PRB

Europe is the oldest region in the world, with almost one in five people ages 65 and older . Many European countries are concerned about the implications of this aging population, including a growing demand for old-age support and a shrinking pool of working-age people to provide it. As the urgency of the care-work crunch becomes more apparent, new research funded by the National Institute on Aging reveals that women and people without children take on a disproportionate share of this unpaid care work across the continent.

Europeans can expect to spend over half of their lives after age 15 providing unpaid family care work, including taking care of children and older relatives. However, women in Europe spend six more years doing unpaid caregiving work than European men, according to a study by Ariane Ophir, now at the Center d’Estudis Demogràfics, and Jessica Polos, now at DePaul University. ¹

Ophir and Polos estimated care life expectancy, or the number of years after age 15 people can expect to spend providing informal care, by sex in 23 European countries. ²  Data on unpaid caregiving came from the European Social Survey , and life expectancy data came from the Human Mortality Database’s abridged period life tables.

FIGURE 1. Women in Europe Spend More Years Than Men Doing Unpaid Caregiving Work, but Patterns Differ Across Countries

Total care life expectancy at age 15 in years by sex, 2004/2005

Source: Ariane Ophir and Jessica Polos, “Care Life Expectancy: Gender and Unpaid Work in the Context of Population Aging,” Population Research and Policy Review 41, no. 1 (2022): 197-227.

In the examined countries, the average care life expectancy is 33 years for men and 39 years for women, they found. And while the duration of caregiving life among men differs across countries—from 17 years in Portugal to 50 years in Norway—there is much less variability among women, reflecting how women consistently take on the primary caregiving burden, the authors explained.

By breaking down caregiving years by level of care, the authors also found that women spend significantly more time providing care at a high level, meaning daily or several times a week. In most of the examined countries, more than half of women’s caregiving years are spent on high-level care, compared to less than half of men’s. Women’s care life expectancy includes five to 10 more years of high-level caregiving than men’s in most countries, they found.

A similar gender gap in caregiving exists in the United States, according to Denys Dukhovnov of the University of California-Berkeley, Joan Ryan of the University of Pennsylvania, and Emilio Zagheni of the Max Planck Institute for Demographic Research.³ Compared to men who provide care, women spend 67% more time on average—around 50 minutes per day—providing unpaid care, their analysis found.

Using data from the American Time Use Survey and the Panel Study of Income Dynamics, Dukhovnov, Ryan, and Zagheni also showed that women in the United States spend twice as much time as men caring for young children, and that women in middle age spend slightly more time than men caring for older adults.

Both studies suggest the importance of considering the gender gap in informal caregiving when designing programs to promote more equitable work and family policies.

When counting unpaid family caregiving, older women and men in Europe can expect to work similar number of years

While women today are in the workforce longer than previous generations, they still spend fewer years employed than men in most European countries. But gender gaps in how long people work shrink or are even reversed when both paid and unpaid work are counted, a separate study by Ophir found.⁴

Ophir examined paid and unpaid working life expectancy at age 50 by sex, or the years 50-year-old women and men are expected to spend in employment and informal caregiving, including caring for grandchildren and helping older adults with daily activities. The study used data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) from 17 countries across Europe.⁵

Women’s working life expectancy is longer than men’s by up to a year in all but four countries, but the components of this work are very different for men and women, the study found. The largest component for women is years spent exclusively in unpaid work, while for men it is years spent only in paid work. Women are also expected to spend more years than men simultaneously in paid and unpaid work in most countries, compounding their caregiving burden.

Most of the years women and men care for grandchildren occur after retirement, while some of the years they spend caring for older adults happen while still employed, especially for men, the study found. While women spend more years than men providing both types of care, the gap is larger with grandchild care, possibly reflecting women’s tendency to retire earlier, Ophir says.

Though concerns over the care burden in aging societies often focus on caring for older adults, caring for grandchildren is also an important part of working life among older women, Ophir says. Debates on increasing retirement age and work-family policies should therefore incorporate an intergenerational perspective, she suggests.

The gendered pattern of caregiving years suggests that women’s “additional investment in unpaid care work in older adulthood, which conflicts with paid work and does not count toward pension benefits, could exacerbate gender inequality later in life and expose older women to additional economic disadvantages,” Ophir further explains.

Childless adults in Europe are more likely to support their older parents than adults with children

Luca Maria Pesando, now at New York University, found that adults with no children are about 20% to 40% more likely than those with children to provide financial, practical, and emotional support to their older parents, especially to mothers.⁶ Using Generations and Gender Survey (GGS) data from 11 European countries, his study examined support to older parents among adults ages 40 and older and whether having any children made a difference.⁷

Assessing the support provided to mothers and fathers separately also reveals gendered patterns. Women are more likely than men to provide support to mothers, regardless of whether they have children, Pesando found. Compared to those with children, both childless men and women are more likely to provide support to their mothers. In contrast, while childless women are more likely to provide support to their fathers, childlessness does not relate to the likelihood that men will provide support to their fathers.

The difference may reflect mothers being more socially and emotionally connected to their children than fathers, Pesando explains. Fathers are also more likely than mothers to have spouses still alive to provide support —reducing the potential burden on adult children—but the study controlled for this gender difference.

These findings are important in light of the growing share of childless adults in most European countries and concerns over the impact on demand for public support as people age. “These findings… support the view that researchers and policymakers should take into more consideration not only what childless people receive or need in old age, but also what they provide as middle-aged adults,” Pesando says.

Patterns of informal caregiving vary across countries, reflecting demographic and social characteristics

While most countries in Europe older populations compared to the rest of the world, life expectancy and fertility levels vary. Norms around gender and family responsibilities also vary, partly reflecting differences in social policies that affect gender equality and care provision. All three studies conducted in Europe show variations in their findings across countries, in part due to their unique demographic profiles, norms, and policies.

Ophir and colleagues show that while the care life expectancy does not vary substantially across countries, the proportion of years spent providing high-level care differs. In Nordic countries such as Denmark and Sweden, women and men have longer care life expectancies but spend a smaller share of this time providing high-level care; they also have smaller gender gaps in caregiving. These countries have more egalitarian gender ideologies than other European countries and more generous welfare regimes that include family caregiving, the researchers say. They are also similar across some demographic factors, such as total fertility rate, age at first birth, life expectancy, and healthy life expectancy, they note.

In countries in Southern Europe, such as Greece, and some Central and Eastern European countries, such as Slovakia, care life expectancies are shorter but involve greater shares of high-level caregiving. These countries rely more on families to take on primary caregiving responsibilities, the researchers note. They do not, however, share similar demographic profiles, suggesting the importance of social contexts in addition to demographic factors in shaping the nature of care life expectancy, they add.

In her analysis examining both unpaid and paid work, Ophir also finds variation across countries in the intensity of care. For example, while the overall working life expectancy is the longest for Swedish adults, most of their unpaid work was low intensity, reflecting the country’s generous welfare regime. While the overall working life expectancy is relatively shorter in Greece, Italy, and Poland, most of the unpaid work for women involves higher-level caregiving.

Pesando finds that adults are less likely to care for their older parents in Northern Europe, where comprehensive publicly funded programs can provide this care. Though differences are not large among countries in Eastern and Western Europe, adults are most likely to support older parents in Russia, followed by Czechia. Both countries are former socialist welfare states with heavy reliance on family support and limited publicly funded services for older adults, he notes.

Despite concerns over the economic implications of population aging and the labor force participation of older adults, informal caregiving has received little attention in policy debates. The disproportionate burden that falls on women and adults without children is therefore largely unnoticed. Discussions of aging-related policies, including pension reforms, old-age entitlements, and changes in the retirement age, should be informed by patterns in informal caregiving. Addressing informal caregiving also helps promote gender equality, especially in later life.

Having been on the forefront of Manifest Destiny, the Gold Rush, and post-World War II urban sprawl, the Southwest has had a long history of exponential growth, innovation, and development. But is this the case across the entire region?  

Here, we present a tale of two states—Arizona and New Mexico—and break down five reasons why the actual story is more nuanced than it seems. 

1. Their populations are not growing at the same rate. Compared to the nation as a whole, which grew by roughly 7% over the decade, New Mexico’s population growth was below average (3%), while Arizona’s was above average (12%). This difference is not explained by fertility rates in Arizona and New Mexico. Nor is it explained by mortality rates; despite New Mexico having a higher age-adjusted mortality rate than Arizona between 2010-2020, the difference is not impactful. It boils down to migration, especially of people moving from other, often neighboring, states. Heading into 2020, Arizona had a net migration gain of almost 600,000 new residents, while New Mexico had a net loss of about 40,000 people.

2. Metropolitan counties are booming, especially in Arizona. Growth in metropolitan counties drove population gains in Arizona and New Mexico from 2010 to 2020. And while most of the population in both states resides in metropolitan counties, the share is much higher in Arizona (Figure 1). This is partly due to the more urbanized landscape of the state: More than half of Arizona’s counties are classified as metropolitan, compared to less than 1 in 5 counties in New Mexico.  

Figure 1. 95% of Arizonans Live in Metropolitan Counties, Compared to 67% of New Mexico Residents

Percent of total state population, by county-type of residence

Sources: U.S. Census Bureau, 2020 Census Redistricting Data (Public Law 94-171); USDA Economic Research Service, 2013 Urban Influence Codes.

In fact, more people live in Arizona’s metro counties than in the entire state of New Mexico. The two largest counties in Arizona are each home to over 1 million people, while the largest in New Mexico has under 700,000. While Bernalillo County is home to 1 in 3 New Mexico residents, Arizona’s Maricopa County has over six times as many people (Figure 2).  

Figure 2. Across Both States, the Highest Population Concentration is in and Around Maricopa County, Arizona

Population density by county for Arizona and New Mexico, 2020

Source: U.S. Census Bureau, 2020 Census Redistricting Data (Public Law 94-171).

Migration into Maricopa County and surrounding counties has driven much of Arizona’s population growth. Meanwhile, most New Mexico counties saw negative net migration; 70% of the metro counties that grew experienced negative net migration, meaning the slight growth that they witnessed can largely be attributed to their birth and mortality ratios. Where New Mexico did see migration gains, the increase was likely due in part to job growth in the oil industry, which may not be sustainable over time.  

Figure 3. Maricopa County Accounts for the Large Majority of Migration Growth in the Area, With Net Migration More Than 6 Times the Next Highest County

Net migration in Arizona and New Mexico, by county, 2010-2019

Source: PRB U.S. Indicators: Net Migration (2010-19).

3. Metropolitan Arizona has an abundance of business and employment opportunities. Arizona boasts one of the fastest-growing economies in the country. Over the past half-decade, the state has consistently witnessed job, income, and sales growth above the national average, with Maricopa County experiencing significant expansions in sectors such as health care, information, construction, and accommodation and food services. Home to Phoenix and its multitude of edge cities, the county was the most populous and fastest-growing in the state from 2010 to 2020, witnessing a 16% jump in its population. New business and job growth, particularly in the tech industry, have earned the area the nickname “Silicon Desert”, reflecting its status as a prosperous, pro-business environment supportive of start-ups with a healthy job market that promotes in-migration but without the high cost of living of California’s Silicon Valley.  

4. New Mexico’s rural settings and struggling economic and education sectors are pushing people to leave. While New Mexico and Arizona rank similarly on quality of life indicators comparing cost-of-living, labor, inequality, life expectancy, and education characteristics, New Mexico lags a bit behind, mostly due to shorter life expectancy and lower rates of college degree attainment. Concerns about the quality of the K-12 education system may contribute to some of New Mexico’s out-migration, as families with children may choose to relocate to neighboring states for better schools. New Mexico scored among the 10 lowest ranking states on measures of fourth and eight-grade math and reading proficiency for the entirety of the 2010 to 2020 period.  

Differences in the states’ economic approaches and opportunities may also help explain the slow growth in New Mexico. While Arizona has largely focused on growing private markets and promoting entrepreneurship, New Mexico has concentrated more resources on public spending. While Arizona regularly ranked among the top 10 states for total job growth, New Mexico frequently ranked among the bottom 10 from 2010-2020. Low job growth combined with a lack of urban settings that appeal to young adults has resulted in out-migration of working-age people to surrounding states such as Arizona, Nevada, Oklahoma, and Texas in search of city life and better job opportunities.  

5. The future for the states presents different challenges. While job growth and the entrepreneurial spirit in Arizona may have their appeal, the state’s population growth is perpetuating increasingly urgent concerns about water availability amidst extensive residential development. Despite the current megadrought depleting the Colorado River—the primary source of water Arizona and all the states surrounding it—development continues without slowing. And while municipalities within Arizona are turning to other sources of water, such as groundwater and reservoirs, to continue accommodating population growth, these alternatives come with their own political complications and are finite. As the population grows and the water supply dwindles, Arizona is walking the limits on growth.

Meanwhile the out-migration of working-age adults and declining population of people under the age of 18 means New Mexico’s population is aging, which raises concern for further economic and quality of life consequences. Providing accommodations for a growing older adult population (such as healthcare, caregiving services, and accessibility modifications) and coping with a shrinking workforce puts pressure on the state’s economy. But recent trends, such as the rise in remote work, could present the opportunity to retain younger workers.  

Early adulthood is often thought of as an exciting time, marked by increased independence and new opportunities. As they enter their 20s, young people are often encouraged to enjoy the so-called best years of their lives. Yet, this stage can also be fraught with increased uncertainty and responsibility. especially for those navigating the transitions of young adulthood in a global pandemic, a new PRB analysis shows.

PRB analyzed data from spring 2020 through fall 2022 using the U.S. Census Bureau’s Household Pulse Survey to understand the anxiety of young adults (which we defined as people ages 18 to 29) relative to older adults (ages 60 and older). We found that more than 40% of young adults reported symptoms of anxiety—such as feeling nervous, anxious, or on edge—more days than not during the coronavirus pandemic.

These findings may not come as a surprise, given the events of the past three years: a global pandemic, record job losses during COVID-19 shutdowns, an attack on the U.S. Capitol, widespread demonstrations and global attention addressing systemic racism and police brutality, and the steepest year-over-year increase in consumer prices in 40 years.

What is surprising is that amidst these events, and despite facing greater health risks from COVID-19, older adults maintained much lower levels of anxiety than young adults during the pandemic. In fact, the anxiety age gap grew even as vaccines became available, restrictions were lifted, and the impacts of the pandemic on health, education, social relationships, and employment began to subside (Figure 1).

Figure 1. Young Adults Were the Most Anxious Group Throughout the COVID-19 Pandemic

Anxiety rates by age group, early and late pandemic period

Note: Early pandemic covers the period from April 23, 2020, to March 29, 2021, and late pandemic covers the period from April 27, 2022, to October 17, 2022. The Early Pandemic period reflects the period before vaccines were broadly available for COVID-19 while the Late Pandemic period reflects the period beginning one year after vaccine access began.

Source: PRB analysis of data from the U.S. Census Bureau’s Household Pulse Survey.

Here is what we know about the growing anxiety age gap during the COVID-19 pandemic:

1. Anxiety rates dropped more for older adults than young adults—though young adults faced lower health risks.

Compared with young adults, older adults are much more likely to experience serious health issues from COVID-19 infections, and adults ages 65 to 74 have a COVID-19 death rate that is 60 times higher than the rate for young adults. Yet, as the pandemic progressed, the share of older adults reporting anxiety fell by 6 percentage points (from 22% to 16%), while anxiety rates for young adults decreased by 2 percentage points (from 43% to 41%).

2. Young adults were more anxious than older adults before the pandemic.

Recent cohorts of young adults have reported more clinical mental health symptoms than previous generations during the same life stage, a trend that extends back to the 1930s. Ahead of the pandemic, young adult anxiety was already rising, while older adult anxiety was on the decline.

Researchers have provided several explanations for this anxiety gap. Young adults may have different emotional responses to stressors than older adults, and older adults may be more likely to have received treatment for anxiety, resulting in fewer symptoms, or less likely to report their symptoms. Additionally, among young adults, addictive use of social media and growing concern about climate change and its impact on their futures have been linked to increased depression, anxiety, and stress among young adults.

3. The anxiety age gap grew for all racial and ethnic groups during the pandemic, but especially for Black adults.

The anxiety gap between Black young adults and Black older adults increased by 9 percentage points between April 2020 and October 2022. Black adults ages 18 to 29 saw a significant increase in anxiety (+3 percentage points), those 60 and older saw anxiety drop significantly (-7 percentage points).¹

While the size of the gap grew most for Black adults, white non-Hispanic adults had the largest anxiety age gap overall at more than 25 percentage points. In fact, white young adults were significantly more anxious than their non-white peers, while white older adults were significantly less anxious than their non-white peers.

Figure 2. The Anxiety Age Gap Was Largest for White Adults, but Black Adults Saw the Gap Increase Most

Size of gap in anxiety rates by age group and racial/ethnic groups during the pandemic

Notes: Young adults refers to adults ages 18 to 29 while older adults refers to those ages 60 and older. Early pandemic covers the period from April 23, 2020, to March 29, 2021, and late pandemic covers the period from April 27, 2022, to October 17, 2022. The asterisk (*) in racial/ethnic categories denotes non-Hispanic.

Source: PRB analysis of data from the U.S. Census Bureau’s Household Pulse Survey.

4. Economic uncertainty alone does not explain the growing anxiety age gap.

Prior to the pandemic, many young adults were already worried about accessing and paying for health care, housing and food security, student loans, and personal debt. Young adults also have lower incomes, on average, compared with older adults—most of whom receive Social Security benefits. And they were particularly impacted by economic upheaval during the pandemic, especially those working in hospitality, leisure, and retail.  

However, using the Household Pulse Survey, we found that the share of young adults living in lower-income households (making less than $25,000 a year) decreased during the pandemic, dropping from 26% to 19% (Figure 3). Meanwhile, the share of older adults living in low-income households increased slightly.

While this may be partially explained by more young adults living with parents during the pandemic, we found similar patterns for job and housing insecurity; young adults’ economic well-being improved relative to older adults over the period examined, yet their anxiety rates did not fall in proportion to these improvements.

Figure 3. The Share of Young Adults Living in Lower-Income Households Declined During the COVID-19 Pandemic

Percent of persons living in lower-income households by age group and period

Note: Lower-income refers to persons living in households with incomes below $25,000. Early pandemic covers the period from April 23, 2020, to March 29, 2021, and Late pandemic covers the period from April 27, 2022, to October 17, 2022.

Source: PRB analysis of data from the U.S. Census Bureau’s Household Pulse Survey.

5. The pandemic uniquely affected areas of life young adults were already more worried about.

Because young adulthood is a period defined by personal, professional, and educational transitions, the pandemic’s impact on the economy, education systems, and opportunities for social interaction uniquely affected people in this age group. Pandemic conditions such as lockdowns, social distancing, shifts to virtual schooling, and restrictions on travel, intensified these areas of stress and worry that young adults were experiencing before the health crisis occurred.

Young adults were more likely to report that COVID-19 made it feel impossible for them to plan for their future, that their plans had been disrupted, and that their close relationships were negatively impacted. They were also more worried about issues unrelated to the pandemic that occurred during this period, including political elections, changes to abortion laws, rising suicide rates, and increased media reporting of sexual assault cases. Relative to older adults, more young adults report a desire to stay informed, but that following the news increased their stress and worry. While the relative health risks of the pandemic were lower for young adults, disruptions to the milestones associated with young adulthood made this age group particularly vulnerable to the mental health tolls of the pandemic. While recent media have emphasized the mental health crisis affecting teens, less has been reported about young adults’ psychological well-being.  More research is needed to determine the lasting impacts of pandemic disruptions on the mental health of those who entered and navigated the so-called best years of their lives during this period of global uncertainty.”

Note

¹ Statistically significant at <0.0001.

Children in households that receive federal rental assistance are healthier and miss less school due to illness than those whose households are waiting for help, new research demonstrates.¹

However, up to 75% of renters who need federal housing assistance—including public housing or rental vouchers—don’t receive it, data show. Most households that get assistance have incomes below the federal poverty line ($30,000 for a family of four in 2023) and a sizeable share live in deep poverty.²

Families receive rental assistance in three main forms:

• Public housing units in buildings owned and run by local public housing agencies.
• Vouchers that cover a portion of the cost for families to rent from a private owner.
• Section 8 Project-Based Rental Assistance that subsidizes affordable units maintained by private owners.

But common waitlist processes for housing vouchers inadvertently favor more stable families, compounding the disadvantage for those facing the greatest hardships, another new study shows.³

Public housing buildings have been widely criticized for isolating disadvantaged families, and media have focused on disrepair. But for families struggling to pay rent and facing the threat of eviction, “receiving housing assistance is like winning the golden ticket,” says Andrew Fenelon, a sociologist and demographer at Penn State University.

Rental Assistance Tied to Better Child Health and Fewer Missed School Days

Children in households with rental assistance had fewer health problems and missed 22% fewer school days for illness compared with children whose households were waiting for assistance, Fenelon and colleagues found.

His research team examined the impact of rental assistance by comparing children in households receiving assistance with those in households that would receive it within two years. They used the National Health Interview Survey (NHIS), a nationally representative dataset, linked with administrative records on housing assistance from the U.S. Department of Housing and Urban Development (HUD) for 1999 to 2014.

Fenelon suspects that the stability rental assistance offers families helps them better manage asthma—a major health reason children miss school. In an earlier study of NHIS and HUD data, Fenlon and colleagues found that children in households with rental assistance were less likely to go to the emergency room for a recent asthma attack than those in households awaiting assistance.⁴

Children in public housing tend to have more health problems than their peers who live elsewhere, including more frequent diarrhea, headaches, skin allergies, and asthma. But public housing does not make kids sicker, Fenelon found in another analysis of NHIS and HUD data.⁵

Investment in affordable and stable housing can boost school attendance by promoting better health, Fenelon says. Healthier kids do better in school and stay in school longer, creating long-term socioeconomic benefits, Fenelon argues.

“Disadvantaged families in public housing tend to have many challenges, but the inability to access stable and affordable housing is not one of them,” Fenelon says. With a reduced rent burden, families can invest more in their children, and with more stability they can better manage their children’s health care needs, he argues.

Rental Assistance May Create Lasting Health Benefits, Improve Child Behavior

In a separate study, receiving a housing voucher during childhood was strongly linked to lower hospitalization rates and less inpatient spending during young adulthood.⁶

Craig Evan Pollack of Johns Hopkins University and team analyzed data from the Moving to Opportunity (MTO) program, which tracked more than 4,600 families in five cities receiving either a traditional housing voucher, a voucher that could only be used in a low-poverty neighborhood, or no assistance between 1994 and 1998. Families in the two voucher groups lived in neighborhoods with similar poverty levels, and the program followed up with them 11 to 21 years later using hospital discharge and Medicaid data.

Looking at the reasons for hospital admissions, Pollack and colleagues found that children whose families received vouchers had significantly lower admission rates for asthma and mental health disorders compared to the control group.⁷ In contrast, there was no difference in emergency department use between the two groups.⁸ The findings suggest that housing policies that reduce childhood exposure to neighborhood poverty can reduce health care use into adulthood.

Another study using MTO data linked receiving a housing voucher to improved mental health among girls and fewer behavioral problems among boys when families moved to neighborhoods with less social disorder, Nicole M. Schmidt of the University of Minnesota and colleagues show.⁹ Social disorder was defined as public drinking, loitering, and police not coming when called.

By contrast, children were more likely to engage in delinquent behaviors in families that experienced housing hardship, reports Sarah Gold of Princeton University. Her findings are based on an analysis of the Future of Families and Child Wellbeing Study, which followed nearly 5,000 children born in large U.S. cities from 1998 to 2000.¹⁰

Housing hardship includes eviction, moving in with another household, homelessness, being unable to pay the rent or mortgage, and frequent moves. More severe or longer periods of housing hardship were associated with increased delinquent behavior such as vandalism, drug use, and assault. Delinquent behavior can lead to school suspension or expulsion, with lasting ramifications, Gold noted.

The link between housing hardship and delinquent behavior was the same for children in families with and without low incomes, suggesting that parental stress plays a role and that housing assistance may reduce stress.

“Children living in households with housing hardship may experience greater levels of family stress, which is linked to increased parental psychological stress and changes in parenting, which can lead to problematic behaviors in children,” writes Gold.

“Not only do children experience stress through their parents, but experiencing their own stress is linked to impulsivity, withdrawal, and aggression, all of which are associated with subsequent delinquent behavior,” she explains.

As Americans age, their happiness and life satisfaction tend to follow a U-shaped pattern, research shows. On average, people in the United States are happiest and most satisfied with their lives when they’re young, experience a decline in both metrics in their 40s (often called a midlife crisis), and then rebound in their 60s.

But what happens after age 65? Do spirits stay high in later life? How is happiness affected by events that happen as people age—like the onset of disabling health conditions or chronic pain, or the deaths of partners and friends?

Findings are mixed and researchers disagree; it depends on how, when, and to whom you ask these questions. “It’s a very heated area of study,” says Anthony Bardo of the University of Kentucky.

Research by Bardo and Scott Lynch of Duke University shows that the cognitive impairment than can accompany aging does not preclude happiness and a high quality of life. But other studies find that satisfaction with life and positive emotions decline with mobility problems and the deaths of spouses and other loved ones.

Despite puzzling society-wide patterns, research offers clues on how individuals might buffer their losses and buoy their spirits as they age, including staying involved in meaningful activities and maintaining a positive outlook. But more research is needed to confirm whether these actions can make and keep us happy or whether happy people are just more likely to do them.

Older People Report Happiness Despite Cognitive Impairment

Older adults can be happy and have a high quality of life despite experiencing some cognitive impairment, Bardo and Lynch show.¹

They analyzed data for 1998 to 2014 from the nationally representative Health and Retirement Study. The study incorporated tests that examined participants’ ability to recall words and count backwards, among other tasks. It gauged happiness by asking whether respondents were happy all or most of the time or some or none of the time in the past week.

“This is a simple yet valid and reliable measure that is commonly used to assess how one feels about her or his overall quality of life,” Bardo says. If respondents needed a proxy to respond for them, the researchers categorized them as unhappy because the proxy version of the survey did not include questions about happiness.

On average, 65-year-olds can expect five out of 18 total years of remaining life to be lived with some cognitive impairment, the study found. Of those five years with cognitive impairment, the average person will live 4.4 years happy and about seven months (0.8 years) unhappy.

“Our findings show that happiness and cognitive impairment do coexist. Happy years of life were shown to substantially exceed the number of years one can expect to live with some cognitive impairment, on average,” Bardo reports.

The study’s main takeaway is that “even when cognitive impairment does occur, older adults can expect a large proportion of those remaining years to be happy ones,” Bardo says.

“People are frightened by the idea of dementia,” he points out. “Some cognitive decline is a normal process. Ideally, these findings will contribute toward reducing some of the stigma and fear.”

Programs that enable older adults with some cognitive decline to remain in their own homes, where most older people prefer to live, may add to their happiness and quality of life, Bardo suggests.

He also notes that we don’t yet know “how to assess the happiness or quality of life of someone with severe cognitive impairment. It’s an issue of great moral and ethical concern.”

Health Issues, Death of a Spouse Dampen Satisfaction With Life

Another study shows that health problems and losing spouses make people less satisfied with life as they age.

Péter Hudomiet, Michael D. Hurd, and Susann Rohwedder are RAND researchers affiliated with the National Bureau of Economic Research (NBER). They analyzed respondents’ reports of life satisfaction from the Health and Retirement Study from 2008 to 2016.²

“When we looked at cross-sectional data that captures a group of people at one point in time, then life satisfaction did indeed increase between ages 65 and 71 and hold steady thereafter, similar to earlier studies,” Rohwedder explains.

“But when we examined a group of individuals tracked over multiple years, we find their life satisfaction tends to fall as they age, and the rate of decline accelerates. Losing a spouse and deteriorating health play important roles in the growing dissatisfaction,” she adds.

Figure 1. Life Satisfaction After Age 65 Declines When Researchers Track the Same Group Over Time

Estimated Trajectories of Life Satisfaction Ascertained by Cross-Sectional and Longitudinal Methods

Source: Péter Hudomiet, Michael D. Hurd, and Susann Rohwedder, “The Age Profile of Life Satisfaction After Age 65 in the U.S.,” Journal of Economic Behavior & Organization 189 (2021): 431-42.

Note: The cross-sectional line shows average life satisfaction in the full sample. The longitudinal line is restricted to observations with valid reports in two adjacent survey waves; and the 2-year changes are tied together starting from the average level observed at age 65.

People with low life satisfaction die younger and thus make up a shrinking share of the older population, the researchers note—making drawing conclusions from data collected at a single point in time challenging.

“Mortality is substantially higher among those who tell interviewers that they are less satisfied with their lives compared to those who are more satisfied with their lives,” Rohwedder notes. “In addition, older people with physical or cognitive impairments are less likely to fill out a survey.”

The research team suggests their findings offer a more realistic perspective on the well-being and resilience of older people. “Without these findings, policymakers balancing the needs of the older population with those of the younger population may incorrectly conclude that older people are more satisfied with their lives than they really are and are of lesser concern,” Rohwedder explains.

Mobility Problems Take a Greater Toll on Well-Being Than Advancing Age

In later life, lower-body impairments may play a greater role than age in determining life satisfaction and emotional and physical well-being, a study led by Vicki Freedman at the University of Michigan finds.³

The research team challenges the notion of the U-shaped well-being curve—highest at youngest and oldest ages—by exploring multiple measures of well-being and considering the interplay of age and lower body limitations.

Their study analyzed 2013 disability and time-use data from the nationally representative Panel Study of Income Dynamics—a different data set than the Bardo and Lynch and NBER studies. These data, based on 1,600 adults ages 60 and older, include participants’ reporting of overall life satisfaction and their experienced well-being, or how they felt while doing certain tasks.

Life satisfaction was measured with the question, asked at one point in time, “Taking all things together, how satisfied are you with your life these days?” For well-being, respondents used daily diaries to record their emotions (happy, calm, frustrated, worried, or sad) and their pain and energy levels while doing randomly selected activities. They also reported lower body limitations—problems with hip, leg, knee, or foot movements.

The researchers found that overall life satisfaction was higher for individuals ages 65 to 74 than those ages 60 to 64, but they observed no age differences in the experienced well-being measures (mood, pain, and fatigue).

“What surprised us is that lower body limitations mattered much more than age in determining all three measures of well-being,” explains Freedman, “and this finding held across age groups.”

People ages 65 to 74 with mobility problems reported the highest pain and fatigue levels. According to Freedman, there may be an initial mismatch for people in this age range between activities and abilities (in other words, people overexert themselves), resulting in more pain and fatigue.

Research Offers Clues on Keeping Spirits High Despite Limitations and Losses

Older adults report they are happiest and most satisfied with their lives while socializing, working, volunteering, and exercising, a research team led by Jacqui Smith of the University of Michigan showed.⁴ Their 2014 study examined daily diaries of 4,600 participants in the Health and Retirement Study who recorded the amount of time they spent doing specific activities the previous day, the feelings they experienced, and the intensity of those feelings.

The researchers found that participants spent an average of 3.6 hours a day viewing television, an activity that some people experienced positively and others experienced extremely negatively. Television is passive, Smith notes, while activities that involve more social, mental, and physical engagement contribute the most to the positive side of adults’ daily emotional balance sheets, she says.

Finding ways to enable older people with disabilities to be involved in physical activity and volunteering could improve their well-being and satisfaction with their lives, Freedman argues. She points to research showing that older adults who make accommodations that allow them to carry out daily activities without assistance or difficulty—such as using a walker or taking public transportation instead of driving—report emotional well-being at levels close to those who don’t need accommodations.

Having adequate income may help some people adapt to their limitations, buffering the negative impact of impairment on their emotional well-being, another study led by Freedman shows.⁵

The loss of spouses and other family members presents different, more complicated challenges to happiness and life satisfaction. In Bardo’s view, humans tend to be resilient. He points to a body of research showing that as people reach older ages, they shed things in their lives that make them unhappy and accentuate the positive.

Bardo’s own research finds that as people move into their 70s and beyond, their family becomes a less important component of day-to-day happiness, and other aspects of life, such as health, friends and acquaintances, place of residence, and hobbies play a bigger role.⁶

This process of shifting focus and accentuating the positive “may largely explain why Americans, on average, become happier with age,” Bardo argues.

However, Rohwedder cautions that future research will need to confirm whether activities associated with greater life satisfaction and other measures of well-being actually cause this effect. “If the least happy die or drop out of the survey at greater rates, as our study documents, then individuals will appear happier at advanced ages, but they did not become happier.”

This article reflects research supported by the National Institute on Aging of the National Institutes of Health at the Centers on the Demography and Economics of Aging and Centers on the Demography and Economics of Alzheimer’s Disease/Alzheimer’s Disease and Related Dementias. Findings from researchers affiliated with the following centers are highlighted: Center for Population Health and Aging, Duke University; NBER Center for Aging and Health Research, National Bureau of Economic Research; and Michigan Center on the Demography of Aging, University of Michigan. Lindsey Piercy of the University of Kentucky contributed to this piece.

References

1. Anthony R. Bardo and Scott M. Lynch, “Cognitively Intact and Happy Life Expectancy in the United States,” The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 76, no. 2 (2021): 242-51.

2. Péter Hudomiet, Michael D. Hurd, and Susann Rohwedder, “The Age Profile of Life Satisfaction After Age 65 in the U.S.,” Journal of Economic Behavior & Organization 189 (2021): 431-42.

3. Vicki A. Freedman et al., “Aging, Mobility Impairments and Subjective Wellbeing,” Disability and Health Journal 10, no. 4 (2017): 525-31.

4. Jacqui Smith et al., “Snapshots of Mixtures of Affective Experiences in a Day: Findings From the Health and Retirement Study,” Population Ageing 7, no. 1 (2014): 55-79.

5. Vicki A. Freedman et al., “Late Life Disability and Experienced Wellbeing: Are Economic Resources a Buffer?” Disability and Health Journal 12, no. 3 (2019): 481-8.

6. Anthony R. Bardo, “A Life Course Model for a Domains-of-Life Approach to Happiness: Evidence from the United States,” Advances in Life Course Research 33 (2017): 11-22.

U.S. Black women are over three times more likely to die in pregnancy or postpartum than white women.

In the United States, non-Hispanic Black women were 3.5 times more likely to die during pregnancy or shortly after childbirth than non-Hispanic white women in 2016 and 2017. Ending Black maternal mortality in America involves addressing structural racism—that is, those aspects of social, political, economic, and health care systems that reinforce inequity, researchers say.

Cardiovascular disorders like preeclampsia are far more likely to kill Black women.

Black-white disparities in maternal death are concentrated among a few causes, research shows (see figure). Black women were five times more likely to die from postpartum cardiomyopathy (a form of heart failure) and preeclampsia and eclampsia (blood pressure disorders) than white women, according to the 2016-2017 data.

Pregnant and postpartum Black women were also more than two times more likely than white women to die of hemorrhage (severe bleeding) or embolism (blood vessel blockage).

FIGURE. Preeclampsia/Eclampsia Is the Leading Cause of Black Maternal Death

U.S. maternal deaths per 100,000 live births for five leading causes of maternal death by race/ethnicity, 2016-2017

Even with more education and income, Black women are more likely to die or suffer severe childbirth complications.

In New York City, non-Hispanic Black women were eight times more likely to die of pregnancy-related causes than non-Hispanic white women between 2011 and 2015, a study by the New York City Health Department showed.

While women with more education, income, and wealth tend to fare better in pregnancy, college-educated Black women in New York City were more than twice as likely to suffer severe complications from childbirth than white women without a high school diploma from 2008 to 2012, another Health Department study documents.

In California, Black mothers with the highest incomes were more likely to have severe childbirth complications than white mothers with the lowest incomes, finds a study of data from 2007 to 2016.

Most pregnancy-related deaths in the United States are preventable.

The Centers for Disease Control and Prevention determined that 84% of all maternal deaths from 2017 to 2019 were preventable, in a study using data from 36 states.

Early diagnosis and treatment of preeclampsia, eclampsia, embolism, and cardiomyopathy are crucial, given the outsize role of these disorders in Black maternal death, says Marie Thoma, a reproductive and perinatal epidemiologist and population health scientist at the University of Maryland. “We need new models of care before, during, and after birth to address these inequities,” she says.

In 2021, COVID-19 led to dramatic increases in maternal death for all U.S. women. The maternal mortality rate for Black women that year—69.9 deaths per 100,000 live births—was 2.6 times the rate for white women (26.6).

Preventing racial inequities means questioning systems, policies, and social structures.

Addressing the stark racial inequities in maternal death requires fundamentally reorienting the current approach to health care, argues Rachel Hardeman, Director of the Center for Antiracism Research for Health Equity at the University of Minnesota. Research and action must be grounded in the understanding that racism is at the root of health inequities, she says.

“We must first ask, ‘How do systems, policies, and social structures combine to create the conditions for poor health?’ rather than asking, ‘What’s wrong with people of color that makes them die younger and at higher rates and suffer more illnesses?’”

The environments where people are born, live, learn, work, play, and seek health care take a collective toll on their health, a wide body of evidence shows.

“The lived experience of racism gets under the skin,” says Paris “Dr. AJ” Adkins-Jackson, Assistant Professor of Epidemiology and Sociomedical Sciences at Columbia University.

The story of CDC epidemiologist Dr. Shalon Irving, who consulted multiple providers but died from blood pressure complications three weeks after childbirth, shows us the devastating effects of the gaslighting that often occurs when individuals who are racialized as Black interact with the U.S. health care system, Dr. AJ explains.

“These stories and data show us that it’s not about the implicit bias of one health care provider, but a systemic problem that requires upstream intervention and solutions,” she says.

For More Information

These PRB resources provide context on maternal health in the United States.

No census is perfect, and 2020 was no exception. Evaluations of the 2020 Census found significant undercounts or overcounts for 14 states and certain demographic groups—including young children and people identifying as Black, Asian, American Indian and Alaska Native, and Latino. In fact, the 2020 Census proved less accurate than the 2010 Census, in which no states had significant overcounts or undercounts and coverage errors for most racial and ethnic subgroups were smaller. Errors in the 2020 data will have lasting repercussions throughout the next decade, with potentially serious consequences for underrepresented groups.

Census data are vital to our democracy. Data from the 2020 Census have already been used to determine the number of representatives each state sends to Congress through 2030 (a process called apportionment) and to help define new legislative districts for the November midterms and for all elections through 2030 (a process called redistricting). Each year, census data are also used to allocate more than one trillion dollars in federal funding for important projects and services that benefit local communities.¹  Given the critical uses of census data, stakeholders need to know how accuracy and data quality are evaluated, and how the 2020 Census stacks up according to those measures.

How Is Census Accuracy Measured?

To gauge the accuracy of the census, the U.S. Census Bureau first produces independent estimates of the size and characteristics of the U.S. population using two methods—Demographic Analysis and the Post-Enumeration Survey. These estimates are then compared to census data to identify discrepancies. Demographic Analysis (DA) estimates the population at the national level only using historical population data, birth and death records, Medicare enrollment records, and estimates of international migration.² The Post-Enumeration Survey (PES) estimates the population at both the state and national levels using a sample survey independent of the census and conducted after its conclusion.³

Coverage is the term used to describe how close a census gets to a complete and accurate count of everyone in the nation. Net coverage error is a key measure of census accuracy that represents the balance between the number of people who were correctly counted and the number who were missed, double-counted, or included when they should not have been, calculated by comparing DA and PES estimates to census data.⁴ A positive net coverage error indicates an overcount of a particular geographic area or population group, while a negative net coverage error indicates an undercount.

How Accurate Was the 2020 Census at the National Level?

Despite unprecedented challenges—including a global pandemic, several natural disasters, political interference, and budgetary uncertainty during the planning process—the 2020 Census did not have either a significant undercount or overcount of the total population, based on results from the PES. The PES estimate of net coverage error was -0.24%, which is not statistically different from zero (see Table 1). In 2010, the PES estimated a very slight overcount of the total population (0.01%), but again, this estimate is not statistically significant. The DA estimate of net coverage error (-0.35%) also showed a slight undercount of the total population in 2020, compared with an estimate of a slight overcount (0.13%) in 2010 (see Table 1).

Table 1. Percentage Net Coverage Error for U.S. Population

Notes: *Denotes a net coverage error that is significantly different from zero. DA includes total population; PES includes household population only (excludes Group Quarters and Remote Alaska). Net coverage error for DA is from the Middle Series.

Sources: U.S. Census Bureau, 2020 Post Enumeration Survey Report, PES20-G-01, March 2022; U.S. Census Bureau, Demographic Analysis Tables, Table 3; U.S. Census Bureau, Presentation: Post Enumeration Survey and Demographic Analysis, March 2022; U. S. Census Bureau, “Census Bureau Releases Estimates of Undercount and Overcount in the 2020 Census,” March 2022.

While the absence of significant coverage error for the total population in 2020 is good news, the bad news is that it masks significant coverage errors for some age groups, some race and Hispanic origin groups, and both homeowners and renters.

Children Were Undercounted More Than Any Other Age Group

Both DA and the PES revealed that the 2020 Census significantly undercounted young children ages 0 to 4, with net coverage error estimates of -5.4% and -2.79% respectively (see Table 1). According to DA estimates, children ages 0 to 4 are the only age group for whom census coverage has decreased each decade since 1980, and they also had the largest estimated undercount rate in 2020.⁵

Undercounts of young children matter because they result in communities not receiving their fair share of federal resources to support programs such as Head Start and the Supplemental Nutrition Assistance Program (SNAP), which provide important services for children and families with low incomes. Undercounts also prevent local policymakers from having accurate information to develop long-term plans for schools and other services.

To reduce the future undercount of young children, the Census Bureau recently formed a new internal working group of subject matter experts focused on improving data for this age group. They are expanding prior research to better understand why young children are more likely to be missed and looking for ways to improve data collection for this age group by changing the instructions, probes, and questions used to create household rosters.⁶

Analysis also revealed issues in counting other age groups. Both DA and the PES found undercounts of children under age 18 and men ages 30 to 49 (see Table 1). While the PES found significant undercounts of both males and females ages 18 to 29, DA found slight overcounts. This difference in coverage estimates for young adults may be due in part to differences in the populations covered by the two approaches; a substantial share of people ages 18 to 29 live in college dorms, which are included in DA but not in the PES sample. The PES estimate of net coverage error for women ages 30 to 49 was not significantly different from zero, while the DA estimate showed a slight undercount. Both DA and the PES found substantial overcounts of people ages 50 and older, especially women. In some cases, older adults may have been counted twice because they had more than one residence—such as a vacation home—and were counted in both locations.

Historically Undercounted Groups Were Underrepresented—Again

The 2020 Census continued to undercount groups who have been historically undercounted—Blacks, American Indians and Alaska Natives, Latinos, and those who reported being of Some Other Race—while significantly overcounting the non-Hispanic White and Asian populations, according to PES estimates (see Table 2). DA coverage estimates for race and Hispanic origin characteristics are not yet available.

Table 2. PES Percent Net Coverage Error by Race and Hispanic Origin: 2010 and 2020

Note: *Net coverage error is significantly different from zero.

Sources: U.S. Census Bureau, 2020 Post Enumeration Survey Report, PES20-G-01, March 2022; U.S. Census Bureau, Presentation: Post Enumeration Survey and Demographic Analysis, March 2022.

The magnitude of the net coverage errors increased significantly between 2010 and 2020 for the non-Hispanic White alone, Asian, and Latino populations, as well as for those who reported being of Some Other Race (see Table 2). At -5.6%, the American Indian and Alaska Native population living on a reservation had the highest undercount rate in the 2020 Census, followed by those identifying as Hispanic or Latino (-5.0%), Some Other Race (-4.3%), and Black (-3.3%). Latinos and the Some Other Race population saw particularly striking increases in their net coverage errors from 2010 to 2020—up 3.5% and 2.7%, respectively. And while Asians were neither undercounted nor overcounted in 2010, they were significantly overcounted (by 2.6%) in 2020.

In prior censuses, most people who reported being of Some Other Race were Hispanic/Latino. The 2020 Census again asked respondents to identify their race and whether they were of Hispanic origin in two separate questions, but many Latinos do not distinguish between race and ethnicity in this way.⁷ For example, 37% of Hispanics/Latinos marked the Some Other Race category in 2010 because they didn’t identify with the race categories on the census form (such as White, Black, and Asian).⁸ If the majority of those who reported Some Other Race in 2020 were also Hispanic/Latino, then the undercount of Hispanics/Latinos was likely even higher than 5.0%.

Although Census Bureau research in 2015 showed that Hispanics/Latinos were much less likely to identify as Some Other Race when the race and Hispanic origin questions were combined into one question, the White House’s Office of Management and Budget (OMB) decided to continue to use the two separate questions for the 2020 Census. However, the new chief statistician of the United States recently launched a formal review of OMB’s standards for collecting federal data on race and ethnicity.

Significant undercounts and overcounts of race and Hispanic origin groups are problematic for two reasons. As noted in our discussion of children, undercounts of population subgroups can result in communities not receiving their fair share of federal resources. In addition, inaccuracy in racial and Hispanic origin data from the 2020 Census may have negatively impacted the redistricting process by impeding the creation of legislative districts that reflect the actual racial and ethnic diversity of the people they represent.

Renters Were Undercounted, While Homeowners Were Overcounted

The PES found that the 2020 Census significantly overcounted homeowners (0.43%) and undercounted renters (-1.48%) (see Table 2). The 2010 PES also found statistically significant overcounts of homeowners and undercounts of renters in the 2010 Census, but the magnitude of the renter undercount was greater in 2020. (DA does not provide coverage estimates for homeowners or renters.) Accurate data on the number and demographic characteristics of both homeowners and renters is needed to design and implement effective housing policy and programs such as affordable housing.

Widespread Errors Found in Data for Group Quarters Population

Counting people who live in group quarters (such as nursing homes, college dorms, and correctional facilities) is challenging in any census. For the 2020 Census, the coronavirus pandemic caused major disruptions and delays in counting the group quarters (GQ) population. In March 2020, nursing homes were shut to outside visitors, and many colleges and universities closed, sending students who were living on campus back home. When census data collection operations resumed in the summer of 2020, it was very difficult to get accurate counts of students who would have been living on campus on April 1.

An accurate count of this population is critical for towns and cities that are home to large numbers of group quarters residents, such as college students or prison inmates. Inaccurate data could negatively impact government funding allocations, disaster planning and emergency response, public health analysis and planning, and infrastructure planning.

After the Census Bureau identified gaps and inconsistencies in the group quarters data during processing and review, bureau staff contacted thousands of group quarters facilities in December 2020 to validate responses and fill in missing information.⁹ Despite these efforts, data users and local officials still uncovered problems with the group quarters data released in the PL 94-171(redistricting data) file in August 2021. Some GQ facilities were missing, others were placed in the wrong geographic location, and some—particularly college dorms—had inaccurate resident counts. In response to these issues, the Census Bureau implemented a one-time Post-Census Group Quarters Review (PCGQR) process in June 2022 to correct mistakes in the 2020 Census data for people living in group quarters. Although such corrections will not be used to modify 2020 Census data, they will be incorporated in the Census Bureau’s population estimates. The PCGQR process runs through June 2023.

How Accurate Was the 2020 Census at the State Level?

The PES for the 2010 Census found no states with significant population undercounts or overcounts. In contrast, the PES for the 2020 Census found six states—five in the South—with statistically significant undercounts and eight with significant overcounts (See Table 3). Net coverage errors for the six states with undercounts ranged from -1.9% in Texas to -5.0% in Arkansas. Although 2010 net coverage errors for these six states were not statistically different from zero, they are provided for comparison in Table 3. Four of the six states also had slight undercount rates in 2010, and two states—Arkansas and Illinois—had slight overcount rates.

Table 3. PES Estimates of Net Coverage Error by State

Note: *None of the percent net coverage errors for states was significantly different from zero in 2010.

Source: U.S. Census Bureau, 2020 Post-Enumeration Survey Estimation Report, PES20-G-02RV, Appendix Table 3, June 2022.

Net coverage errors for the eight states with significant overcounts ranged from 1.5% in Ohio to 6.8% in Hawaii. None of these states are in the South—they are distributed across the Northeast, Midwest, and West. Seven of these states also had slight, but insignificant, overcounts in 2010, but the increase in the magnitude of the overcount rates in 2020 was striking.

These state-level undercounts and overcounts in 2020 matter because they directly impacted the apportionment process, resulting in some states losing seats in the House of Representatives and others missing out on seats they might have gained if their populations were fully counted. This allocation of Congressional seats and corresponding electoral college votes cannot be changed until the 2030 Census. In addition, states with significant undercounts will not receive their fair share of any federal funding that is allocated based on population count.

Analyzing Components of Coverage From the PES Can Provide Additional Information About Census Accuracy

Net coverage error is only part of the picture of census data accuracy. The PES provides separate estimates of each component in the net coverage error calculation—erroneous enumerations, whole-person imputations, and omissions. Erroneous enumerations include people counted more than once, such as college students counted in both their dorms and at home. It also includes those who were counted but should not have been, such as foreign tourists. Whole-person imputations are people the Census Bureau added to the count from housing units that appeared to be occupied but whose residents did not complete a census form. Omissions are people who were missed in the census.

In the calculation of net coverage error, the number of omissions is offset by the combined number of erroneous enumerations and whole-person imputations. A net coverage error that is close to zero can mask a high number of erroneous enumerations and whole-person imputations that are offset by an equally high number of omissions. For example, the net coverage error for the 2010 Census was near zero (0.01%), yet there were 16 million people missed in the census and 10 million erroneous enumerations plus 6 million whole-person imputations.¹⁰ The omission rate was 5.3%. To get a more complete picture of census accuracy, stakeholders often focus on the rate of omissions—for the total population, population subgroups, and states.

Omission Rates Increased in 2020, Especially for Historically Undercounted Groups

The PES estimated a net undercount of 782,000 people in the 2020 Census. This total reflects approximately 18 million erroneous enumerations plus whole-person imputations, offset by 18.8 million omissions. The omission rate increased from 5.3% in 2010 to 5.8% in 2020.¹¹

Omission rates varied among racial and Hispanic origin groups (see figure). At 10.5%, Hispanics had the highest omission rate in 2020, followed closely by Blacks at 10.2%, and those reporting Some Other Race at 9.9%. Omission rates for all three groups increased between 2010 and 2020. These high omission rates, along with the significant undercount rates reported in Table 2, have raised concerns about the overall quality of 2020 Census data for race and Hispanic origin groups.

Figure. Census Omission Rates by Race and Hispanic Origin: 2010 and 2020

Note: Race is Alone or in Combination.

Source: U.S. Census Bureau, Presentation: Post Enumeration Survey and Demographic Analysis, March 10, 2022.

The 2020 omission rates were lowest among Asians (3.5%) and Whites (4.5%). Omission rates declined between 2010 and 2020 for the American Indian and Alaska Native, Native Hawaiian and Other Pacific Islander, and Asian populations.

Omission rates in the 2020 Census also varied by state. Table 4 ranks state omission rates for 2020 from worst to best and compares them to 2010. In 2020, these rates ranged from a high of 11.1% in Montana to a low of only 0.7% in Delaware. The two states with the highest omission rates—Montana and Louisiana—did not have significant undercounts, according to the PES. However, the next six states were in fact those that had significant undercounts in 2020. The omission rates for states with significant overcounts did not cluster as tightly as those with undercounts. Although Hawaii had the highest overcount rate at 6.8%, its omission rate ranked 43rd. And while New York had a significant net overcount rate of 3.4%, it had an omission rate of 5.9%, ranking 20th among the states.

Table 4. States Ranked by 2020 Omission Rates With Comparison to 2010 Omission Rates and Ranking

Sources: U.S. Census Bureau, 2020 Post-Enumeration Survey Estimation Report, PES20-G-02RV, Appendix Table 4, June 2022; U.S. Census Bureau, DSSD 2010 Census Coverage measurement Memorandum Series #2010-G-04, Table A1, May 2012.

Although no states had significant undercount or overcount rates in 2010, Table 4 shows that 29 states experienced increases in their omission rates between 2010 and 2020, while 21 states and the District of Columbia saw decreases. Excluding Ohio, all states with significant overcounts in 2020 saw their omission rates decline across the decade. The state rankings by omission rate varied considerably between 2010 and 2020. Among the four states with the highest omission rates in 2020, none ranked worse than 12th in 2010. On the other hand, Iowa—the state with the lowest omission rate in 2010 (2.6%)—saw its ranking drop from 50th in 2010 to 18th in 2020. Evaluation of omission rates, in addition to net coverage errors, provides useful information about variations in census data accuracy and quality among the states. In addition to providing estimates of population coverage, the PES also provides estimates of the coverage of housing units in the census (see Box).

Box 

Coverage of Total Housing Units Improved in the 2020 Census

In every census, some housing units are missed or counted in error. The Census Bureau’s Post-Enumeration Survey (PES) helps evaluate the accuracy of the housing units or addresses that were included in the census count. Similar to the estimates provided for persons, the PES estimates the number of housing units that were correctly enumerated, the number erroneously enumerated, and the number that were omitted when they should have been included. Erroneous enumerations include addresses that are duplicates, businesses rather than residential housing units, group quarters, housing units that do not exist or are uninhabitable, and housing units that were not available for occupancy until after census day (April 1). The PES provides estimates of net coverage error for housing units by occupancy status, tenure, type of structure, geographic area, and race and Hispanic origin of the householder.¹²

As was true for total population, the 2020 Census did not have a significant undercount or overcount of total housing units for the nation. The PES estimate of net coverage error was 0.04% and is not statistically different from zero. This represents a coverage improvement over the censuses of 1990, 2000, and 2010, which all had significant undercounts of total housing units (see Table). However, in 2020, there was a significant overcount of occupied units (0.33%) and a significant undercount of vacant units (-2.57%), although the net coverage error for vacant units was lower than in 2010, 2000, and 1990. The rate of erroneous enumerations of housing units was 3.1% (or 4.4 million), and the omission rate was 3.1% (or 4.3 million).

Table. The 2020 Census Overcounted Occupied Housing Units and Undercounted Vacant Units

Percent Net Coverage Error by Occupancy Status for Housing Units in the United States: 1990 to 2020

The Census Bureau does not speculate on the possible reasons for these coverage patterns but notes in their report that the 2020 Census faced challenges in conducting fieldwork during the COVID-19 pandemic. The PES, like other surveys, also faced challenges related to the pandemic and because of a general decline in survey response rates.

The small net coverage error for total housing units masks some significant coverage errors for some geographic areas and some types of structures, as well as by tenure and by race and Hispanic origin of the householder.

While housing units in the Northeast region were overcounted by 1.9%, there were no significant undercounts or overcounts of housing units in the Midwest, South, or West. Only two states had significant undercounts of housing units—South Carolina (-2.5%) and Vermont (-4.1%). However, seven states had significant overcounts: Alabama (2.7%), Massachusetts (1.4%), New Jersey (2.5%), New York (3.7%), Ohio (1.2%), Rhode Island (1.6%), and Utah (0.8%).

Rented housing units had an overcount of 0.85%, while there was no significant undercount or overcount of owned housing units. Net coverage error rates for single housing units and large multiunit structures (10+ units) were not statistically different from zero. In contrast, small multiunit buildings with two to nine housing units had a significant overcount of 5.1%, while mobile homes and other types of units had a significant undercount of -4.3%.

There were statistically significant overcounts of housing units with householders who were Black (0.87%), Asian (1.37%), Native Hawaiian or Other Pacific Islander (2.64%), or Some Other Race (0.58%).¹³ These significant net coverage errors for Black and Some Other Race householders are in the opposite direction of the net coverage errors for persons in these same groups (-3.3% and -4.34% respectively). One possible explanation for this discrepancy is that the census duplicated housing units, but not all the people living in those units, according to Census Bureau researchers. This would contribute to an overcount of housing units without contributing to an overcount of people.

What’s Next?

The consensus among census data users and stakeholders is that the Census Bureau did a remarkable job overall conducting the 2020 Census under extremely difficult circumstances. While there is no evidence of significant population undercounts or overcounts at the national level, the PES did uncover significant coverage errors for 14 states and several population subgroups. These inaccuracies have potentially serious consequences for underrepresented groups and individual states that will persist until the 2030 Census.

Although the Census Bureau provided 2010 coverage error estimates for counties and places with populations of 500,000 or more, no such substate estimates have been released for the 2020 Census, making it difficult to assess how data accuracy and quality varied across geographic areas and population subgroups within states. Citing the problems with group quarters data, as well as the data accuracy issues identified by both DA and the PES, some data users and stakeholders are calling for the Census Bureau to release more measures of data quality at the substate level.¹⁴ Evaluations of such data could provide a more comprehensive picture of the strengths and limitations of 2020 Census data at the local level, and help identify ways to improve the count in the 2030 Census.

References

1. George Washington Institute of Public Policy, “Counting for Dollars 2020: The Role of the Decennial Census in the Geographic Distribution of Federal Funds,” April 29, 2020.

2. Demographic Analysis provides three series of population estimates—low, middle, and high—by varying the level of historical births, international migration, and Medicare enrollment records. To simplify comparisons, we report estimates from the middle series only.

3. See “How Will We Measure the Accuracy of the 2020 Census?” for a more detailed description of both Demographic Analysis and the Post-Enumeration Survey.

4. See “How Will We Measure the Accuracy of the 2020 Census?” for a detailed description of net coverage error.

5. DA estimates of the number of children ages 0 to 4 are viewed as more accurate than those from the PES because DA uses birth records from the birth registration system in the United States, which is nearly 100% complete. See “Census Bureau Expands Focus on Improving Data for Young Children” for the DA estimates of net coverage error for children ages 0 to 4 from the 1970 through 2020 censuses.

6. See “Census Bureau Expands Focus on Improving Data for Young Children” for more information about these efforts.

7. See “Why Are They Asking That? What Everyone Needs to Know About 2020 Census Questions” for a detailed description of each question.

8. Paola Scommegna, “Changing Race and Ethnicity Questions Reflect Evolving Views,” PRB, February 19, 2020.

9. See “2020 Census Group Quarters” for a discussion of group quarters collection procedures and data processing and review.

10. See “Understanding Who Was Missed in the 2010 Census” for more information about net coverage error and omissions in the 2010 Census.

11. U.S. Census Bureau, Post Enumeration Survey and Demographic Analysis, March 10, 2022.

12. The householder is one of the people who owns or rents a housing unit. If the owner or renter lives somewhere else, then the householder is any one of the adults living in the housing unit.

13. Excluding the category Non-Hispanic White Alone, race and Hispanic origin of the householder is defined as alone or in combination with other groups.

14. See National Academies of Sciences, Engineering, and Medicine, Understanding the Quality of the 2020 Census: Interim Report, 2022, for a detailed discussion of potential substate quality indicators.

People in U.S. states with more liberal policies can expect to live longer than their peers in states with more conservative policies, thanks in part to laws on minimum wages, tobacco taxes, gun safety, and the environment, according to new research.

Life expectancy differences among states have widened in recent years, as state policies have become more polarized. In general, states where policies have become more liberal have added years to their residents’ lives more quickly, while states where policies have veered conservative have seen slower gains in life expectancy, finds research led by Jennifer Karas Montez of Syracuse University.¹

“The chances that an individual can live a long and healthy life appear to be increasingly tied to their state of residence and the policy choices made by governors and state legislators,” says Montez.

The research team examined life expectancy and policy change between 1970 and 2014 by merging annual data from the U.S. Mortality Database with annual data on 135 state-level policies scored on a liberal to conservative scale.² A liberal policy was defined as expanding state power for economic regulation and redistribution or for protecting marginalized groups, or restricting state power for punishing deviant social behavior; a conservative policy was defined as the opposite. For example, a high minimum wage would be categorized as a liberal policy, while low corporate taxes would be conservative.

State policy movement to the right (more conservative) and left (more liberal) since the 1980s, and particularly since 2010, may have affected the life expectancy of residents, the analysis found. States that enacted more conservative policies were more likely to see their life expectancy gains slow, stagnate, or even decline in recent years, compared with states with more liberal policies. Overall, state policies became more polarized over the 44 years studied (see Figure 1).

Figure 1. State Policies Have Become More Polarized Since 1980

Sources: Jennifer Karas Montez et al., “U.S. State Policies, Politics, and Life Expectancy,” The Milbank Quarterly 98, no. 3 (2020): 668-99; Jacob M. Grumbach, “From Backwaters to Major Policymakers: Policy Polarization in the States, 1970-2014,” Perspectives on Politics 16, no. 2 (2018): 416-35; and U.S. Mortality Database, University of California, Berkeley.

A striking example can be found in the case of Oklahoma and Connecticut (see Figure 2). The two states had identical life expectancies (71.1 years) in 1959. But between 1980 and 2019, life expectancy rose by just 2.5 years in Oklahoma (to 76.1), compared to a 5.9-year jump (to 80.8) in Connecticut. Oklahoma has seen an increasingly conservative policy environment, whereas policies in Connecticut have become more liberal.

Figure 2 Life Expectancy Gap Grew From 1959 to 2019, as Oklahoma’s Policies Became More Conservative, Connecticut’s More Liberal

Sources: Jennifer Karas Montez et al., “U.S. State Policies, Politics, and Life Expectancy,” The Milbank Quarterly 98, no. 3 (2020): 668-99; Jacob M. Grumbach, “From Backwaters to Major Policymakers: Policy Polarization in the States, 1970-2014,” Perspectives on Politics 16, no. 2 (2018): 416-35; and US Mortality Database, University of California, Berkeley.

If all states enjoyed the health advantages of a state like Connecticut, U.S. life expectancy would be on par with other high-income countries, according to the study’s analysis. While Oklahoma’s life expectancy in 2019 ranked near Croatia, Turkey, and the Czech Republic, Connecticut’s ranked among the Netherlands, Ireland, and Australia.³

An important driver of policy polarization in the United States is a shift in the way laws are made. The devolution of policymaking authority from the federal to the state level has opened the door for diverging policies, as states have gained more discretion over programs like welfare and Medicaid.

As policies have diverged, so have life expectancies. In 2019, the range in state life expectancy grew to 7.1 years—up from 5.5 years in 1959 (see Figure 3). That year, Hawaii had the longest life expectancy (81.8 years), while Mississippi had the shortest (74.7 years).

Figure 3. Life Expectancy Differences Have Widened Among States

Sources: Jennifer Karas Montez et al., “U.S. State Policies, Politics, and Life Expectancy,” The Milbank Quarterly 98, no. 3 (2020): 668-99; and U.S. Mortality Database, University of California, Berkeley.

A related study by Montez and colleagues found that more conservative marijuana policies and more liberal policies on the environment, gun safety, labor rights, economic taxes, and tobacco taxes were related to lower mortality in working-age Americans between 1999 and 2019.⁴ In particular, gun safety laws were associated with a lower suicide risk among men, labor protections like minimum wage and paid leave were tied to a lower risk of alcohol-related death, and tobacco taxes and economic taxes were linked to a lower risk of death from cardiovascular disease.

Marijuana restrictions were the only conservative policies associated with lower mortality, specifically from suicide and alcohol-related causes, the study found. Montez notes that while marijuana can provide pain relief, it also has been linked to an increased risk of developing problem drinking, depressive disorders, and schizophrenia, as well as a higher risk of motor vehicle accidents and suicide.⁵

Had all states enacted the most liberal policies, 171,030 lives might have been saved in 2019, the study estimates. On the other hand, enacting the most conservative policies might have cost 217,635 lives.

“The decisions being made in state houses are increasingly having life and death consequences for working-age Americans,” Montez said. “Much of the narrative about the rising death rates of working-age Americans has pointed to opioid manufacturers and businesses leaving certain parts of the country. Our analyses points to another major player, and that’s state policymakers.”

About the U.S. Mortality Database: Released in 2018 and updated annually, the U.S. Mortality Database (USMDB) provides mortality data by sex for U.S. geographic areas (all Census Divisions, Census Regions, States, and Washington, D.C.) for all years since 1959 and for counties since 1982. The series were constructed from natality and mortality data distributed by the National Center for Health Statistics and population data from the U.S. Census Bureau, using the methods of the Human Mortality Database (HMD) for Census regions, divisions and states, and Bayesian inference for the counties.

Access is free and the data can be downloaded following a short registration process. Initially funded by the National Institute on Aging and currently supported by various sponsors, including the University of California, Berkeley Center on the Economics and Demography of Aging and the Society of Actuaries, the USMDB is managed by the HMD team at the University of California, Berkeley.

This research was supported by the National Institute on Aging and conducted by a team of researchers including Jason Beckfield, Harvard University; Derek Chapman, Virginia Commonwealth University; Julene Kemp Cooney, Syracuse University; Jacob M. Grumbach, University of Washington; Mark D. Hayward, University of Texas at Austin; Blakelee Kemp, University of Nebraska-Lincoln; Huyseyin Zeyd Koytak, Syracuse University; Nader Mehri, Syracuse University; Shannon M. Monnat, Syracuse University; Steven H. Woolf, Virginia Commonwealth University; and Anna Zajacova, University of Western Ontario.

References

[1] Jennifer Karas Montez et al., “U.S. State Policies, Politics, and Life Expectancy,” The Milbank Quarterly 98, no. 3 (2020): 668-99.

[2] Jacob M. Grumbach, “From Backwaters to Major Policymakers: Policy Polarization in the States, 1970-2014,” Perspectives on Politics 16, no. 2 (2018): 416-35.

[3] Organization for Economic Cooperation and Development (OECD), “Life Expectancy at Birth.”

[4] Jennifer Karas Montez et al., “U.S. State Policy Contexts and Mortality of Working-Age Adults,” PLOS One, 17, no. 10 (2022).

[5] Karas Montez et al.