperwork to more precisely define their health challenges.

Table 1 also shows that 13.8% of ABAWDs live with an adult over the age of 65 in their household, suggesting that many are potential caregivers in some form and likely have caregiving responsibilities beyond what is captured on paper. A recent Brookings Institution report corroborates this finding, showing that nearly 40% of low-income ABAWDs are parents, and 5% are noncustodial parents to children under the age of 21 (Bauer, Hardy, and Howard 2024). While most variants of work requirements seem to only apply to ABAWDs and not all SNAP and Medicaid beneficiaries, given how similar the overall populations are, and the fact that many SNAP and Medicaid adult users often have children, the administrative burden associated with these requirements might spill over beyond ABAWDs alone and reduce take-up (and hence incomes) for these other beneficiaries. Adding more work requirements will create confusion and more paperwork for all adults who receive SNAP and Medicaid, not just ABAWDs, the group most likely to have work requirements levied on them.

The social safety net offers minimal support to low-income ABAWDs

Despite ABAWDs having health challenges and caregiving responsibilities that make participation in the labor market difficult, our current social safety net does very little to support these adults. ABAWDs receive a very small share of in-kind income from benefits, and these benefits come with strings attached. In the case of food assistance through the Supplemental Nutrition Assistance Program, ABAWDs who are not homeless or veterans must work 80 hours per month to receive SNAP benefits or lose these benefits if they don’t meet the hours-worked criteria for three months in a row (USDA 2024). In the case of the Earned Income Tax Credit (EITC), adults without dependents receive an average annual benefit of $295, one-tenth of what families with children get (Crandall-Hollick, Falk, and Boyle 2023).

To get a sense for how much support ABAWDs receive from the government, researchers compared the poverty rate of ABAWDs with comparable adults with children before and after social safety net transfers (Gornick et al. 2024). Between 2016 and 2019, the pre-tax, pre-transfer poverty rate for nondisabled childless adults was 12.7%, and after taxes and transfers, the poverty rate for this group was only 2.4 percentage points lower (10.3%). By contrast, for nondisabled adults with children, social assistance programs reduced the incidence of poverty for this group by 5.4 percentage points—more than twice as much as that of childless adults. This lack of social safety net support for nondisabled, childless adults is unique to the U.S. Further, the U.S. ranked last in its ability to reduce poverty for nondisabled, childless adults—reducing poverty for this group by only 19% compared with 35%–66% for Canada, the UK, the Czech Republic, Finland, Ireland, and the Netherlands (Gornick et al. 2024).

With very few safety net programs to support them otherwise, ABAWDs are hence already heavily incentivized to work, so tighter work requirements are not needed to further this incentive. Under the status quo, living standards for ABAWDs are far higher if they are able to find steady, sustaining work.

Low-income adults generally face steep labor market challenges, making it difficult to meet work requirements

With such meager government support, adults without dependents or documented disabilities are clearly motivated to turn toward employment and to work more when possible. Moreover, just because adults with children are so far exempt from some work-requirement proposals, they, too, would benefit from increased employment. If advocates of work requirements were motivated to boost living standards for low-income households through policy, then efforts to boost employment opportunities across all low-income households, not just ABAWDs, would make sense.

The first clear labor market challenge for ABAWDs is one shared by low-income families with children and even by more privileged job seekers—the United States has spent far too much time with excess unemployment rates in recent decades. This excess unemployment rate represents a clear and profound macroeconomic policy failure: the failure to maintain economywide spending at levels that would ensure that employer demand for workers was strong enough to soak up all willing workers in a reasonable amount of time (Bivens and Zipperer 2018). This macroeconomic failure is something no individual worker has control over, yet it is entirely determinative of whether all (or even the vast majority of) potential workers are able to find regular work at sustaining wages.

Advocates for more stringent work requirements gloss over this reality of the labor market. They portray the population affected by work requirements as stubbornly refusing to move into paid employment when it is available—often claiming they are afflicted by a “culture” of nonwork. But when unemployment is high, steady work is often unavailable. Employment rates for low-income adults without dependents (just like work for all adults) is highly cyclical, rising when the macroeconomic environment is more favorable and overall unemployment rates fall, and falling when overall unemployment rises due to slack job markets.

Figure A shows the number of hours worked for adults in the bottom 30th percentile of the household income distribution between 1979 and 2019. As unemployment increases, the number of available jobs in a given local labor market becomes scarce, and workers work fewer hours, suggesting that the jobs low-income adults take are much more tied to aggregate labor market health. Given that more than 80% of workers in this group lack a college degree, these adults are much more likely to work in sectors that don’t require higher levels of education, such as construction or the restaurant industry, which are highly cyclical in nature.

Figure A

Figure B shows a similar pattern for household income from wages. When unemployment is low, income for low-income adults increases, presumably because jobs are less scarce, and workers are able to work more hours. However, as unemployment increases, household income from work declines.

Figure B

By making the incentives faced by ABAWDs the central policy concern that motivates work requirements, rather than the larger macroeconomic environment, advocates for work requirements are not being honest about the real barriers to work.

Low-wage work is precarious, making work time hard to maintain

In addition to work availability being tied to labor market health for low-income ABAWDs, the types of jobs available to this group even in times of general economic health often pay low wages and are precarious in nature. It is difficult for workers to maintain a consistent number of work hours each month when they are scheduled for many hours one week and a few hours the next week, or when their employee classifications shift from full-time, full-year employment to part-time, temporary, and contractor work arrangements (Farber 2008; Kalleberg 2009, 2011, 2012; Kalleberg and Marsden 2013). These employment arrangements and scheduling practices, which are often out of workers’ control, decrease the regularity and predictability of work time and can make it hard for workers to maintain the consistent work-hour requirements needed to satisfy the documentation (by either working 80 hours per month or 20 hours per week). For example, workers with seasonal jobs would only be eligible for benefits during the season that they work but would be unable to access benefits at crucial moments when they’re laid off, even if they have reasonable assurance that they will be reemployed or they are actively seeking employment. Moreover, a disproportionately large share of workers in low-wage jobs (66% of janitors and housekeepers, 90% of food service workers, 87% of retail workers, and 71% of home care workers) reported their hours varied within the last month, which highlights the pervasiveness of such practices (Lambert, Fugiel, and Henly 2014).

Figure C shows the rate at which workers report having variable hours, conditional on working, for workers in the bottom 30% of household labor income and workers in the upper 70%. Workers in low-income households have substantially higher rates of hour variability, with the gap widening during economic downturns and narrowing during periods of labor market health. Critically, research has shown that the rise in hour variability often goes hand in hand with low numbers of work hours, where workers with low income both work the fewest hours during downturns and have the most variable hours (Cai 2023).

Figure C

Hour variability is just one type of scheduling practice that might make it hard for workers to document working a consistent 20 hours per week to fulfill work requirements. Figure D below, reproduced from the Shift Project, highlights the pervasiveness of scheduling practices that make it challenging to rely on a job to provide steady income over time (Zundl et al. 2022). These practices include employers canceling shifts, changing shift times, and giving less than two weeks’ notice for a work schedule to employees. While these trends are just for the service sector, this is an important industry to understand in the context of work requirements since many of the workers in the service sector earn low wages and are eligible for SNAP and Medicaid.

In 2021, around 15% of workers reported having their shifts canceled, and nearly 40% of workers reported working a so-called “clopening” shift (a scheduling practice where managers schedule the same worker to work a closing shift followed by an opening shift the following day). Just over one-third of the service sector reported being scheduled for an on-call shift, and around 60% of workers reported a shift-timing change or were given less than two weeks’ notice of a schedule change (Zundl et al. 2022). Critically, the graphs further show how these conditions persist even in favorable macroeconomic conditions. 

Figure D

These scheduling practices have implications for workers’ ability to have consistently predictable schedules and can have spillover impacts on their ability to hold down jobs altogether. Late notice or on-call shifts, for example, can make it very hard to plan for caregiving duties, and if workers need to use public transit, last-minute notice may cause them to be late or miss their shifts if they can’t catch the right bus. Unsurprisingly, job turnover is high in jobs with these scheduling practices. Figure E shows that workers who have to work on-call shifts have around a 7 percentage point higher rate of job turnover than workers who do not. Similarly, workers who experience scheduling time changes have a higher rate of turnover than workers who do not have employers that change schedules at the last minute. Workers with little schedule notice and workers who experience canceled shifts, similarly, are more likely to experience job turnover than workers with more notice (Choper, Schneider, and Harknett 2021). 

Figure E

Taken together, the available labor market options for ABAWDs present often-overlooked challenges to maintaining steady work. Work is only available under the right macroeconomic conditions, and even then, the conditions of work can prevent workers from maintaining steady hours or employment. The high levels of turnover that occur from workers seeking better work conditions make it nearly impossible to maintain eligibility for income assistance if recipients are required to demonstrate consistent work. 

A Center on Budget and Policy Priorities (CBPP) report corroborates this by showing that half of low-income workers who were subject to Medicaid work requirements would have failed a work-hours test in at least one month over the course of the year (Katch, Wagner, and Aron-Dine 2018). Note, though, that this means that those workers would likely have passed the work-hours test in the eleven other months. Moreover, in occupations in which SNAP or Medicaid beneficiaries are concentrated, unemployment is twice as high as the unemployment rate for typical middle-class occupations (Butcher and Schanzenbach, 2018). Penalizing workers for job conditions beyond their control is yet another way that advocates for work requirements are not being honest about the real intent of work requirements.

Work requirements don’t actually boost employment

The stated goal of work requirements is often to increase work effort, often to 80 hours per month or 20 hours per week for ABAWDs. But the prior section showed that the jobs available to low-income workers generally are not good-quality jobs with dependable hours that enable workers to meet the thresholds to maintain benefits. Moreover, ABAWDs cannot rely on the social safety net for economic security, suggesting that the argument—that changing ABAWDs’ incentives (via work requirements) will bring more people into the workforce and off social safety net programs—is flawed. This flaw is confirmed in academic research: Where work requirements exist, there has been no meaningful increase in employment.

In the past, there have been empirical challenges in causal analysis estimating the impact of work requirements on employment. Described in Gray et al. (2023), participation in programs like SNAP is underreported in most major surveys, making it difficult to gauge any change in participation in response to policy (Meyer, Mittag, and George 2020; Ziliak 2015; Meyer and Mittag 2019). Additionally, not all adults who are eligible for programs like SNAP actually take up the programs, and these underlying preferences driving the take-up decision are unknown to researchers. As a result, estimates of employment based on a policy change on this population might be smaller in magnitude than they would be if we could simply capture adults who were eligible for SNAP and had a preference for using it. Finally, there may be selection bias if researchers limit the study sample to those most likely to be impacted by work requirements. In the context of SNAP, Gray et al. (2023) overcame empirical challenges by using linked administrative data to causally assess the impact of work requirements when they were reinstated after the Great Recession and found that the work requirements had no impact on employment. Others have similarly found no impact of work requirements on employment (Vericker et al. 2023; Stacy, Scherpf, and Jo 2018; Feng 2022; Cook and East 2024).

Studies of the effects of work requirements on Medicaid have yielded similar results. Two phone surveys in Arkansas following the 2017 introduction of work requirements to Medicaid found no discernible change in employment, in part because an estimated 38%–48% of recipients newly subject to work requirements were already working at least 20 hours per week (Sommers et al. 2020). This finding is totally consistent with our general argument here: Low-income adults already have a huge incentive to work but face high external barriers, aside from any internal barrier that might exist such as a lack of motivation. Given this, there is little reason to think that work requirements would meaningfully boost employment.2

By making the process of applying for crucial safety net programs more burdensome, work requirements effectively function like a cut to programs

While work requirements do not reliably increase employment, they do significantly increase the administrative burden and costs of applying for safety net programs. This increased administrative burden, in turn, reduces access and take-up. Prior studies have shown that administrative burdens of all kinds can reduce program enrollment (Cook and East 2024; Deshpande and Li 2019; Finkelstein and Notowidigdo 2019; Gray 2019; Homonoff and Somerville 2021), and increases in work requirements are no exception. 

For adults trying to ascertain if they’re eligible for safety net programs, policies that increase work requirements will lead to increased learning and compliance costs associated with gaining access to these programs (Herd and Moynihan 2018). For example, when new work requirements go into place, adults with limited literacy or who face language barriers may not know about these requirements and lose coverage as a result. Workers may also face hurdles in getting employers to provide enough hours to meet the work requirements and the additional paperwork to verify employment hours per week (Bauer and East 2023).

While some of the reductions in caseloads due to work requirements may truly be because workers can’t satisfy the arbitrary hours threshold, in many cases, the sheer amount of additional administrative burdens levied on adults seeking benefits, and on case workers screening to ensure that work requirements are met, is a major driver in the decline in participation. In the same rigorous academic studies that found minimal employment impacts in response to work requirements for SNAP, researchers found that the new work requirements increased program exits by 64% among incumbent participants, and for participants newly subject to work requirements, program participation was reduced by 53% (Gray et al. 2023). In response to Medicaid’s work requirements in Arkansas, researchers documented a decline in Medicaid coverage by 13.2 percentage points, while the share of the uninsured increased by 7.1 percentage points (Sommers et al. 2019).

The consequences of losing access to SNAP and Medicaid for low-income adults are severe, often resulting in food and health insecurity. Losing SNAP eligibility has been found to increase the incidence of experiencing physically unhealthy days by 14% (Feng 2022). Following the Medicaid work requirements experiment in Arkansas, researchers compared outcomes for respondents who were still enrolled and respondents who were disenrolled from the program. Among those disenrolled, 49.8% reported serious problems paying off medical debt, almost twice as many respondents compared with 26.8% for those still enrolled. Moreover, 55.9% of disenrolled respondents delayed needed care in the past year because of cost, and 63.8% delayed taking medications because of cost. By contrast, only 28.2% of enrolled respondents delayed needing care, and 32.8% delayed medications (Sommers et al. 2020).

The silver lining in these findings is that when administrative burdens are reduced, people regain access to crucial benefits. For example, during the 2008–2009 Great Recession, policymakers significantly (if temporarily) reduced requirements for SNAP receipt. These changes were made because it was recognized that the recession—a change in macroeconomic conditions outside the control of potential beneficiaries—was driving the increased need for SNAP. Researchers found that these administrative policies that relaxed requirements explained 28.5% of the 69% increase in SNAP participation (Ziliak 2013). Research has further shown that participation is greatest when measures to reduce the administrative burden for SNAP—like lengthening eligibility and recertification periods, eliminating in-person interviews, and offering online applications—are more generous (Herd and Moynihan 2018; Prenatal-to-3 Policy 2024).

Policies that would measurably improve employment in low-income households

For those genuinely concerned about improving access to work, there are policy choices that are far more effective than work requirements.

This agenda would include macroeconomic policy to maintain full employment but would also stress policy choices that remove real-world barriers to work like providing access to child and elder care, reducing incarceration, enforcing antidiscrimination laws, and removing unnecessary credentialization in the hiring process.

Maintaining full employment: Our analysis in prior sections shows a clear link between hours worked and earnings for low-income adults and the national unemployment rate, suggesting that when the economy is at full employment, low-income adults stand to benefit the most from greater access to work opportunities. It is also worth noting that full employment also leads to strong reductions in the federal budget deficit—something else proponents of work requirements also claim is a goal (Bivens 2019).

Increasing scheduling predictability: Adding hours criteria to individuals in jobs with schedule practices that are out of their control only increases both barriers to work and economic insufficiency. If policymakers were serious about supporting individuals who want steady work, they would call for workplace protections that reduce precarious scheduling practices, thereby providing more employment security. Policies like secure scheduling laws can go far to improve employment prospects for low-income ABAWDs. Studies of new secure scheduling laws, which mandate that workers get at least two weeks’ notice of their schedule, among other rules, found that these policies increase economic security and worker health and well-being (Harknett, Schneider, and Irwin 2021).

Providing better help with caregiving responsibilities: For adults with caregiving duties, a key barrier to work is the prohibitively high cost of care. Given that 5% of ABAWDs have noncustodial children under 21 and 14% of ABAWDs live with a person over the age of 65, access to affordable child and elder care could make a huge difference for this population of adults. Women charged with caregiving have lower employment and earnings than those with similar characteristics who are not caregivers (Maestas, Messel, and Truskinovsky 2024). Policies that could support caregivers or reduce the cost of elder care could significantly help ABAWDs gain access to employment. For example, studies show that when barriers to child care are reduced through informal or formal care, women are much more likely to work (Posadas and Vidal-Fernandez 2013; ASPE 2016).

Reducing the labor market scarring of incarceration: For Black men, incarceration presents a uniquely challenging obstacle in terms of gaining employment (Pager 2003; Williams, Wilson, and Bergeson 2019). At least one in five Black men will experience incarceration at some point in their lives (Robey, Massoglia, and Light 2023), and any instance of incarceration severely reduces their subsequent likelihood of gaining employment (Pager 2003). Policies that reduce incarceration and recidivism rates, such as job reentry programs, could do much more to support this population’s gaining employment.

Reducing misplaced credentialization: Degree inflation—the rising demand for a four-year college degree for jobs that previously did not require one—has been found to be costly to workers in terms of missed job opportunities (Cohen 2023). A Harvard Business Review study found that over 60% of employers indicated that they would reject candidates that otherwise fit their job descriptions because they did not have college diplomas (Fuller and Raman 2017). Given that a large share of ABAWDs lacks college degrees, this can hinder their ability to gain access to employment they might be fully qualified for otherwise. Moreover, degree inflation appears to be responsive to local labor market supply. When there is an economic downturn and an increased number of people looking for work, employers are more likely to increase skill requirements (Modestino, Shoag, and Ballance 2020). By contrast, when the economy is tight, employers tend to relax them (Modestino, Shoag, and Ballance 2016). This suggests that the degree requirement, rather than being a necessary qualification for a job, serves as an unfair screening device that may actually hurt employers in the long run.

Reducing education requirements has been a bipartisan policy initiative in Alaska, Maryland, Pennsylvania, and Utah, where governors eliminated the requirement of a four-year college degree for many jobs in state government. Research has also shown that when degree requirements are dropped, employers are more specific in their job descriptions about the types of skills required for a job, which has the potential to open up new job positions for an additional 1.4 million workers (Fuller et al. 2022).

Offering better transportation options: Transportation access continues to be an issue for low-income adults and adults in rural areas (Mengedoth 2023). Low-income adults are less likely to own cars and more likely to take public transit. Public transit tends to have longer commute times and can occasionally break down, making it difficult for workers to get to work on time. Investments in more frequent public transportation with wider geographic coverage could support low-income workers’ employment prospects.

Reducing existing work requirements: This is intentionally provocative, but prior sections have described the health and nutrition consequences of failing to gain access to safety net programs like SNAP and Medicaid, such as food and health insecurity, which could be a barrier to work. Further, the existing research base shows near-zero measurable benefit of existing work requirements in promoting work. The upshot of these findings is that it is entirely possible that reducing eligibility barriers to safety net programs—barriers like work requirements—may well be more effective in promoting work than raising those barriers would be. A majority of adults who gained coverage through Medicaid expansion in Ohio and Michigan found that having health care made it easier to find and maintain work (Katch, Wagner, and Aron-Dine 2018).

Conclusion

A key policy trade-off for all welfare state programs is making sure that they reach all eligible populations while avoiding free riders who claim benefits that society and policymakers did not aim to make available to them. When eligibility rules are inclusive and generous, more people will be able to access these programs but with the risk that those who are not meant to be recipients may access these programs anyway. When eligibility rules are stringent and harsh, the likelihood of benefits going to populations not intended to receive them is certainly reduced but at the cost of reducing access to populations all agree the programs are meant to help.

The evidence surveyed above highlights that the U.S. safety net is too stingy and already tilts too hard toward making safety net benefits difficult to access. Further tightening eligibility screens will make this problem worse, with zero tangible benefit in the form of higher levels of employment among low-income adults.

Despite these facts, proponents of work requirements argue that these adults “have no excuse” to not be working since they don’t have children to take care of that live in the home or documented disabilities. Our analysis shows that there are still plenty of barriers that keep low-income adults out of the workforce, including disabilities and the increased likelihood of having an elderly person in the household. Further, we survey the available evidence on work requirements and find that they do not meaningfully increase employment. They ultimately just reduce the number of workers able to access safety net benefits. By picking ABAWDs as the group to be mandated to maintain work requirements, policymakers are effectively identifying a group they view as “least deserving” to access benefits and punishing them with the hope of not receiving a lot of backlash.

We also show that when labor market conditions are right, low-income workers do work and earn more than they do when unemployment is high, suggesting that macroeconomic policy has more to do with ABAWDs’ ability to work than the absence of incentives does. Finally, we outline a wide range of policies that support work for low-income adults and argue these policies would be better at promoting work than any work requirement restriction. If policymakers were serious about creating opportunities to work, they would pass policies like secure scheduling laws and affordable care policies that would meaningfully reduce barriers low-income adults face in gaining employment.

Notes

1. Survey evidence shows that support for work requirements in Medicaid and the Supplemental Nutrition Assistance Program (SNAP) is high—69.8% of adults surveyed in 2022–2023 supported work requirements for Medicaid, and 72.5% supported work requirements for SNAP (Haeder and Moynihan 2023).

2. The Arkansas program was later deemed unconstitutional.

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Figure A

Despite the remarkable similarity in unemployment rates shown in Figure A, Black workers in Maryland and Alabama may not be as equally well off as they appear to be. Figure B reveals that between 2018 and 2023, a much larger share of Maryland’s Black population was employed than Alabama’s. In 2023, the employment-to-population ratio (EPOP) in Maryland was 64.6%, compared with just 55.5% in Alabama and 59.6% for the United States as a whole. 

Figure B

If Black unemployment rates are so similar in both states, why are employment-to-population ratios so different? Because of fundamental differences in each state’s approach to social and economic policy. While Alabama adopts the Southern economic development strategy, for example, Maryland does not. This strategy seeks to disempower workers—especially Black and brown workers—to ensure employers can extract their labor for as little compensation as possible. In practice, this translates to higher rates of incarceration in Alabama than in Maryland, especially for Black men. Alabama has no minimum wage, compared with Maryland’s $15 per hour wage floor. Alabama lacks pro-worker, family-supportive labor policies like Maryland’s paid sick days and paid family and medical leave laws. And Alabama underinvests in public services.

Each of these policy decisions limits job opportunities that support a decent standard of living and can lead workers to become discouraged and leave the labor market. Since workers who leave the labor market are no longer counted as unemployed, the Southern economic development strategy may be artificially lowering Alabama’s Black unemployment rate. To underscore how vastly different labor market conditions are for Black workers in two states with similarly low unemployment rates, we compare these components of the Southern economic development strategy in Alabama versus Maryland. 

Alabama incarcerates more of its residents

Figure C shows that Alabamians are incarcerated at a rate that is 1.4 times higher than all Americans and 1.7 times higher than Marylanders.1 This is remarkable given that the United States as a whole already incarcerates its citizens at a rate higher than any country in the world. It is not just that Alabama imprisons more of its residents than Maryland does, Black Alabamians are highly overrepresented in the prison population. 

Figure C

According to data from the Prison Population Initiative, in 2021 just 26% of Alabama’s resident population was Black compared with 43% of its jail population and 53% of its prison population. Maryland also incarcerates Black residents at a disproportionately high rate—Black Marylanders made up 29% of the resident population of the state but were 59% of Maryland’s jail population and 71% of those in Maryland’s prisons. However, since Maryland incarcerates residents at a lower rate, fewer Black residents are incarcerated overall: 594 per 100,000 Black Marylanders compared to 1,014 per 100,000 Black Alabamians are incarcerated in prisons alone. 

Incarceration disproportionately impacts Black men and has ripple effects in the labor market for reentering workers since having a criminal history makes it more difficult to find a job. Ban the box policies seek to reduce the stigma and penalty associated with a criminal record by eliminating disclosure on job applications and delaying background checks until later in the hiring process. Maryland has a statewide ban the box policy for both public and private sector employment, while in Alabama, ban the box has only been adopted for the city of Birmingham.

While these facts do not establish a causal relationship, a striking pattern between rates of incarceration and EPOPs for prime age Black men emerges. Between 2017 and 2021, prime-age (25–54) Black men in Maryland, where incarceration rates are lower, were an average of 14.9 percentage points more likely to be employed than prime-age Black men in Alabama. As shown in Figure D, 83% of prime-age Black men in Maryland were employed compared with just 68.1% in Alabama and 75.4% of Black men nationally. Similarly, Black women were more likely to be employed in Maryland (78.6%) than they were in Alabama (70.6%) or nationally (72.5%). 

Figure D

Large numbers of low-wage jobs do not serve Alabamians and their families

Another factor distinguishing Alabama’s labor market from that of Maryland is the quality of available jobs. The Southern economic development model prescribes that workers are paid low wages and, in keeping with this, Alabama has no state minimum wage; what applies is the federal minimum wage, which has been stuck at $7.25 per hour since 2009. In contrast, as of January 1, 2024, Maryland raised its minimum wage from $13.25 per hour to $15 per hour. 

In December 2023, before Maryland’s $15 per hour minimum wage was in effect, fewer than 10% of Marylanders were paid less than $15 per hour. But in Alabama, the percentage of workers was 22%: More than one in five Alabamians were paid less than $15 per hour. 

Many Alabamians lack access to paid sick leave and paid family and medical leave

In addition to differences in employment-to-population ratios, incarceration rates, and minimum wages, Alabamians are much less likely than Marylanders to have access to paid sick leave and paid family and medical leave—supports which are crucial for balancing work and family. For example, while 90.8% of workers in Maryland have access to paid sick leave, just 68.2% do in Alabama. Again, these dramatically different outcomes reflect different policy approaches in the two states.

Maryland has an earned sick and safe leave law whereby all workers can accrue up to 64 hours of leave time to use for their own or a family member’s illness and in cases of domestic violence, sexual assault, or stalking. The law requires employers with 15 or more employees to provide paid leave, while smaller employers may provide unpaid leave. Additionally, Maryland Family and Medical Leave Insurance goes into effect starting in 2026. This law requires all employers to participate in some form of paid leave insurance offering workers up to 12 weeks of paid leave for the birth of a child, their own or a family member’s serious illness, or to arrange for a family member’s military deployment. In contrast, Alabama does not require employers to provide workers with paid family and medical leave. Rather, employers or individuals may purchase paid family leave benefit policies from private insurance companies. This approach will undoubtably leave many workers, especially low-wage workers, without paid leave. For those who are covered, policies will likely provide fewer weeks of coverage and lower wage replacement rates at higher costs. 

Alabama underinvests in the public sector

The private versus public approach to paid leave is emblematic of broader differences in how the role of government is perceived in Alabama versus Maryland. These disparate views are clearly reflected in each state’s willingness to invest in its public sector, even when the cost is subsidized by the federal government rather than being paid from state and local revenues. Under the American Rescue Plan Act of 2021 (ARPA), federal funds were granted directly to state and local governments. In addition to investments in infrastructure and public health, other approved uses of these funds included offering hiring and retention bonuses to fill public employee vacancies and raising public sector wages. As of September 2023, Maryland has spent 86% of its total state allocation and increased public sector employment by 3.5% between September 2022 and September 2023. Alabama, on the other hand spent just 36% of allocated funds and public sector employment grew just 1.1% over the same period.

These data show that comparing state unemployment rates in isolation paints a misleading picture. At any level of geography, the unemployment rate overlooks workers who want a job but aren’t actively searching due to discouragement, care responsibilities, or other obstacles. But interstate comparisons of unemployment rates also fail to distinguish the quality of jobs available and how policy choices influence these outcomes. Maryland and Alabama provide a striking example of how policies to support working people, ensure adequate pay and access to paid leave, and invest in public goods can help drive higher rates of employment while helping workers to cover basic necessities like food, rent, childcare, and transportation. 

1. The overall incarceration rate includes those in prisons, jails, federal prisons, youth facilities, and involuntary commitments. Prisons and Jails make up the vast majority of these. 

 
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The challenge for each of us is to understand how race shapes the American experience in countless intersecting, and sometimes contradictory, ways that can be hard to disentangle from the influence of other markers of identity or class, such as gender. Given those complexities, anti-racist economic research and policy often involves nuance, and is not easily boiled down into a simple checklist or a formulaic step-by-step guide. In fact, even the most well-meaning attempts to “check all the right boxes” can come across as superficial, performative, detached, or worst of all, counterproductive.

Guiding principles for anti-racist research, the ‘bodycam’ for racial economic injustice

Phrases like anti-racist, racial equity, and racial justice have quickly become part of the standard lexicon of people and institutions grappling with what it really means to be diverse, equitable, and inclusive. These concepts, however, are more than just “woke” or “progressive” jargon. They are standards for making and sustaining meaningful changes that help to dismantle social, economic, and political structures that perpetuate racial inequality. Here anti-racist research plays a key role. Rather than simply reciting the problem of racial inequity, anti-racist research questions its causes, exposes its consequences, and proposes ways to resolve it. Economists and other social scientists use data and statistical methods to model the processes of human decision-making and evaluate the effects of policy decisions. Those same tools also help to expose how race is used to systematically assign access, opportunity, power, and economic resources exclusive of individual skill, ability, effort, or merit.

The myth of race-neutral policy

Race-neutral policies—such as the drive to eliminate affirmative action—are harmful for achieving true racial equity and justice. Race-neutral policies fail to reverse the persistent and in some cases widening gaps between economic outcomes for Black and white Americans that are largely due to racism that is entrenched within the very fabric of our customs, laws, systems, and institutions. We must acknowledge and tackle the barriers posed by structural racism with race-conscious policies that target the intersection of race, class, and gender. Only race-conscious policies—policies that may disproportionately help communities of color—can dismantle the structural barriers to prosperity, safety, and equity for Black Americans.

Race and ethnicity in empirical analysis: How should we interpret the race variable?

In trying to understand racial and ethnic groups well enough to write policy that improves their economic outcomes, we have to have a clear understanding of what “race” means in statistical analysis and how the effect of race is measured. Race factors into economic outcomes in complicated ways that even more sophisticated statistical models can’t capture. We need to carefully interpret the effect or predictive power of race in measured disparities—in both descriptive and more sophisticated statistical models—because our assumptions affect how we design policy to address racial disparities.

Stratification economics: A moral policy approach for addressing persistent group-based disparities

Conventional ideas for how to shrink racial disparities rely on methodological individualism—the notion that racial economic disparities can be eliminated by developing the “human capital” of disadvantaged groups (i.e., by “fixing Black people”). Stratification economics rejects that approach as misguided and doomed to failure. Rather, this explicitly moral research discipline recognizes that structural forces limiting opportunities for Black Americans were set up by white Americans to preserve their economic dominance. Thus eliminating racial disparities requires policy interventions that make structural changes to the way our economy functions. Stratification economics seeks to reduce disparities to improve the health and well-being of communities first and foremost, not to improve productivity.

Serving, organizing, and empowering communities of color: Best practices for aligning research, advocacy, and activism

Improving economic opportunities and well-being in communities of color requires more than data and research. It requires grassroots groups that reject the transactional nature of electoral campaigns in favor of humility, deep listening, year-round engagement, and love. Only by questioning assumptions and organizing people around the issues they prioritize can you build trust and lasting change. For grassroots groups that want to truly advance policies that serve communities’ needs, there is much to take away from the lessons learned at Black Leaders Organizing Communities in Milwaukee.

Asian Americans and the anti-racist equity agenda: Contradictions and common ground

Asian Americans are a growing, predominantly progressive political force in the United States. On average, they favor a bigger government with more services and support affirmative action—and cite universal health care, progressive tax reforms, gun control, and the environment as top concerns. However, stereotypes about Asian Americans, as well as a small but vocal contingent of Asian Americans working against anti-racist policies (such as affirmative action) complicate efforts to sustain multiracial coalitions working toward racial justice.

Multidimensional identities of the Hispanic population in the United States

The Hispanic population in the United States is a large and diverse group of people with multidimensional identities. Existing survey instruments pose critical limits on research into this population and thus impact resulting policies. While the principle of racial and ethnic self-identification is important to respect and preserve, designing better surveys with more objective indicators of racial and ethnic background would provide a clearer picture of diverse subgroups and how they fare economically compared with one another and with other demographic groups. This is a critical step that would enable researchers to advance the collective understanding of the Hispanic population and thus allow policymakers to better address the challenges Hispanic people in the United States face.

The power of self-determination in building sustainable economies in Indian Country

Tribal governments are a significant part of the national economy, thanks to a policy shift toward tribal self-governance that ushered in an era of economic development, led by tribal gaming. Yet the economic and cultural shocks that deprived Native Americans of their livelihoods and social infrastructure for so long are still affecting Indian Country. To effectively address the economic and social challenges faced by Native Americans and their communities, policymakers and researchers must understand that tribal self-determination through self-governance is the only policy that produces positive results, and that further advances for Native Americans require tackling bureaucratic barriers such as tribes’ incomplete authority to put their lands to good and productive use, their inability to collect taxes to pay for government operations, and discriminatory higher costs for accessing capital.

Racial and ethnic disparities in the United States: An interactive chartbook

This interactive chartbook provides a statistical snapshot of race and ethnicity in the United States, depicting racial/ethnic disparities observed through population demographics; civic participation; labor market outcomes; income, poverty, and wealth; and 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.

]]> https://www.epi.org/publication/high-pressure-labor-markets-narrowing-racial-gaps/ Tue, 24 Aug 2021 09:00:48 +0000 https://www.epi.org/?post_type=publication&p=229440 One of the most compelling but underrecognized reasons that the Federal Reserve should aim for running the economy hot is the potential to narrow troubling racial gaps in wages and employment. Expansionary macroeconomic policies—policies that prioritize low unemployment over preemptively slowing growth in aggregate demand in the name of controlling potential inflation—create “high-pressure” labor markets characterized by very low unemployment and rapid job growth. While the legacy and present effects of structural racism mean that high-pressure labor markets by themselves are unlikely to fully erase race-based gaps in labor market outcomes, the potential to narrow these gaps is an undeniable benefit of more-expansionary macroeconomic policy.

The growing evidence that high-pressure labor markets can narrow these gaps calls for macroeconomic policies that test the absolute limits of how low unemployment can be pushed. Macroeconomic policymakers frequently weigh the potential benefits of higher-pressure labor markets against the potential risks, namely accelerating price inflation driven by excessively fast wage growth. The potential to close race-based gaps in the labor market should be counted as a substantial benefit in these deliberations and should convince policymakers to take on more inflation risk. Furthermore, running labor markets at the maximum sustainable pressure will provide much-needed information on what else we need to do to foster racial equity in labor markets.

This paper explores the promise and limits of high-pressure labor markets in reducing racial labor market gaps and how too-slack labor markets have helped thwart progress in closing the gaps. It then draws lessons from these investigations for policymakers. Its main findings are:

• Reductions in the unemployment rate boost hourly wages of typical (median) Black workers more than they boost hourly wages of typical white workers.
  • In 2019, the median Black worker was paid 32.2% less in hourly wages than the median white worker, up from 28.6% in 1973. Had the unemployment rate averaged 1 percentage point less annually from 1973 to 2019, the median Black–white wage gap could have declined to 18.0%. If the unemployment rate had averaged 2 percentage points less (a very ambitious target), the median Black–white wage gap could have fallen to just 5.4% (an 80% reduction in the size of this wage gap).
• Reductions in the unemployment rate provide an even bigger relative boost for median Black annual earnings, by increasing both hours worked and hourly wages.
  • In 2019, the annual earnings of the typical (median) Black worker amounted to just 80% of the annual earnings of the median white worker. Had the unemployment rate averaged 2 percentage points less annually over the 1970–2019 period, the Black–white median earnings gap (measured as a ratio) could have essentially closed. Had the unemployment rate averaged 1 percentage point less annually over that period, the typical Black worker in 2019 could have been paid 90.1% as much as the typical white worker—reflecting a 50% decrease in the Black–white median annual earnings gap.
• The Black–white unemployment gap (how much, in percentage points, the Black unemployment rate exceeds the white unemployment rate) closes significantly when the overall economy has fewer idle resources (i.e., when potential output climbs closer to actual output, leading to a rise in the measured “output gap”). For example, the Black unemployment rate falls more than twice as much as white unemployment when the economy’s output gap rises by 1 percentage point.
  • Even this disproportionate reduction in Black unemployment might understate how equalizing overall economic tightening can be. When the output gap measure rises 1 percentage point, the share of Black persons who are employed (the Black employment-to-population ratio, or EPOP) actually rises nearly seven times as much as the share of white persons employed (the white EPOP). But because the share of Black persons who are either working or actively looking for work (the Black labor force participation rate) also rises faster than white labor force participation when the output gap improves, the Black unemployment rate reduction is muted relative to gains in employment.
• Sustained high-pressure labor markets may have more power than we thought to close the Black–white unemployment ratio. For decades, the Black unemployment rate has been, on average, roughly twice the white unemployment rate. This persistent and distressingly high Black–white unemployment ratio (the Black unemployment rate divided by the white unemployment rate) has traditionally been seen as much more resistant to closing with high-pressure labor markets. However, the Black unemployment rate has only been included in most data sets since the early 1970s and, since then, genuinely high-pressure labor markets have been quite rare. Pre-1970s data that provide a potential proxy for the Black–white unemployment ratio show that sustained high-pressure labor markets may well actually reduce it significantly.

The policy lessons from this data are clear. While overall wage and price inflation remain the proper targets of policy (employment measures are not reliable enough to make good policy guides), policymakers need to change how they balance those targets:

• As they weigh the potential benefits of higher-pressure labor markets against the risks, policymakers should count, on the benefits side, potential reductions in chronic racial gaps in labor market outcomes.
• More forbearance should be exercised as wages and prices rise during economic recoveries and expansions, and at a bare minimum wage and price targets should be kept symmetric over business cycles: Every year that sees wage and price inflation come in 1% below target must be matched by a year with wage and price inflation coming in 1% above target.
• The potential to close race-based gaps in the labor market should convince policymakers to take on more inflation risk than they otherwise would have (that is, they should wait for actual and sustained, rather than forecast, inflation to appear before raising interest rates).

Background on the unemployment and inflation trade-off

All else equal, policymakers should aim for an unemployment rate so low that it reflects only the transitory and voluntary shifts of workers in and out of work or between employers. However, because of the way policymakers have traditionally sought to affect the rate of unemployment, they have instead aimed for a rate that was high enough to avoid any chance, even remote, of sparking inflation.

The primary way policymakers influence the unemployment rate is through measures that change the pace of aggregate demand growth. Aggregate demand is economywide spending of households, businesses, and governments. When this spending is strong, employers need workers to produce the output of goods and services needed to satisfy customer demand, which keeps unemployment low and employment growth strong. When this spending lags, less output and hence fewer workers are needed to satisfy demand, so employment growth lags and unemployment rises.

If policymakers boost economywide spending too much, however, demand might outstrip the productive capacities of firms. As demand runs ahead of supply, this puts upward pressure on wages and prices as firms scrambling to meet demand find they need to hire more workers and can charge customers a bit more for scarce goods. This “inflation barrier” to further efforts to boost demand—the point of tightness in labor markets that sparks an upward drift of inflation—may well be hit before the unemployment rate that reflects only voluntary job transitions is attained.

This balancing between demand growth that is strong enough to keep unemployment low, but not strong enough to generate accelerating inflation, is a central problem of macroeconomic policy (often called stabilization policy). Traditionally, the entity doing this balancing in the United States has almost always been the Federal Reserve, which tries to spur demand primarily by lowering interest rates and can brake escalating demand by raising interest rates. However, the Great Recession exposed the extreme limits of the Fed’s ability to generate strong enough demand growth and has elevated the role of fiscal policymakers (Congress and the president) in boosting (or failing to boost) demand by adjusting spending levels and taxation in the economy.1

Far too often in recent decades, policymakers have erred in targeting—or at least unnecessarily tolerating—demand growth that was too weak to generate enough pressure in labor markets to give workers leverage in wage negotiations with employers.2 This toleration of low-pressure labor markets was often done in the name of keeping inflationary pressures in check. But given that genuine inflationary pressures in the U.S. economy have been extraordinarily rare since the 1970s, the targeting of too-weak demand growth has often been about guarding against even the risk of inflation. A growing body of recent research notes that the benefits of low unemployment are large enough to justify taking on substantially more inflation risk than has previously been tolerated.

The most obvious benefits of low unemployment are more job opportunities for more people and more hours of work available to U.S. families. A less obvious benefit, but one that shows up strongly in the data, is faster hourly wage growth for the vast majority of U.S. workers, a particularly important benefit given the anemic pace of wage growth for these workers in recent decades.3 Yet another increasingly discussed benefit of low unemployment is its ability to put sustained pressure on compressing race-based gaps in the labor market. The rest of this paper largely tries to put some empirical bounds on just how large this last benefit might be.

Why aim for ‘high-pressure’ labor markets and not ‘full employment’?

The Fed’s legal mandate is to pursue maximum employment consistent with price stability. Over the years “maximum employment” has often been referred to as “full employment.” However, there is no universally agreed upon definition of full employment. For some, full employment simply means that anybody who wants a job can find a job. For others, particularly macroeconomists, it means attaining the rate of unemployment (often called “the natural rate”) below which further increases in economywide spending will mostly lead to accelerating inflation rather than greater output. “High-pressure” labor markets just mean labor markets characterized by low unemployment, fast rates of job creation, rapid job-finding among the unemployed, and sustained effort by employers to keep their enterprises properly staffed. Labor markets can be “high pressure” yet still tolerate further reductions in unemployment without leading to unsustainable wage or price inflation, and the term “high-pressure” may better connote a continuum of labor market states rather than a single fixed point.

Further, old theories of “disguised unemployment” and new developments in advanced capitalist economies (the rise of “gig work”) argue that “full employment” and “high-pressure labor markets” might not always coincide.

Joan Robinson (1936) defined “disguised unemployment” as follows:

In a society in which there is no regular system of unemployment benefit, and in which poor relief is either nonexistent or “less eligible” than almost any alternative short of suicide, a man who is thrown out of work must scratch up a living somehow or other by means of his own efforts. And under any system in which complete idleness is not a statutory condition for drawing the dole, a man who cannot find a regular job will naturally employ his time as usefully as he may. Thus, except under peculiar conditions, a decline in effective demand which reduces the amount of employment offered in the general run of industries will not lead to “unemployment” in the sense of complete idleness, but will rather drive workers into a number of occupations—selling match-boxes in the Strand, cutting brushwood in the jungles, digging potatoes on allotments—[that] are still open to them.

The modern U.S. economy obviously does not totally lack relief for the unemployed, and the reach and influence of the gig economy is often wildly overstated. But it seems clear that one margin of survival that many U.S. workers draw on when regular work is slack due to weak aggregate demand is to engage in gig or otherwise irregular work. But gig work generally does not provide high-quality jobs or economic security. In some deeply unsatisfactory sense, the rise of gig work could theoretically help fulfill the promise of one definition of full employment—that anybody “who wants a job can find a job.” But, in an economy with measured unemployment kept low only by a large incidence of gig work, if policymakers boosted aggregate demand, it is highly likely that many gig workers would leave the gigs behind and look for and find more regular work. In short, describing labor markets as “high pressure” might better describe the condition that employers are competing actively among themselves to attract workers.

High-pressure labor markets and median racial wage gaps

Since 1979, wage gaps between Black and white workers have widened significantly. Figure A shows the gap in two ways: how much less in percent terms the median Black worker earns in hourly wages than the median white worker, and the percent by which the average hourly wage of Black workers is less than the average hourly wage of white workers, holding other characteristics constant. The latter, a regression-adjusted average gap, controls for educational attainment, gender, ethnicity, and age. Both gaps widened significantly over time, but the median gap started larger and has expanded more rapidly since the late 1970s.

Figure A

Previous work has indicated that median Black wage growth responds more strongly to changes in unemployment than does median white wage growth.4 Figure B confirms this. The figure shows the relationship between the unemployment rate and wage growth. Specifically, it shows the change in wage growth that occurs if the unemployment rate rises by 1 percentage point. For white median hourly wages, a 1-percentage-point increase in overall unemployment is associated with wage growth that is 0.52% slower. For Black median wages, wage growth declines by 0.76%. As the figure shows, the coefficient for median Black wage growth is nearly 50% larger than for median white wages.5

Figure B

Figure C uses the estimated coefficients from Figure B and calculates counterfactual median Black–white wage gaps under three scenarios: unemployment rates that averaged 1, 1.5, and 2 percentage points lower over the 1973–2019 period. These scenarios are plausible alternatives of what might have been under a policy regime that determinedly aimed for high-pressure labor markets. Over this period, the unemployment rate was high, averaging 6.2%. One closely watched measure of the unemployment rate consistent with stable inflation—the nonaccelerating inflation rate of unemployment (or NAIRU) estimated by the Congressional Budget Office (CBO)—averaged 5.3% over this same period, almost a full percentage point lower. Additionally, between 1947 and 1973, the unemployment rate averaged 4.7%, exactly 1.5 percentage points lower than in the post-1973 period, and inflation before the oil price shock of 1973 was generally contained. Finally, when unemployment fell more than 2 percentage points beneath the 1973–2019 average in the late 1990s, and again in 2018–2019, there was no marked uptick in wage or price inflation requiring that macroeconomic policymakers slow demand growth.

Figure C

We should be clear that examining a counterfactual that assumes substantially lower unemployment on average does not reflect an assumption that recessions never happen. Instead, it simply assumes that macroeconomic policymakers do their job and ensure that every period of above-average unemployment is matched by an equivalent period of below-average unemployment. Estimates of the natural rate of unemployment are not hard floors below which the economy is never meant to go; instead they are averages that the unemployment rate should fluctuate both above and below. Running the economy far above even too-conservative natural rate estimates over decades is a policy failure that can clearly be addressed.

Achieving and sustaining high-pressure labor markets since the early 1970s would have dramatically narrowed the median Black–white wage gap. Had unemployment averaged 2 percentage points less over the entire period, 80% of the median Black–white wage gap that appeared in 1973 could have been erased (as the gap shrank from 28.6% to 5.4%). With unemployment averaging just 1 percentage point less (essentially just hitting conventional measures of the natural rate of unemployment), the median wage gap could have fallen slightly (to 18.0%) rather than rising by almost 8 percentage points over this period. In short, high-pressure labor markets hold great potential to reduce this particular measure of racial inequality in the labor market.

The gap-narrowing power of high-pressure labor markets is even more evident when looking at median annual earnings. Annual earnings can be affected by tighter labor markets not only through higher hourly wages but also through increased hours worked during the year. As shown in Figure B, the decline in Black worker annual earnings associated with an uptick in the unemployment rate is an even larger decline than the decline in Black worker hourly earnings. Applying the same counterfactual scenarios of unemployment rates that average 1, 1.5, and 2 percentage points lower over the 1973–2019 period yields dramatic results for the median Black–white annual earnings gaps, shown in Figure D. In this figure, the gaps are presented as ratios—how much Black workers earn as a share of what white workers earn.

Figure D

Between 1970 and 2019, the ratio of Black to white annual earnings rose from 62.4% to 80.0%. If the unemployment rate had averaged 1 percentage point lower after 1973, then this ratio could have surpassed 90% by 2019. If the unemployment rate had averaged 2 percentage points lower, the Black–white annual earnings ratio would have essentially been 1, indicating near-complete equality in this measure.

High-pressure labor markets and gaps in employment and unemployment

As we have frequently noted, the Black unemployment rate has been, on average, roughly twice the white unemployment rate since 1972 (the first year that Black unemployment is measured by the Bureau of Labor Statistics). Further, as shown in Figure E, this rough 2-to-1 ratio prevails if one looks at the measure of “nonwhite” unemployment compiled by the BLS before 1972 (Black workers accounted for a very large majority of nonwhite workers over that pre-1972 period).

Figure E

The Black–white unemployment ratio shrinks only slightly if one adjusts for age, educational credentials, and gender composition of the workforces. In the figure, this ratio is calculated by comparing the “Black adjusted” line (see figure note) with the line for the white unemployment rate. For example, between 1976 and 2019, the overall Black–white unemployment ratio averaged 2.3 while the adjusted ratio averaged 2.0. This is an improvement for sure, but a depressingly small one, at roughly 15%.

One conclusion that can be drawn from Figure E is that the ratio of Black to white unemployment is pretty stubborn: It does not seem to fall quickly during periods of labor market tightness (when all rates fall together). However, even if the Black-to-white unemployment ratio never moved, the raw gap in unemployment rates between Black and white workers (simply the Black unemployment rate minus the white unemployment rate) would shrink rapidly during periods of overall labor market tightness, and would expand rapidly during periods of overall labor market distress. At a minimum, this means that Black workers see disproportionate gains and losses from effective and ineffective macroeconomic stabilization policy, respectively. Thus, getting macroeconomic stabilization policy right is a key issue for racial equity.

Figure F confirms this intuition, using the output gap as a proxy for overall economic, and thus labor market, health.6 The output gap is a measure of how fully the economy’s resources are being utilized at any given point in time (resources including potential workers). Specifically, it is calculated as the quotient of actual gross domestic product (GDP) divided by a measure of potential GDP (what GDP could have been had the economy’s resources been fully utilized), minus 1. When actual GDP is lower than potential GDP, the output gap is negative. As actual GDP falls further and further behind potential GDP, the gap measure becomes more negative; as it comes closer to potential GDP, it rises.

Figure F

Given this, as the gap rises, resource utilization increases and the overall unemployment rate generally falls. The figure shows the relationship between the overall output gap and Black and white labor market indicators. As can be seen, the Black unemployment rate is twice as responsive as the white unemployment rate to a change in the output gap: specifically, a 1-percentage-point increase in the output gap (moving actual GDP 1% closer to potential GDP) is associated with a 1.57-percentage-point decline in the Black unemployment rate, compared with a 0.64-percentage-point reduction in the white unemployment rate. Because the BLS measures the unemployment rate of Black workers only after 1971, we also include a measure of the responsiveness of what the BLS labels “nonwhite” unemployment—a series that goes back to 1954. In the years before 1972, Black workers made up more than 90% of those labeled nonwhite.7 The advantage of using a series with a longer historical perspective is that one can examine a period of very tight labor markets that were achieved in the mid-to-late 1960s. The overall unemployment rate, for example, fell to under 4% for three straight years in the late 1960s. In this longer time series, the overall responsiveness of the nonwhite unemployment rate to an increase in the output gap measure (-1.54) is quite close to the responsiveness of Black unemployment in the more recent series.

These differential rates of responsiveness translate into substantial closing of the gap between Black and white unemployment rates when the overall economy tightens up, with this gap defined simply as the Black unemployment rate minus the white unemployment rate (i.e., the gap is by how many percentage points the Black unemployment rate exceeds the white unemployment rate). The figure shows that each percentage-point increase in the output gap (i.e., a tightening of the economy and labor market generally) is associated with a 0.70-percentage-point reduction in the Black–white unemployment gap. Each percentage-point increase in the output gap is also associated with a 0.90-percentage-point reduction in the nonwhite–white unemployment gap. It is possible that relatively greater responsiveness of the nonwhite–white unemployment gap is due to the inclusion of workers who are not Black in the nonwhite unemployment calculation. It is also possible that the difference is due to the longer time series available with the nonwhite–white unemployment rate gap. By restricting this series to just post-1971 data points (to make it consistent with the Black unemployment rate coverage), the responsiveness of the nonwhite–white unemployment gap to a 1-percentage-point reduction in the output gap shrinks to 0.83 percentage points.

Race-based differentials in the responsiveness of labor market indicators to a change in the output gap are even larger when examining the responsiveness of the Black and white employment-to-population ratios. The Black EPOP rises by 1.2 percentage points as the output gap increases, while the white EPOP rises by 0.18 percentage points, just over a seventh as much.

If the ratio of Black to white unemployment rates was constant, then the change in the gap between these rates would also just equal this ratio multiplied by the change in the white unemployment rate. Given the relative stubbornness of the Black–white unemployment ratio (for example, as seen in Figure E), it might seem that it is essentially constant regardless of the state of labor market pressure. But it may not be.

Can high-pressure labor markets reduce Black–white unemployment ratios, not just gaps?

Looking at the Black and white unemployment rates over time—like those displayed in Figure E—can easily convince observers that the ratio of Black to white unemployment is nearly constant. In good times and in bad, the Black unemployment rate looks to be roughly twice the white unemployment rate. But there are actually some reasons for optimism—tempered, to be sure—that this ratio is not as unyielding to change as it seems. For one, there seems to be a shallow but steady downward trend in this ratio over time. For another, more detailed evidence indicates that the Black–white unemployment ratio may indeed respond measurably to high-pressure labor markets. That evidence is highlighted in the discussion of the next two figures, which show the overall unemployment rate and the Black–white unemployment ratio (Figure G) and the nonwhite–white unemployment ratio (Figure H) prevailing at business cycle peaks. Both show a clear positive relationship between the overall unemployment rate and the respective ratios (i.e., an increase in one measure coincides with an increase in the other). All else equal, this would indicate that a higher-pressure labor market overall does indeed put downward pressure on the Black–white unemployment ratio.

Figure G

Figure H

However, the positive relationship in Figure G is likely driven in some part by the trend in the Black–white unemployment ratio coinciding with the fact that, since 1976, later business cycles have consistently achieved lower unemployment rates. Between the business cycle peaks of 1979 and 2019, the Black–white unemployment rate ratio actually declined by a bit over 20% (as did the adjusted ratio, a ratio that estimates what the Black unemployment rate would have been had the composition of the Black labor force shared the same age, educational credentials, and gender mix as the white labor force). This trend likely would have led to successively lower Black–white unemployment ratios in 1989, 2000, and 2019 anyhow. But on top of this trend, unemployment rates in these business cycle years were successively lower over time. Given this, it is not clear if it is a given year’s unemployment rate or a long-running trend that drives the pattern in Figure G.

To test the connection, Figure H includes data on nonwhite unemployment back to 1959 and thus includes business cycles not characterized by uniformly lower overall unemployment rates over time. The strong positive relationship between rising overall unemployment and an increasing Black–white unemployment rate ratio still holds.

Figure I looks at the responsiveness of various labor market ratios (not gaps, as was analyzed above in Figure F) to changes in the output gap while controlling for a time trend. The first three data points come from a regression that used a lagged measure of the output gap. They show a significant decline in the Black–white and the nonwhite–white unemployment rate ratios associated with each percentage-point increase in the output gap (remember, as the economy improves and actual GDP gets closer and closer to potential GDP, the output gap rises). As before, this analysis includes a look at the coefficient on the nonwhite–white unemployment ratio from this regression just in the years after 1971 to see if some of the difference between its responsiveness and the responsiveness of the Black–white unemployment ratio is simply due to different timespans. The responsiveness of the nonwhite–white unemployment ratio is roughly same (but actually increases slightly) when just looking at the post-1971 period.

Figure I

The results from estimating the responsiveness of ratios of EPOPs and labor force participation rates (LFPRs) demonstrates a similar pattern as that shown by Black–white employment gaps. When the output gap rises, the Black–white ratio of EPOPs rises, meaning that the share of Black persons employed approaches closer to the share of white persons employed. A similar increase holds for the ratio of nonwhite-to-white EPOPs and, again, the responsiveness of this ratio falls a bit when only the post-1971 period is examined. The ratio of Black to white LFPR rises when output gaps rise as well, meaning Black labor force participation approaches closer to white labor force participation. Again, the responsiveness of the ratio of nonwhite–white LFPRs is greater than for the Black–white ratios, but much of this seems due to the different time periods; when only post-1971 years are included, the responsiveness is very similar.

The upshot of this examination is that there is some suggestive evidence that even key labor market ratios (not just absolute gaps) that compare labor market performance for Black and white workers might indeed narrow during periods of high-pressure labor markets. This more hopeful interpretation may have been missed by those looking only at raw measures of the Black and white unemployment rates over time (like those shown in Figure E).

First, the post-1971 period might not contain enough episodes of truly tight labor markets to allow the relationships between high-pressure labor markets and the Black–white unemployment ratio to be well estimated. Figure I has some slight suggestive evidence of this: The responsiveness of the nonwhite–white EPOP and LFPR is greater when the pre-1971 period is included. This pre-1971 period includes a long stretch in the 1960s when unemployment was beneath 4% for four straight years (1966–1969). Further, Aizer et al. (2020) found that many racial gaps in labor markets (like the Black–white earnings gap and the measures of occupation segregation) fell significantly in the 1940s. These declines were concentrated in areas with more defense spending. This spending not only contributed to tighter labor markets but also often came attached with anti-discrimination conditions. The authors could not disentangle the precise effect of each of these influences, but the large spillovers of reduced race-based gaps in industries not directly affected by the defense spending suggests a large role for high-pressure labor markets generally. And the U.S. labor market of the 1940s was high pressure in a way not seen since: The unemployment rate was below 2% for three straight years between 1943 and 1945. In short, since we have begun measuring the Black unemployment rate specifically, we just may not have seen enough periods of genuinely high-pressure labor markets to get a solid statistical read on what happens to the Black–white unemployment ratio when labor markets get truly tight and are kept that way for a sustained period of time.

Second, while the employment rate of Black workers rises significantly faster than for white workers as the output gap rises, the labor force participation rate of Black workers also rises faster, muting any disproportionate decline in unemployment for Black workers. If the measure is simple joblessness and not unemployment, then it seems clear that the Black–white jobless ratio clearly declines when labor markets tighten up.

Part of the reason why the stronger responsiveness of the Black–white EPOP to output gap increases translates weakly into a reduction in the Black–white unemployment ratio is likely due to different dynamics of labor force participation over the business cycle. A recent paper by Cajner, Coglianese, and Montes (2020) makes a significant contribution in our understanding of the cyclical behavior of labor force participation. Their main finding is that the LFPR is indeed affected by the state of labor market tightness, but that it responds substantially more slowly to positive or negative shocks than employment or unemployment. They further find that the Black LFPR responds substantially more strongly to a negative shock to the overall labor market. So when economic growth slows and the labor market develops slack, the rise in the Black unemployment rate relative to the white unemployment rate can be somewhat muted because of a larger labor supply response from Black workers. This means that the Black–white unemployment ratio may actually fall during recessions. Just to buttress this point, it is striking that the two lowest annual Black–white unemployment ratios on record occurred in 2009 and 2020—two of the worst years for economywide labor market health in the past 70 years or more.

As recoveries begin, Black EPOPs respond more strongly to improving economic conditions. All else equal, this should lead to a reduction in the Black–white unemployment ratio. But because the Black LFPR recovers more quickly than the white LFPR , the progress in reducing the racial unemployment gap is blocked by faster labor force growth among Black workers. By the time late in recoveries when labor markets are getting tight again, the white LFPR likely begins recovering more strongly, which allows for a reduction in the Black–white unemployment ratio. This modestly complicated series of dynamics likely explains part of why the salutary effect of lower unemployment rates on the Black–white unemployment ratio might be harder to detect in a simple eyeballing of trends. In 2019, the last business cycle peak, the Black–white unemployment ratio hit its lowest point at any business cycle peak on record. This likely reflects both a shallow but nontrivial downward trend over time and pressure that tight labor markets put on compressing the ratio. Additionally, the prolonged (if too slow) recovery following the Great Recession allowed ample time for the white LFPR to recover from the negative shock of the Great Recession and to stop putting downward pressure on the white unemployment rate.

Policy implications

The upshot of this examination is that sustained periods of high pressure in U.S. labor markets might significantly narrow racial gaps in unemployment and other key labor market measures. Given the long history of structural racism in the United States and the intentional policy efforts that created these gaps, it seems incumbent upon policymakers to use every tool available to try to close them. High-pressure labor markets look as promising as (or more promising than) any other tool. The large benefits—moral, political, and economic—of closing these labor market gaps call upon macroeconomic policymakers to consider them when assessing the benefits and costs of a “go for growth” strategy targeting high-pressure labor markets. To be explicit: The potential of more aggressive expansionary macroeconomic policy to help close race-based gaps in the labor market is worth taking on more risk of sparking inflation.

This policy recommendation for macroeconomic policymakers (including the Federal Reserve) to take on extra inflation risk in the name of narrowing racial gaps in the labor market is likely frustratingly imprecise to some. Some policymakers would prefer the clarity of, say, a numerical target for the Black unemployment rate. However, excess confidence in the ability of macroeconomic policymakers to use hard-and-fast ex ante labor market targets that precisely define “high pressure” has backfired in the past. Specifically, that unfounded confidence is a prime reason why labor markets were kept too slack for so long in recent decades, as hard targets such as estimates of the NAIRU turned out to be wrong, leading to unemployment rates in excess of what was needed for reasonable inflation control. Further, if using overall unemployment rates as precise labor market targets has proven to lead to unsatisfactory outcomes (and it has), using the Black unemployment rate as a specific target might be even worse, as one would be implicitly targeting not only the overall rate, but also how robustly the ratio between the Black and the overall rate changed as overall unemployment rose and fell depending on labor market conditions.

One of the most direct and thoughtful calls for having the Federal Reserve aim for narrower racial gaps in the labor market was by Bernstein and Jones (2020b). Their paper is often described as calling on the Fed to “target the Black unemployment rate,” but it does so only in the sense described above: It calls upon the Fed to consider the benefits of narrower gaps, and explicitly make them part of their criteria for decision-making. As the authors explain, “It is not just asking the chair to tell us about the gaps; it requires him or her to make closing them a part of their mandate” (Bernstein and Jones 2020a).

These sensible calls to narrow labor market gaps do raise an important question: Why is there reticence to demand that macroeconomic policymakers achieve a full elimination of labor market gaps? The answer is because it is unlikely that macroeconomic policy by itself can neutralize the centuries-long legacy of structural racism. This legacy has led to disadvantages Black workers face along numerous margins in the labor market, and while high-pressure labor markets can help to ameliorate these disadvantages, high-pressure labor markets likely cannot completely undo them before inflationary pressures require some moderating of expansionary policy.

For example, some of the gap in unemployment rates between Black and white workers represents differing levels of educational credentials. As we showed earlier (Figure E), adjusting the Black unemployment rate under a scenario that gives the Black and white workforces the same age and educational profiles does reduce the Black–white unemployment ratio by a small amount, around 15%. This gap in educational credentials obtained by Black and white workers is itself largely a function of historic discrimination, but it is unlikely to be solved simply by boosting aggregate demand. Further, even at the same level of educational credentials, it is certainly possible for the quality of educational investments to differ systematically between Black and white workers. Research has shown that educational investments are not only larger in white neighborhoods, but they have also been systematically reduced in schools with larger shares of Black students.8 Thus it seems likely that equalizing labor market outcomes will require interventions over and above expansionary macroeconomic policy to address the differences in education investment.

Despite these caveats, the results in this paper and previous research clearly show that expansionary macroeconomic policy can have profoundly equalizing effects. It also seems clear that policymakers have not fully accounted for these benefits when weighing benefits against the potential cost of sparking inflationary pressure. Further, ignoring these potential benefits may even result in worse analytical forecasting. Concepts like the natural rate of unemployment and the level of potential output for the U.S. economy often are estimated by assuming a given Black–white unemployment gap that does not close as the economy heats up.

An oft-cited example is the Congressional Budget Office (CBO) estimate of the natural rate of unemployment. The CBO assumes the overall unemployment rate reached in 2005 is consistent with the economy’s natural rate. It then takes group-specific unemployment rates that prevailed in 2005 and allows the overall natural rate to change only as the group-specific shares of the labor force change over time due to demography or immigration flows (Shackleton 2018). In some sense, this method implicitly assumes that group differences in unemployment that prevailed in 2005 are set in stone. Some have gone so far as to call this assumption racist. This seems wrong. The existence of the gaps is evidence of racism. But it would be odd indeed to ignore them entirely when doing forecasting given how persistent they have been. Instead, it seems that this assumption of ever-persisting gaps is evidence more of pessimism (much of it arguably well-earned) than of racism. 

But if these gaps do indeed close further as labor markets enter high-pressure periods, then any estimate of the natural rate of unemployment can be lower and estimates of potential output can be higher than one would otherwise forecast. In essence, we will never know the full extent of other policy interventions that need to be done to foster racial equity until we have fully maximized the reach of high-pressure labor markets. To put this another way, we won’t even know the size of the Black–white unemployment gap until we are sure we have reached the lowest rate of overall unemployment consistent with sustainable inflation. And yet for the vast majority of years over recent decades, we have not been close to this minimum unemployment rate.

In recent months, handwringing about the possibility of eventually “overheating” the U.S. economy due to excessively generous fiscal stimulus has begun. It is true that we are not completely certain about how low unemployment can go or how much the economy’s supply side will respond to growth in aggregate demand—so signs of overheating should indeed be monitored. But we should be very cautious about premature declarations of overheating. We have not seen sustained and broad-based wage and price inflation in the U.S. economy for decades. And we now know much more than in previous years about just how equalizing a high-pressure labor market can be, both for compressing wage growth among low-, middle-, and high-wage workers and for closing race-based gaps in labor market measures. These benefits are utterly enormous, and maximizing them is worth the risk of being very patient before aiming to deflate high-pressure labor markets through policy.

Endnotes

1. See Bivens 2016 for the central role of fiscal policy in conditioning economic growth after the Great Recession of 2008–2009.

2. See Bivens and Zipperer 2018 for some evidence of this.

3. See Mishel and Bivens 2021 for the central role of low-pressure labor markets in suppressing wage growth for most of the post-1979 period. See Gould 2020 for a broad overview of wage trends over the same period.

4. See Wilson 2015 for evidence on this.

5. While the differences between the coefficients are not statistically significant at most conventional levels, the difference in magnitude is economically large and is consistent and robust across the various regression specifications and time periods.

6. We switch to using an output gap measure for the state of the overall economy in this section because there is an arithmetic relationship between the overall unemployment rate and disaggregated measures of unemployment and employment by group. When, for example, the Black unemployment rate falls, this will by definition also reduce the measured overall unemployment rate. Our output gap measure does not have any arithmetic relationship to disaggregated labor force measures, so we use it for the rest of this paper.

7. See Hobbs and Stoop 2002 for evidence of this.

8. See Derenoncourt 2021 for evidence that as neighborhoods’ share of Black residents increased over time, public investments shifted more heavily toward policing and incarceration and white students saw higher enrollments in private schools. See Johnson 2011 for evidence that racial segregation led to lower resources for students in schools with higher shares of Black students.

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