2020 Census Self-Response Rates Are Lagging in Neighborhoods at Risk of Undercounting Young Children

2020 Census Self-Response Rates Are Lagging in Neighborhoods at Risk of Undercounting Young Children

U.S. 2020 Census self-response rates are lagging in neighborhoods with a very high risk of undercounting young children, according to a new analysis by Population Reference Bureau (PRB).

As of June 25, 2020, the average self-response rate in census tracts with a very high risk of undercounting young children was 55%, while self-response rates in tracts with a low risk of undercounting young children—or a potential net overcount—were much higher, at 69%. The mean response rate across all census tracts in the United States was almost 62% (see Table 1).

TABLE 1. Average 2020 Census Self-Response Rates Across Census Tracts, by Risk of Undercounting Young Children

Tracts Average Tract-Level Response Rate (%)
All census tracts in the United States 61.5 %
All census tracts in large counties 63.0 %
Census tracts with a low risk of undercounting young children 69.1
Census tracts with a high risk of undercounting young children 64.5 %
Census tracts with a very high risk of undercounting young children 55.3 %

Notes: Data on response rates reflect rates as of June 25, 2020. Large counties are the 689 counties that had at least 5,000 children ages 0 to 4 in the 2010 Census.
Source: PRB analysis of data from the U.S. Census Bureau.

Data on the risk of child undercount are available for census tracts in 689 large counties—those with at least 5,000 children under age 5 in 2010. Collectively, these 689 counties accounted for about 93% of the national net undercount of young children in the 2010 Census. As of mid-June, the average 2020 Census self-response rate across tracts in these counties was 63%.

The following interactive figure shows a map of Washington, DC, which exhibits a sharp east-west divide in self-response rates and the risk of undercounting young children. Census tracts shaded dark red are those with a very high risk of undercounting young children and very low self-response rates (less than 49% as of June 25, 2020).

FIGURE. Risk of Undercounting Young Children and 2020 Census Self-Response Rates in the Washington, DC Area, by Census Tract (June 25, 2020)

Source: PRB analysis of data from the U.S. Census Bureau.

Just 20 counties—mostly located in California, New York, and Texas—account for 41% of all young children living in very high-risk census tracts (see Table 2). A few of these counties stand out because they have both large numbers and shares of children living in very high-risk neighborhoods, in combination with relatively low self-response rates. For example, 84% of children under age 5 in Miami-Dade County, Florida (132,235), live in neighborhoods with a very high risk of undercounting young children, and the average self-response rate in that county in late June was 59%, which is below the average across all large counties (63%). Over two-thirds of young children in Hidalgo County, Texas, live in very high-risk neighborhoods, and the mean self-response rate in that county was 46% in late June.

TABLE 2. 2020 Census Self-Response Rates in 20 Counties With the Largest Numbers of Young Children Living in Very High-Risk Census Tracts

wdt_ID Counties Number of Young Children Living in Very High-Risk Tracts Percent of Young Children Living in Very High-Risk Tracts Average Tract-Level Response Rate (%)
1 All large counties 4,062,432 24.7 63.0
2 Los Angeles County, CA 290,389 46.5 57.8
3 Harris County, TX 140,160 39.7 55.0
4 Miami-Dade County, FL 132,235 84.0 58.6
5 Cook County, IL 130,949 39.9 59.9
6 Kings County, NY 94,935 49.0 48.8
7 Queens County, NY 91,986 63.5 51.0
8 Bronx County, NY 87,466 82.5 52.9
9 Dallas County, TX 86,532 42.2 57.9
10 Broward County, FL 71,264 63.9 59.1
11 Philadelphia County, PA 69,185 64.2 50.9
12 Maricopa County, AZ 55,238 19.8 62.2
13 Hidalgo County, TX 54,216 67.7 46.4
14 Orange County, CA 52,528 27.8 71.0
15 Wayne County, MI 45,665 39.6 61.9
16 San Diego County, CA 45,340 21.4 67.5
17 San Bernardino County, CA 44,715 28.9 59.5
18 El Paso County, TX 42,160 64.7 58.8
19 Santa Clara County, CA 41,967 35.4 71.3
20 Bexar County, TX 39,492 28.5 59.7
21 Riverside County, CA 36,421 23.1 60.5

Note: Data on response rates reflect rates as of June 25, 2020. Large counties are the 689 counties that had at least 5,000 children ages 0 to 4 in the 2010 Census.
Source: PRB analysis of data from the U.S. Census Bureau.

Why does it matter if a neighborhood has a large share of households that have not responded to the census? Low self-response rates could lead to less accurate counts and fewer dollars for communities that need those funds the most. Accurate census data ensure that funding is equitably distributed for numerous programs benefitting children and families, such as the National School Lunch Program and Head Start. Census Bureau data are used to distribute more than $675 billion in federal funds to states and local communities for health, education, housing, and infrastructure programs each year.

Response Rates Are Lower in Neighborhoods With Higher Concentrations of Black, American Indian, and Latinx Children

Self-response rates were lowest in neighborhoods with high concentrations of racial and ethnic minorities in the young child population. The mean self-response rate for all tracts where Blacks make up the majority of young children was 51%, compared with 64% for tracts with a majority of non-Hispanic White children. The average self-response rate was just 21% in tracts with a majority of American Indian/Alaska Native children, which probably reflects the delayed start of the Census Bureau’s Update Leave operation in many rural areas. The average self-response rate for tracts with a majority of Latinx children was also relatively low, at 54%. The mean self-response rate was 62% in neighborhoods with a majority of Asian American children under age 5—similar to the average rate for neighborhoods with a majority of non-Hispanic White children.

These low response rates in communities of color are important because historically, certain racial and ethnic groups have faced a higher risk of being missed in the decennial census. Results from the 2010 Census show that among children under age 5, the net undercount rate was 7.5% for Latinx children and 6.3% for children classified as Black alone or in combination with one or more other races. The net undercount rate for all children under age 5 was 4.6%—higher than any other age group.

Identifying Neighborhoods to Target for Outreach

PRB has developed a series of maps and databases, which are being updated on a weekly basis, to help improve targeting of communities where children are most likely to be missed in the census. These resources highlight census tracts with a very high risk of undercounting young children and low 2020 Census self-response rates.

Interactive maps

Users can zoom in and out of these maps to view patterns in their states and local areas and can click on a census tract to view the tract FIPS code, undercount risk category, 2020 Census self-response rate, and estimated number of children under age 5 in 2014-2018.

The maps are divided into 11 separate files, covering all 50 states and the District of Columbia.

Users can zoom in and out of these maps to view patterns in their states and local areas and can click on a census tract to view the tract FIPS code, undercount risk category, 2020 Census self-response rate, and estimated number of children under age 5 in 2014-2018.

The maps are divided into 11 separate files, covering all 50 states and the District of Columbia.

Each database includes data on the risk of undercounting young children, the latest 2020 Census self-response rates, weekly change in response rates, key predictors of child undercount, and the racial/ethnic composition of the young child population.

  • State-County FIPS code.
  • State FIPS code.
  • State abbreviation.
  • State name.
  • County FIPS code.
  • County name.
  • Tract code.
  • Undercount of young children risk category.
  • 2020 Census self-response rate.
  • Weekly change in 2020 Census self-response rate.
  • Number of children ages 0 to 4.
  • Total population.
  • Percent of population that are young children ages 0 to 4.
  • Population ages 0 to 4 that is Black Alone.
  • Population ages 0 to 4 that is American Indian and Alaska Native Alone.
  • Population ages 0 to 4 that is Asian Alone.
  • Population ages 0 to 4 that is Native Hawaiian/Other Pacific Islander Alone.
  • Population ages 0 to 4 that is Two or More Races.
  • Population ages 0 to 4 that is White Alone, Not Hispanic.
  • Population ages 0 to 4 that is Hispanic or Latino.
  • Percent of children ages 0 to 4 in families with incomes below 100% of poverty.
  • Percent of adults ages 18 to 34 with less than a high school diploma.
  • Percent of children ages 0 to 17 living in a female-headed household.
  • Percent of children ages 0 to 5 living with grandparents.
  • Percent of households that are limited-English speaking.
  • Percent of children ages 0 to 5 living in immigrant families.
  • Percent of population living in renter-occupied housing units.

About These Estimates

The Census Bureau calculates household self-response rates for geographic areas that receive their census invitations in the mail, as well as households in Update Leave areas that receive their census invitation and paper form when a census taker drops off a package of materials at their residence.

Net undercounts represent a balance between two groups. One group is people omitted from the Census. The second group is erroneous enumerations (mostly people counted twice) and whole-person imputations.

The estimated risk of undercount for young children is based on PRB’s analysis of American Community Survey estimates and the U.S. Census Bureau’s Revised 2018 Experimental Demographic Analysis Estimates for young children. Data are based on 2020 Census tract boundaries.

While 2020 Census self-response rates are available for 2020 Census tracts, PRB’s original database on the undercount of children is based on 2010 Census tract boundaries. PRB matched 2010 Census tracts to 2020 Census tracts using a crosswalk file provided by the Census Bureau.

For a detailed description of the methods and data sources used to predict child undercount risk, please refer to William P. O’Hare, Linda A. Jacobsen, Mark Mather, and Alicia Van Orman’s report, Predicting Tract-level Net Undercount Risk for Young Children.


This research was funded by The Annie E. Casey Foundation, Inc., and we thank them for their support. The findings and conclusions presented in this report are those of the authors alone and do not necessarily reflect the opinions of the Foundation.

We also thank Dr. William P. O’Hare for all his work on the undercount of children in the census and for providing expert guidance to PRB staff on this project.

If you have any questions, please contact Mark Mather at PRB.

Untidy Kitchen slow motion.

Married Women With Children and Male Partners Do More Housework Than Single Moms

Women with children and a heterosexual male partner do the most housework—more even than single moms, according to an analysis of time-use data.1

Specifically, married and cohabiting mothers report more housework than never-married or divorced/separated mothers, but all mothers report about the same amount of child-care time, find Joanna Pepin of the University of Texas at Austin, Liana Sayer of the University of Maryland, and Lynne Casper of the University of Southern California (see table).

TABLE: Mothers With a Male Partner Do More Housework,
Sleep Less Than Single Mothers

Predicted Minutes of Mother’s Time in Activities, by Marital Status
  Married Cohabiting Never Married Divorced / Separated
Childcare 120 115 119 118
Housework 171 165 138 145
Leisure 209 243 219 201
Sleep 513 513 527 520

Note: Based on American Time Use Surveys (2003-2012). Model controls for extended family member, number of children, children under two years old, children ages two to five, education, employment, race, age, and weekend diary day.

Source: Joanna R. Pepin, Liana C. Sayer, and Lynne M. Casper, “Marital Status and Mothers’ Time Use: Childcare, Housework, Leisure, and Sleep,” Demography 55, no. 1 (2018): 107-33.

For the study, they examined 24-hour time-use diaries from participants in the nationally representative American Time Use Survey (ATUS) between 2003 to 2012; they focused on white, black, and Hispanic mothers ages 18 to 54 with at least one child under age 13 living with them. Their analysis takes into account weekday and weekend schedules, and other differences such as employment, education, age of children, and the presence of other extended family members in the household.

Married Mothers Sacrifice Sleep and Leisure

After adjusting for other factors, married mothers did significantly more housework and slept less than never-married and divorced mothers, which runs counter to the notion that single mothers are time poor because they lack a partner to help with household chores and work for pay, Pepin notes.

The findings show the trade-offs mothers make in the face of limited time. All mothers protected their time with their children, doing roughly the same amount of child care once other factors are controlled. But married mothers did more housework at the expense of their own leisure and sleep, while nonpartnered mothers tended to do less housework and sleep somewhat more, the researchers find.

Cohabiting mothers spent about the same amount of time doing housework and sleeping as married mothers, but cohabiting mothers reported more leisure time. The differences in leisure time between married and cohabiting mothers may reflect differences in work hours, work schedules, or commuting times, but more research is needed to clarify the reason.

Married women may feel that to be a good wife, they must prioritize housework and child care ahead of their own leisure and sleep.

Social Expectations Shape Women’s Time

The data show that women with a male partner in the home put more time into housework, such as home-cooked meals—work that is symbolic of women’s feminine roles. “Being in a partnership appears to ratchet up the demands or expectations for housework,” Pepin points out.

Married women may feel that to be a good wife, they must prioritize housework and child care ahead of their own leisure and sleep, Pepin suggests. In other research, women have told interviewers that they feel social pressure to provide home-cooked meals, clean clothes, and a well-kept house; these expectations appear to be closely tied to contemporary definitions of appropriate behavior for wives and mothers.

Mothers’ Quality and Quantity of Leisure Time Differ

While never-married and cohabiting mothers reported more leisure time than married and divorced/separated mothers, they were more likely to spend it in sedentary activities, such as watching television, usually alone. Pepin notes this time-use pattern may be partially explained by physically demanding jobs, although more research is needed to be certain. By contrast, married mothers were slightly more likely to report leisure activities that were social and active, such as going out with friends or exercising.

Work Schedules Challenge the Traditional Household Division of Labor

Another study using ATUS time-diary data examined time spent on various types of housework in U.S. heterosexual married-couple families with children.2 Regardless of whether or not they are employed outside the home, women tend to do more traditionally female housework tasks (interior cleaning, laundry, and meal preparation) and men do more traditionally male-typed housework tasks (home maintenance, yard work, and vehicle care).

However, work schedules and time constraints can contribute to nontraditional divisions of housework tasks between parents, report researchers Noelle Chesley of the University of Wisconsin–Milwaukee and Sarah Flood of University of Minnesota–Twin Cities.

They analyzed parents’ housework time and tasks, comparing breadwinner father/at-home mother couples with breadwinner mother/at-home father couples. They drew on ATUS time diary data for 2008 to 2012 from more than 4,500 participants; their analysis controlled for a variety of characteristics including age, education, race, unemployment, income, retirement, disability, and the number and ages of children in the household.

Cooking, Cleaning, and Laundry Comprise the Bulk of Housework Tasks

The way couples divide traditionally female household tasks drives the overall division of housework time because female-typed tasks tend to be done much more frequently (often daily) than male-typed tasks, they find.

Breadwinners spend less time doing housework tasks traditionally linked to their gender on work days. Housework time differences among breadwinner mothers and breadwinner fathers shrink as their daily work hours increase, suggesting that time availability plays a role in reducing gender differences in housework among parents in similar situations.

Women feel socially accountable for the appearance of the household.

“Our comparisons also suggest that at-home parents may do more gender nontraditional household tasks on days their spouses work,” they report.

In breadwinner mother/at-home father couples, differences in housework time depend on whether breadwinner mothers are at work on a given day. On their days off, breadwinner mothers do more housework than at-home fathers, spending as much time doing housework as at-home mothers. By contrast, at-home mothers appear to do more housework daily whether breadwinner fathers are at work or not.

“Among working parents, mothers and fathers likely feel different housework pressures,” the researchers suggest. “Women feel socially accountable for the appearance of the household.”

Earlier research shows that breadwinner mother/at-home father couples engage in a “domestic handoff” on breadwinner mothers’ days off, either as a way for mothers to feel in control or to give the at-home fathers a break, according to the researchers.

They find that breadwinner mother/at-home father couples are frequently economically disadvantaged. The arrangement is often an adaptation to male job loss or job instability rather than a choice made “out of a strong desire to fulfill gender egalitarian ideas.”

This article was produced under a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The work of researchers from the following NICHD-funded population research centers was highlighted in this article: University of Maryland, University of Minnesota, and University of Texas-Austin.



  1. Joanna R. Pepin, Liana C. Sayer, and Lynne M. Casper, “Marital Status and Mothers’ Time Use: Childcare, Housework, Leisure, and Sleep,” Demography 55, no. 1 (2018): 107-33.
  2. Noelle Chesley and Sarah Flood, “Signs of Change? At-Home and Breadwinner Parents’ Housework and Child-Care Time,” Journal of Marriage and the Family 79, no. 2 (2017): 511-34.

Aging and Health in China: What Can We Learn From the World’s Largest Population of Older People?

No other country in the world is experiencing population aging on the same scale as China.

The United Nations projects that there will be 366 million older Chinese adults by 2050, which is substantially larger than the current total U.S. population (331 million).1By that time, China’s share of adults ages 65 and older wills have risen from just 12% to a projected 26%. This rapid population aging—driven by recent declines in fertility and mortality—raises concerns about the health and well-being of older Chinese adults and will create considerable challenges for the health care system.

While life expectancy in China is increasing, older adults may spend more of their advanced years in poor health and with disabilities. Families have been the primary source of care for older adults, but the country’s rapid economic development and urbanization have separated millions of older adults from their children, contributing to an increasing demand for community-based health care.

These demographic and socioeconomic changes raise important questions for researchers and policymakers. How are older Chinese adults faring relative to their parents’ and grandparents’ generations? How is rapid urbanization affecting health and the availability of potential caregivers among older adults? How are older women faring relative to men, and which factors contribute to the gender gap in health? More broadly, what are the key factors associated with healthy aging in China, and what can policymakers do to improve health and reduce health disparities in the context of the country’s rapid socioeconomic development?

This issue of PRB’s Today’s Research on Aging (Issue 39) summarizes recent research on aging and health in China from U.S. National Institute of Aging-sponsored investigators and surveys, especially the China Health and Retirement Longitudinal Study (CHARLS) and Chinese Longitudinal Healthy Longevity Study (CLHLS). Results from these studies can shed light on the key determinants of healthy aging and help identify policies to address the challenges posed by rapid population aging in China.2The findings can also offer insights to policymakers in other countries with rapidly growing older populations.


Life Expectancy Is Increasing but China’s Aging Population Faces Health Challenges

China’s life expectancy has increased steadily during the past half century. In 1960, average life expectancy at birth in China was around 44 years. By 2017, it had increased to 76 years.3

Physical and cognitive health among older adults—especially women—is also improving with rising educational attainment and better medical care.4

Yi Zeng and colleagues find evidence of morbidity compression among China’s older adults—a reduction in the proportion of life spent with disability. Among adults ages 80 and older, mortality and self- reported disability rates have fallen relative to cohorts born 10 years earlier, according to their analysis of CLHLS data.5A recent study of adults ages 50 and older, based on CHARLS data, shows that at age 50, men can expect to live 24 years without activity limitations (26 years for women).6

These life expectancy gains and reductions in disability, however, are linked to rapid economic development in urban areas. Older adults in rural areas have not fared as well, leading to growing rural-urban disparities in health.7

Rising obesity rates and high smoking prevalence (among men) also present major health challenges for China’s aging population. In 2011, 28% of men and 38% of women ages 45 and older were overweight, putting them at higher risk of heart disorders, hypertension, diabetes, and stroke.8 Over half of men ages 45 and older (53%) smoked in 2011, compared with 5% of women in that age group.9 High levels of pollution—especially in urban areas—pose additional health risks.

“Public health campaigns and incentives are urgently needed on all these fronts so that the predictable long-term consequences of these behaviors on older age disease are not realized,” report researchers James Smith and his colleagues.9

Supportive Policies Can Address the Caregiving Gap for Older Adults

Families have traditionally been the major source of financial and caregiving support for older adults in China, and most older adults have children living with them or nearby who can provide caregiving assistance. CHARLS data show that about 41% of older adults live with an adult child, and another 34% have an adult child living nearby.11

However, China’s relatively low fertility rate will reduce the availability of family caregivers in the future.12 Between 1980 and 2015, China applied a family planning policy limiting most families to only one child to control the country’s rapid growth. Adults ages 65 and older have five to six surviving children, on average, while younger cohorts born in the late 1950s and 1960s have fewer than two adult children on average.13 China’s total fertility rate (TFR), or average number of births per woman, is around 1.6. By comparison, the TFR in Asia as a whole (excluding China) is around 2.3, and the TFR in the United States is around 1.7.14

A growing number of young adults in China are also moving from rural to urban areas for employment opportunities. The share of the population living in urban areas increased from 19% in 1980 to 60% in 2019.15 This trend has left many older adults geographically separated from their adult children.16

Home- and community-based services could help fill the caregiving gap by providing older adults with medical, rehabilitation, and other healthcare services. These kinds of paid services are increasing but are not widely available, even in cities.17

As the demand for home-based care increases, the cost of providing that care will also increase with the rising number of disabled older adults. These costs include paid medical and nursing services, as well as opportunity costs for unpaid family members or friends who are providing care. Using CLHLS data, Zeng and colleagues find that older Chinese women are more likely than their male counterparts to become disabled. Yet expenditures on home-based care are lower for older women than men—an issue that needs more attention from policymakers, according to the study researchers.18

Chinese policymakers can promote healthy aging among older adults by implementing policies that address rural-urban disparities in health. Smith and colleagues argue that policies to help keep families together by allowing older adults to migrate to cities with their children could help reduce the caregiving gap in future years.19 Policymakers can also address older adults’ needs by expanding access to home- and community-based services, which older adults prefer over institutional (nursing home) care.20

Finally, the government could improve health and longevity by expanding access to health insurance coverage. By 2011, about 93% of Chinese adults ages 45 and older had health insurance. However, middle-aged and older adults with lower incomes were less likely to be insured—especially those with less education, divorced/widowed women, and those living in rural areas.21

Older Adults Report Worse Health in Poor, Rural Areas

Older adults in rural China face additional health challenges. Self-reported health status varies widely across different areas of the country, with people from poorer rural counties reporting the worst health status. In the poorer rural counties, half of adults ages 45 and older reported being in poor health in 2011-2012, compared with 9% in better-off urban counties (see figure).22

Poor self-reported health in rural areas may reflect a lack of public health investments relative to better-off urban counties. Smith and colleagues find that living in areas in China without a strong public health infrastructure is associated with worse health in old age. In particular, using surface water instead of tap or underground water and using a toileting system without water may negatively affect health, including general health status and activities of daily living such as dressing, bathing, eating, and getting into or out of bed.23

FIGURE 1: Older Chinese Living in Poorest Rural Counties More Likely to Report Poor Health

Bar chart

Self-Reported Health Status of Chinese Adults Ages 45 and Older, 2011-2012

Source: James P. Smith, Meng Tian, and Yaohui Zhao, “Community Effects on Elderly Health: Evidence from CHARLS National Baseline,” Journal of the Economics of Ageing 1-2 (2013): 50-59.

Health Behaviors and Pollution Pose Health Risks in Urban Areas

China’s rapid economic development has contributed to longer life expectancy, but it has also brought challenges related to lifestyle changes and pollution, particularly in urban areas.

Biological risk—measured biomarkers that reflect cardiovascular, metabolic, and inflammatory processes—is higher among older adults living in urban areas. This urban-rural gap in biological risk is largely explained by lifestyle factors such as lower levels of physical activity, according to a recent analysis of CHARLS data by Yuan Zhang and Eileen Crimmins.24 Another study of Chinese adults ages 65 and older, based on CHARLS data, showed that living in urban areas later in life is associated with better initial cognitive status but a faster rate of cognitive decline.25 The researchers argue that faster cognitive decline in cities may be linked to higher levels of population density and “constricted life space,” high housing costs, and the high cost of food and health services, among other factors.

China has also become one of the most polluted regions in the world, posing additional health risks for older adults—especially those living in cities. Long-term exposure to fine particulate matter in China is linked to greater risk of mortality in old age, while proximity to green space is associated with longer life expectancy.24 Older adults living in rural and southern areas are more sensitive to pollution than those residing in urban areas and in northern China, where pollution levels are higher. Higher sensitivity to pollutants in rural areas may be linked to time spent outdoors, health care access, baseline health status, or differences in the type of particulate matter present in rural and urban areas.26

Wide Gender Gaps in Health Persist

Older Chinese women fare worse than men across a wide range of health measures. In 2011, women ages 45 and older were more likely than men to report being in poor or very poor health; experience depression, body pain, and hypertension; and have difficulties with activities of daily living (see table).

TABLE: Older Chinese Women Experience Poor Health Relative to Men Health Differences Among Chinese Men and Women Ages 45 and Older

  Women (%) Men (%)
Poor or very poor health 27.5 22.4
High depressive symptoms 40.7 28.1
ADL/IADL difficulty 29.6 21.8
Body pain 36.6 24.9
Total hypertension 43.9 40.3
Undiagnosed hypertension 39.6 42.8
Get treatment if hypertensive 48.8 44.6

Note: ADL means activities of daily living, such as dressing, bathing, eating, and getting into or out of bed. IADL means instrumental activities of daily living, such as shopping, cooking meals, managing money, and making phone calls.
Source: James P. Smith, John Strauss, and Yaohui Zhao, “Healthy Aging in China,” Journal of the Economics of Ageing 4, no. 2, (2014): 37-43.

Persistent gender gaps in education and literacy are partly to blame for older Chinese women’s poor health relative to men. Among adults ages 45 and older, about 40% of women were illiterate in 2011-2012, compared with 13% of men. Yet differences across age groups show the rapid progress women have made in recent years: The share of women ages 75 and older who were illiterate, at 80%, was 58 percentage points higher than the illiteracy rate among women ages 45 to 55 (22%).27

Researchers have also identified wide gaps in the cognitive abilities of older men and women. Gender differences in education largely explain this gap, especially among those in the oldest age groups and poorer communities. These gender differences in cognitive ability are decreasing as educational attainment increases among Chinese women in younger cohorts.29

Women’s childbearing patterns help explain the gender gap in health at older ages. Prior to the implementation of China’s one-child policy in 1980, women commonly had many children and started bearing them at a young age, which can negatively affect women’s health. Researchers have found that these effects can carry over into old age: Chinese women with four or more children are more likely to experience disabilities (impairment of activities of daily living, such as trouble dressing, bathing, eating, and getting into or out of bed) and poor self-rated health than women with one to three children.30

Conditions in childhood may also help explain gender gaps in health later in life. Using CLHLS data, Ke Shen and Yi Zeng find that favorable childhood conditions—based on birthplace, father’s socioeconomic status, and access to medical care in childhood—are linked to longer life expectancy through better socioeconomic status in adulthood.

“Public policies that target childhood well-being could effectively improve socioeconomic achievements in adulthood and, in turn, promote good health at senior ages,” argue researchers Shen and Zeng.31

However, this positive association is partially offset by “mortality selection.” The mortality selection hypothesis argues that unfavorable childhood circumstances result in high mortality rates among the most vulnerable people within a population and longer longevity for those who reach adulthood. This selection effect is larger for women than for men, possibly because surviving females tend to be healthier in countries with a strong son preference.32

“A girl might have worse nutrition and receive less care than a boy might. Such gender discrimination increases the mortality of vulnerable female infants, thus the surviving women are more selectively robust and have a higher chance of living into advanced ages,” according to the researchers.


China’s rapid economic development and urbanization may be a double-edged sword in their potential effects on the health and well-being of older adults. On the one hand, rapid economic growth has contributed to rising life expectancy and lower levels of disability. Rising educational attainment, especially among women, should lead to further improvements in health and reductions in health disparities among older adults. China’s health care system has also improved as the government has expanded access to public health services in both urban and rural areas.

 On the other hand, rapid urbanization is separating millions of older adults from their adult children. Rising obesity rates, high smoking prevalence, and high levels of pollution also raise serious concerns about the health of China’s older adults in the coming decades.

These health challenges will be exacerbated by rapid demographic change. China has the world’s largest population of older adults and is experiencing population aging on an unprecedented—and unstoppable—scale.

China’s challenges will be shared by leaders in many other developing countries that have experienced rapid declines in fertility and mortality in recent decades. Studying the factors associated with healthy aging in China can help policymakers and planners address the looming shortage of caregivers and improve the health and well-being of older adults.


1 United Nations (UN), World Population Prospects 2019, https://population.un.org/wpp/DataQuery/.

2 Yi Zeng, “Towards Deeper Research and Better Policy for Healthy Aging—Using the Unique Data of Chinese Longitudinal Healthy Longevity Survey,” China Economic Journal, 5, no. 2-3 (2012): 131-49.

3 World Bank Open Data, https://data.worldbank.org/.

4 James P. Smith, John Strauss, and Yaohui Zhao, “Healthy Aging in China,” Journal of the Economics of Ageing 4, no. 2 (2014): 37-43.

5 Yi Zeng et al., “Improvements in Survival and Activities of Daily Living Despite Declines in Physical and Cognitive Functioning Among the Oldest-Old in China–Evidence From a Cohort Study,” Lancet 389, no. 10079 (2017): 1619-29.

6 Hao Luo et al., “Health Expectancies in Adults Aged 50 Years or Older in China,” Journal of Aging Health 28, no. 5 (2016): 758-74.

7 Zuyun Liu et al., “Are China’s Oldest-Old Living Longer With Less Disability? A Longitudinal Modeling Analysis of Birth Cohorts Born 10Years Apart,” BMC Medicine 17, no. 23 (2019).

8 Overweight is classified as having a body mass index of 25 or more.

9 Smith, Strauss, and Zhao, “Healthy Aging in China.”

10 Smith, Strauss, and Zhao, “Healthy Aging in China.”

11 Xiaoyan Lei et al., “Living Arrangements of the Elderly in China: Evidence From the CHARLS National Baseline,” China Economic Journal 8, no. 3 (2015): 191-214.

12 Smith, Strauss, and Zhao, “Healthy Aging in China.”

13 Yi Zeng, “Options of Fertility Policy Transition in China,” Population and Development Review 33, no. 2 (2007): 215-46.

14 Toshiko Kaneda, Charlotte Greenbaum, and Kaitlyn Patierno, 2019 World Population Data Sheet (Washington, DC: Population Reference Bureau, 2019).

15 UN, 2018 Revision of World Urbanization Prospects, https://population.un.org/wup/ (2018).

16 Smith, Strauss, and Zhao, “Healthy Aging in China.”

17 Zhanlian Feng et al., “China’s Rapidly Aging Population Creates Policy Challenges in Shaping a Viable Long-Term Care System,” Health Affairs 31, no. 12 (2012): 2764–73.

18 Yi Zeng et al., “Implications of Changes in Households and Living Arrangements for Future Home-Based Care Needs and Costs of Disabled Elders in China,” Journal of Aging Health 27, no. 3 (2015): 519-50.

19 Smith, Strauss, and Zhao, “Healthy Aging in China.”

20 Feng et al., “China’s Rapidly Aging Population Creates Policy Challenges in Shaping a Viable Long-Term Care System.”

21 Chuanchuan Zhang et al., “Health Insurance and Health Care Among the Mid-Aged and Older Chinese: Evidence From the National Baseline Survey of CHARLS,” Health Economics 26, no. 4 (2017): 431-49.

22 James P. Smith, Meng Tian, and Yaohui Zhao, “Community Effects on Elderly Health: Evidence From CHARLS National Baseline,” Journal of the Economics of Ageing 1-2 (2013): 50-9.

23 Smith, Tian, and Zhao, “Community Effects on Elderly Health: Evidence From CHARLS National Baseline.”

24 Yuan Zhang and Eileen Crimmins, “Urban-Rural Differentials in Age- Related Biological Risk Among Middle-Aged and Older Chinese,” International Journal of Public Health 64, no. 6 (2019): 831-39.

25 Yuanxi Xiang et al., “The Impact of Rural-Urban Community Settings on Cognitive Decline: Results From a Nationally Representative Sample of Seniors in China,” BMC Geriatrics 18, no. 1 (2018): 323.

26 John S. Ji et al., “Residential Greenness and Mortality in Oldest-Old Women and Men in China: A Longitudinal Cohort Study,” The Lancet Planetary Health 3, no. 1 (2019): e17-25.

27 Tiantian Li et al., “All-Cause Mortality Risk Associated With Long-Term Exposure to Ambient PM2·5 in China: A Cohort Study,” The Lancet Public Health 3, no. 10 (2018): e470-477.

28 Xiaoyan Lei et al., “Gender Differences in Cognition in China and Reasons for Change Over Time: Evidence From CHARLS,” Journal of the Economics of Ageing 1, no. 4 (2014): 46-55.

29 Lei et al., “Gender Differences in Cognition in China and Reasons for Change Over Time: Evidence From CHARLS.”

30 Xiaomin Li et al., “Female Fertility History and Mid-Late-Life Health: Findings From China,” Journal of Women and Aging 30, no. 1 (2018): 62-74.

31 Ke Shen and Yi Zeng, “Direct and Indirect Effects of Childhood Conditions on Survival and Health Among Male and Female Elderly in China,” Social Science & Medicine 119 (2014): 207-14.

32 Shen and Zeng, “Direct and Indirect Effects of Childhood Conditions on Survival and Health Among Male and Female Elderly in China.”


Which Country Has the Oldest Population? It Depends on How You Define ‘Old.’

Japan, Italy, and Germany top the list of the world’s oldest countries—if the data are based on the share of the population ages 65 and older.But redefine “old” as the share of the population expected to live 15 years or less and Bulgaria, Latvia, and Ukraine top the list. Japan and Italy don’t even make the top 10 (see table).

What If “Old” Isn’t Measured by a Chronological Age?

Our measures of aging are static, but old age is changing, Warren Sanderson and Sergei Scherbov of the International Institute for Applied Systems Analysis (IIASA) argued at an international conference earlier this year and in their book, Prospective Longevity: A New Vision of Population Aging. 2

In their view, using age 65 as the threshold for old age overstates the impact of aging on societies. It assumes people become dependent and cease being economically active on their 65th birthdays.


Table. Top-10 Countries With the Oldest Populations Vary by Measurement Used

Share of the Population Ages 65 and Older, 2015 Share of the Population With a Remaining Life Expectancy of 15 Years or Less
Rank Country % Rank Country %
1 Japan 26.0 1 Bulgaria 18.1
2 Italy 22.4 2 Latvia 16.5
3 Germany 21.1 3 Ukraine 15.6
4 Portugal 20.7 4 Croatia 15.6
5 Finland 20.3 5 Serbia 15.2
6 Bulgaria 20.1 6 Germany 15.0
7 Greece 19.9 7 Lithuania 14.9
8 Sweden 19.6 8 Hungary 14.4
9 Latvia 19.3 9 Romania 14.4
10 Denmark 19.0 10 Georgia 14.4

Source: International Institute for Applied System Analysis (IIASA), Aging Demographic Data Sheet 2018 (Laxenburg, Austria: IIASA, 2018).

New measures could better capture the dynamics of population aging and eventually become the basis for policy changes that make public pensions such as Social Security more sustainable.3

Sanderson and Scherbov created a life expectancy-based measure, called the prospective old age dependency ratio (POADR), that calculates the number of people in age groups with remaining life expectancies of 15 years or fewer, divided by the number of adults (ages 20 and older) in age groups with life expectancies greater than 15 years.

By contrast, the commonly used old-age dependency ratio (OADR) is based solely on chronological age and is usually the ratio of people ages 65 and older to those in the traditional working ages (15 to 64).

Using the POADR’s moving threshold for defining old age means fewer people are classified as old in many countries, and the ratio of old people to working-age people increases much more slowly over the next few decades, Scherbov showed.4

The POADR and OADR are available for side-by-side comparison for all major countries on the Population Division of the United Nations Department of Economic and Social Affairs’ (UNDESA) website, Profiles of Ageing 2017.5 A PRB Population Bulletin, “Rethinking Age and Aging,” explored this measure in depth.6

Calls Increase for New Measures and Better Data

Sanderson and Scherbov are part of a growing chorus of scholars, organizations, and UN experts calling for both new measures of aging and better data on older people—explored in-depth at a February 2019 conference, “Measuring Population Ageing,” in Bangkok organized by UNDESA, IIASA, and Chulalongkorn University.

Most alternative measures of old age and dependency are based on groups of people’s characteristics rather than chronological age—not only remaining life expectancy, but also physical health, cognitive function, and labor force participation—which provide a more nuanced picture of older people’s needs and contributions to society. For example:

National Transfer Accounts (NTA): This set of multiple measures illustrates the complex ways resources flow across generations, including from older people to their children and grandchildren. The NTA project offers detailed data for 60 countries on the interplay among family support, government programs, and savings at various life stages (expressed as per capita averages).7

Measures such as the NTA allow researchers to examine the degree of economic dependency within a society and expected changes related to population aging, explained Alexia Fürnkranz-Prskawetz of the Vienna Institute of Demography and IIASA.8 Adding a time transfer measure that accounts for unpaid caregiving to the economic data highlights interdependency among generations and the disparate contributions of women of all ages, she showed.

Healthy Aging and Care Dependence: The World Health Organization (WHO) is working to refine measures of healthy aging as part of their 10 Priorities for a Decade of Action on Healthy Ageing.The focus is on older people’s ability to function (functional ability) within their environment, not their age or the conditions or diseases they have, explained WHO’s Ritu Sadana.10  The WHO measure of functional ability is based on the interaction between an individual’s physical and cognitive capacity (called intrinsic capacity) and their environment (both physical and social aspects of their home, neighborhood, and community).

Rather than using chronological age, the dependent portion of the population can be better estimated by focusing on care dependency, Sadana argued. The WHO care dependency measure tracks difficulty with the six activities of daily living—bathing, dressing, eating, getting in/out of bed, using the toilet, and walking across a room—related to declines in function.

Conference participants also examined better ways to track progress toward the internationally agreed upon Sustainable Development Goals (SDGs) as they relate to the well-being of older people, including health care access and economic security. Unlike the earlier Millennium Development Goals, the global targets for 2030 call for ensuring that the SDGs are met for all segments of society, including vulnerable older adults. Data on older people are limited in many countries and often not separated from data on other members of their multigenerational households, making assessing older people’s well-being difficult in many countries, reported Amal Abou Rafeh, Division for Inclusive Social Development/UNDESA; Storey Angele, Office of National Statistics, United Kingdom and Titchfield City Group on Ageing; and Patricia Conboy, HelpAge International.11

60 Is Becoming the New 50 as People Worldwide Live Longer

At the root of this interest in new ways and better data to measure old age are dramatic increases in life expectancy—dubbed a global longevity revolution by UNDESA.

UNDESA reports that new 60-year-olds in high-income countries can expect to live at least another 25 years, based on their Population Division’s World Population Prospects 2017.12 As recently as the 1950s, 25 years of additional life expectancy were limited to those 50 years old and younger in these countries, they show.

“In a very real, demographic sense, 60 is the new 50,” the agency suggests, further explaining the shift in this way:

“In Ethiopia, a new 60-year-old can expect to live at least another 18 years. This was also true of 50-year-old Ethiopians in 1950. In Ireland, a new 60-year-old can expect to live at least another 24 years. This was also true of 50-year-olds in Ireland in the 1950s.

“Progress has been fastest in Asia—where 60 is the new 48 [the average life expectancy of a 60-year-old is that of a 48-year-old in 1950]. Latin America shows about average progress—where 60 is the new 50. And Africa and Europe have had slower progress—where 60 is the new 54.”

PRB’s 2018 World Population Data Sheet underscores the growing shift toward longer lives and older populations, showing that 82 countries are projected to have at least 20 percent of their population ages 65 and older in 2050, compared with 13 countries in 2018.13 Most African countries have relatively young populations but are expected to experience some of the world’s most rapid growth in their total number of older people between 2018 and 2050, with profound social implications.

Better data will help us answer a key question: Are older people leading both longer and healthier lives? The authors of a Demography article, “Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades,” say, “Yes, for Americans, particularly men.”14 Morgan E. Levine of Yale University and Eileen M. Crimmins of the University of Southern California find biological evidence that Americans appear to be aging more slowly than they were two decades ago. They point out that decelerating aging and postponing age-related disease and disability can improve individual quality of life and have profound economic implications.

But among countries worldwide, a University of Washington study finds wide variation in how well or poorly people age.15 Their analysis, published in The Lancet, shows that a 30-year gap separates countries with the highest and lowest ages at which people experience the health problems of an average 65-year-old. Angela Y. Chang and colleagues report that 76-year-olds in Japan and 46-year-olds in Papua New Guinea have the same level of age-related health problems as an average 65-year-old. On this health-related scale of how a country’s people are faring, the United States ranked 53rd out of 195 countries.

Some highlights of the wide-ranging two-day conference, “Measuring Population Ageing: Bridging Research and Policy, are captured in this Twitter Moment; PowerPoint presentations and videos of the sessions are available here. As part of a panel of journalists and communicators examining media’s role in both shaping public attitudes and perpetuating stereotypes on population aging, PRB shared our experience communicating population aging concepts using digital media.



  1. International Institute for Applied Systems Analysis (IIASA), Aging Demographic Data Sheet 2018 (Laxenburg, Austria: IIASA, 2018).
  2. Warren C. Sanderson and Sergei Scherbov, Prospective Longevity: A New Vision of Population Aging (Cambridge: Harvard University Press, 2019).
  3. IIASA, “Analyzing Population Aging From a New Perspective,” IIASA Policy Brief, no. 12 (2016).
  4. Sergei Scherbov and Warren Sanderson, “New Measures of Population Ageing,” presentation at Measuring Population Ageing: Bridging Research and Policy expert group meeting, Bangkok, Thailand, Feb. 25-26, 2019, accessed at www.un.org/en/development/desa/population/events/pdf/expert/29/session1/EGM_25Feb2019_S1_SergeiScherbov.pdf.
  5. United Nations, Department of Economic and Social Affairs, Population Division, Profiles in Ageing 2017, accessed at https://population.un.org/ProfilesOfAgeing2017/index.html.
  6. Marlene Lee, “Rethinking Age and Aging,” Population Bulletin 63, no. 4 (2008), accessed at www.prb.org/wp-content/uploads/2008/12/63.4aging.pdf.
  7. National Transfer Accounts: Understanding the Generational Economy, accessed at www.ntaccounts.org/web/nta/show/.
  8. Alexia Fürnkranz-Prskawetz, “Quantifying Economic Dependency,” presentation at Measuring Population Ageing: Bridging Research and Policy expert group meeting, Bangkok, Thailand, Feb. 25-26, 2019, accessed at www.un.org/en/development/desa/population/events/pdf/expert/29/session6/EGM_26Feb2019_S6_AlexiaFuernkranz-Prskawetz.pdf.
  9. World Health Organization (WHO), 10 Priorities for a Decade of Action on Healthy Ageing, accessed at www.who.int/ageing/10-priorities/en/.
  10. Ritu Sadana, “Healthy Ageing—What Is It, Can We Measure It & Use It,” presentation at Measuring Population Ageing: Bridging Research and Policy expert group meeting, Bangkok, Thailand, Feb. 25-26, 2019, accessed at www.un.org/en/development/desa/population/events/pdf/expert/29/session1/EGM_25Feb2019_S1_RituSadana.pdf.
  11. Amal Abou Rafeh, “Conceptual Considerations for Measuring Ageing in the Context of MIPAA and Agenda 2030,” presentation at Measuring Population Ageing: Bridging Research and Policy expert group meeting, Bangkok, Thailand, Feb. 25-26, 2019, accessed at www.un.org/en/development/desa/population/events/pdf/expert/29/session2/EGM_25Feb2019_S2_AmalAbouRafeh.pdf; Angele Storey, “Tichtfield City Group on Ageing and Age-Disaggregated Data,” presentation at Measuring Population Ageing: Bridging Research and Policy expert group meeting, Bangkok, Thailand, Feb. 25-26, 2019, accessed at www.un.org/en/development/desa/population/events/pdf/expert/29/session2/EGM_25Feb2019_S2_StoreyAngele.pdf; and Patricia Conboy, “Leaving No One Behind—Measurement Issues,” presentation at Measuring Population Ageing: Bridging Research and Policy expert group meeting, Bangkok, Thailand, Feb. 25-26, 2019, accessed at www.un.org/en/development/desa/population/events/pdf/expert/29/session2/EGM_25Feb2019_S2_PatriciaConboy.pdf.
  12. United Nations (UN), World Population Prospects: The 2017 Revision (New York: UN, 2017).
  13. Toshiko Kaneda, Charlotte Greenbaum, and Kaitlyn Patierno, 2018 World Population Data Sheet (Washington, DC: Population Reference Bureau, 2018) , accessed at www.prb.org/2018-world-population-data-sheet-with-focus-on-changing-age-structures/.
  14. Morgan E. Levine and Eileen M. Crimmins, “Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades,” Demography 55, no. 2 (2018).
  15. Angela Y. Chang et al., “Measuring Population Ageing: An Analysis of the Global Burden of Disease Study 2017,” Lancet Public Health 4, no. 3 (2019).
Crowd from overhead

Citizenship Question Risks a 2020 Census Undercount in Every State, Especially Among Children

The addition of a citizenship question to the 2020 Census may put almost one in 10 U.S. households and nearly 45 million people at greater risk of not being counted―the question has been shown to reduce response rates. Undercount risk is particularly high among young children.

In April 2020, a census questionnaire will be provided by internet or mail to every housing unit in the country. A citizenship status question, which has not been included in a full decennial census enumeration since 1950, is planned for the 2020 Census.1 The question has raised concerns among elected officials, census experts, and community groups, and in the summer of 2018, dozens of states, cities, and other organizations filed lawsuits challenging the question’s addition to the census form.2

Each census question—how it is worded, how many and which categories are included—is usually carefully considered and pretested. The Commerce Department, however, added the citizenship question for the 2020 Census very late in the process. While the question text will be the same as the citizenship question that now appears on the annual American Community Survey (ACS), it was not included in the crucial 2018 Census Test, which served as the final dress rehearsal before the 2020 count.

In a memorandum to the secretary of Commerce in January 2018, the Census Bureau’s chief scientist reported that adding a citizenship question to the decennial census would be “very costly,” “harm the quality of the census count,” and would result in “substantially less accurate citizenship status data than are available from administrative sources.”3

Research Suggests a Citizenship Question Would Add Costs, Decrease Response, and Reduce Quality

Census Bureau research strongly suggests that “adding a citizenship question to the 2020 Census would lead to lower self-response rates in households potentially containing noncitizens, resulting in higher fieldwork costs and a lower-quality population count.”4 In short, adding a citizenship question increases the likelihood that people living with noncitizen(s) will be missed in the census count. When people are not counted in the census it is called an undercount.

To evaluate the size of the population at greater risk of being undercounted, Population Reference Bureau (PRB) identified households from the 2016 ACS in which at least one resident was a noncitizen. Anyone who is not a citizen of the United States by birth or naturalization is considered a noncitizen, including legal permanent residents and people who are in the United States with a student or work visa.

In 2016, at least 9.8 percent of households contained at least one noncitizen, according to analysis of administrative records and ACS data conducted by the U.S. Census Bureau.Because households with noncitizens are slightly larger on average than those without noncitizens, approximately 14 percent of the population (nearly 45 million people) lived in households with at least one noncitizen, putting them at increased risk of not being counted.6



The addition of a citizenship question in the 2020 Census may put children at a double disadvantage.


Children, Minorities, and People in Poverty Are Most at Risk

More than 13 million children under age 18 lived with at least one noncitizen in 2016. Across all age groups, children under age 5 were the most likely to live in noncitizen households (20 percent), while the share was lowest for adults ages 65 and older (5 percent) (see figure and table). Given that undercount rates have historically been highest among young children (relative to other age groups), the addition of a citizenship question in the 2020 Census may put children at a double disadvantage.7



Estimated Number and Percent of Population Living in Households With at Least One Noncitizen, by Demographic Characteristics, 2016

Total Population Population Living in Household With At Least One Noncitizen
Number Percent
Total population  323,128,000  44,824,000 14
Age Group
0 to 4 19,726,000 3,943,000 20
5 to 17 53,825,000 9,438,000 18
18 to 24 31,018,000 4,813,000 16
25 to 44 85,147,000 15,103,000 18
45 to 64 84,182,000 8,860,000 11
65 and older 49,228,000 2,665,000 5
Hispanic/Latino 57,390,000 25,775,000 45
Asian* 17,362,000 7,916,000 46
White* 197,487,000 6,772,000 3
Black* 39,809,000 3,214,000 8
Multiracial* 7,689,000 726,000 9
Other* 741,000 226,000 30
Native Hawaiian/Pacific Islander* 533,000 153,000 29
American Indian/Alaska Native* 2,117,000 42,000 2
Citizen 300,712,000 22,408,000 7
Noncitizen 22,415,000 22,415,000 100
Housing Tenure
Renter 110,685,000 24,306,000 22
Owner 204,362,000 20,065,000 10
Poverty Status
Income below poverty 44,208,000 9,369,000 21
At or above poverty 270,961,000 34,933,000 13

* Non-Hispanic. Data for American Indian/Alaska Native, Asian, Black, Native Hawaiian/Pacific Islander, Other, and White are for one race alone.

Source: PRB analysis of data from U.S. Census Bureau, American Community Survey, 2016.


People in poverty are also more likely to live in noncitizen households. In 2016, more than one in five people in poverty (21 percent) lived in a household with at least one noncitizen, compared with 13 percent of those above poverty. The share among renters was also high—22 percent of renters lived with at least one noncitizen, more than double the share for homeowners (10 percent).

The Asian population was most likely to live with at least one noncitizen in the household (nearly 46 percent), followed by the Hispanic/Latino population (45 percent). But, due to differences in the relative population size of these two groups, many more Hispanics/Latinos lived with at least one noncitizen (26 million) than Asians (8 million). Among non-Hispanic whites, about 3 percent (7 million) lived with noncitizens. American Indian and Alaska Native people were least likely to live with a noncitizen (2 percent, or 42,000 people).



People in poverty are also more likely to live in noncitizen households.


The Undercount Risk Affects Every State

The state with the largest population―California―had the largest number of people living in noncitizen households (11 million), followed by Texas (6 million). The state with one of the smallest populations―Vermont―had the smallest number of people living in noncitizen households (17,900).

In 21 states and the District of Columbia, 10 percent or more of the population lived in households with at least one noncitizen. In four states―California, Nevada, Texas, and New York―20 percent or more of the population lived in a household with at least one noncitizen (see interactive map).



Even states without many international migrants would potentially be affected by a census undercount. In 2016, West Virginia had the smallest share of people living with noncitizens (2 percent), but that still translates to more than 30,000 individuals who could be missed in the 2020 Census count.

An Accurate Count of the Population Is Essential

Article 1, Section 2 of the U.S. Constitution (as amended by the Fourteenth Amendment) states that congressional representation must be based on “counting the whole number of persons in each State.”8 An accurate count of the population is both essential and required for political redistricting, and it plays a vital role in many areas of public life.

The decennial census helps shape important infrastructure investments, such as hospitals, schools, roadways, bridges, and railways. Billions of dollars in federal funding are allocated each year based on census data, with an estimated $675 billion in funds distributed based on census data in fiscal year 2015.9

Accurate census data are also vital for public health. Detailed population information is critical for emergency response in the wake of disasters. First responders and disaster recovery personnel use population data to help identify where and how much help is needed. Similarly, demographic details from the census assist epidemiologists and public health personnel in everything from tracking disease outbreaks, to combating the opioid epidemic, to improving child health.

As these examples suggest, the inclusion of a citizenship question on the 2020 Census could reduce the availability of critical services for some of America’s most vulnerable populations, while also increasing the potential costs to taxpayers and reducing the quality of census data.


  1. A citizenship question appeared on the decennial census long form, completed by approximately one in six households, in 1980, 1990, and 2000. A citizenship question also appears on the annual American Community Survey.
  2. Hansi Lo Wang, “Multi-State Lawsuit Against Census Citizenship Question to Move Ahead,” NPR, July 26, 2018, accessed at www.npr.org/2018/07/26/629773825/multi-state-lawsuit-against-census-citizenship-question-to-move-ahead, on Oct. 2, 2018.
  3. John M. Abowd, “Memorandum: Technical Review of the Department of Justice Request to Add Citizenship Question to the 2020 Census” (Jan. 19, 2018), accessed at www.osec.doc.gov/opog/FOIA/Documents/AR%20-%20FINAL%20FILED%20-%20ALL%20DOCS%20%5bCERTIFICATION-INDEX-DOCUMENTS%5d%206.8.18.pdf#page=1289, on Oct. 2, 2018.
  4. David Brown et al., “Understanding the Quality of Alternative Citizenship Data Sources for the 2020 Census,” Center for Economic Studies (August 2018), accessed at www2.census.gov/ces/wp/2018/CES-WP-18-38.pdf, on Oct. 2, 2018.
  5. J. David Brown et al., “Understanding the Quality of Alternative Citizenship Data Sources for the 2020 Census.”
  6. PRB analysis of data from U.S. Census Bureau, American Community Survey, 2016.
  7. William P. O’Hare, “Why Are Young Children Missed So Often in the Census?,” The Annie E. Casey Foundation, KIDS COUNT Working Paper (December 2009), accessed at www.aecf.org/m/resourcedoc/aecf-WhyareYoungChildrenMissedInCensus-2009.pdf, on Oct. 2, 2018; and “One Million Missing: Undercount of Young Kids in the 2020 Census Threatens Gains,” The Annie E. Casey Foundation, June 27, 2018, accessed at www.aecf.org/blog/one-million-missing-undercount-of-young-kids-in-2020-census-threatens-gains/, on Oct. 2, 2018.
  8. “The Constitution: Amendments 11-27,” National Archives, accessed at www.archives.gov/founding-docs/amendments-11-27#14, on Oct. 2, 2018.

Continuity and Change in the U.S. Decennial Census

The first nation in the world to take a regular population census, the United States has been counting its population every 10 years since 1790—as required by the U.S. Constitution (Article I, Section 2).1

The first U.S. census was conducted by 16 U.S. marshals and their 650 assistants. It took them 18 months to visit households and compile the final tally of 3.9 million people, including nearly 700,000 slaves.

The population of the United States today is nearly 85 times larger than it was during that first census. The land area of the nation has changed. Technology and social structures have changed. And while the primary purpose of the decennial census remains the same—determining the number of seats each state occupies in the U.S. House of Representatives—uses of the data have grown.

The 1790 census included information on “Free White males of 16 years and upward” (to assess the country’s military potential).2 Today an accurate count of residents helps to shape important infrastructure investments, such as hospitals, schools, roadways, bridges, and railways. Detailed population information is also critical for emergency response during disasters.

Enumeration Methods Have Changed With Technology

Over the decades, census-taking switched from a task of the U.S. marshals to one of specially-trained enumerators (who took over in 1880), and from paper-and-pencil tabulation, to punch-cards, to electronic data collection.

Early censuses were taken by going door to door. In those early years—when literacy was low—enumerators asked questions and recorded information about each occupant in a household. Later, they began offering a mail-back form for those who did not want to respond in person. It was not until 1960 that mail self-response became the primary census data collection mode, but some communities, like those in remote Alaska, are still enumerated in person today.

Continuing the tradition of changing methods for changing times, the 2010 Census provided field workers with handheld electronic devices to capture address data, but still relied on paper data collection for nonresponse follow-up (when a trained Census worker visits an address for in-person data collection if the form for that address was not already mailed in).

Making yet another technological leap, the 2020 Census is designed to be conducted primarily via internet self-response. While paper questionnaires will still be mailed and in-person enumeration will be conducted for those households who do not respond, the Census Bureau expects that most households will submit their 2020 form online.

Questions Evolve in Response to Societal Change

The census questionnaires have changed every decade. In most cases the changes involved requesting more detailed information, but sometimes the modifications simply reflected prevailing social and political currents. For example, the number of racial categories used in the census has fluctuated considerably over the years. Groups identified by geography (such as Asians, Pacific Islanders, and Aleutian Islanders) have been listed as races, together with groups defined by skin color (blacks and whites). The racial categorization of some nationality groups has also changed over time. Asian Indians were included in the white race in the 1970 Census but were counted in the Asian and Pacific Islander category starting with the 1980 Census. The 1970 Census was the first to ask U.S. residents whether they were of Hispanic origin. And beginning with the 2000 Census, Americans were given the choice of marking all “race” categories with which they identified, resulting in the first decennial counts of multiracial persons.

As living arrangements became more complex, the question that asks how each household member is related to the householder added more response categories, including one for “unmarried partner” to reflect the increase in cohabitation.

In every decennial census from 1940 to 2000, two questionnaires were used to collect information: a “short form” with only basic questions such as age, sex, race, and Hispanic origin; and a “long form” that included about 50 additional questions on socioeconomic and housing characteristics. Only a subset of households received the long-form questionnaire—about one in every six in 2000. However, the 2020 Census—like that of 2010—will be a short form-only census. This is because the decennial long form has been replaced by the American Community Survey (ACS). The ACS is a nationwide, continuous survey designed to provide reliable and timely demographic, housing, social, and economic data every year. The ACS replaced the long form beginning in 2010 by collecting long form-type information throughout the decade rather than only once every 10 years.

Hard-to-Count Populations

Decades of research have shown that the decennial census is very accurate, but (like population censuses in other countries) it is subject to both undercount and overcount errors that differ by age, sex, and race. The 2010 Census was no exception. Despite the best efforts and careful planning of Census Bureau staff, the direct, physical enumeration of the U.S. population is imperfect.

Part of the challenge in counting the population accurately is that some people are harder to count than others. People who lack a permanent address are less likely to complete a census form than people who have a permanent address. Similarly, language barriers, distrust of government, and frequent moves tend to make certain groups harder to count. On the other side of the spectrum, some people may be counted more than once. For example, those who own more than one home may submit a census form for each address, and children away at college may be counted at both their college and parental home.

In 2010, the Census Bureau estimated that their total overcount was fairly small (about 36,000 people, or 0.1 percent of the population), but that over/under-counts varied by age, race, and other characteristics.3Both the 2000 and 2010 census tended to undercount renters and overcount homeowners. Young children tend to be undercounted, while older adults tend to be overcounted.4

The Census Bureau works to reduce over-/under-counts with each census. One new tool for the 2020 Census is the Response Outreach Area Mapper (ROAM), which can be used by community groups to identify local areas of potential undercount and target outreach to those neighborhoods.

Population and Cost Continue to Increase

As the total population count soared from 76 million in 1900 to more than 281 million by 2000, the cost of conducting the decennial census rose from about 16 cents per person to more than $16 per person.5 For the 2010 Census, the rate increased to more than $38 per person. The mailout/mail-back questionnaires, first used extensively in the 1960 Census, drastically cut back the need for enumerators to go door to door. In 2010, questionnaires were mailed to nearly all households, yet the Census Bureau still employed more than 600,000 temporary workers to help carry out that Census.6

The 2010 Census cost about $12.3 billion, but the 2020 Census is projected to be the most expensive ever, at approximately $15.6 billion, an increase of 27 percent.7 The population, however, is projected to increase by just under 8 percent between 2010 and 2020 (see table). While cost continues to rise faster than population, the increase in expense from 2010 to 2020 is expected to be lower than that of censuses for the past several decades because of anticipated savings from online response and streamlined field operations.

United States Population and the Cost of the Census

Census Population % Increase from Previous Census Cost % Increase from Previous Census
1980 226,542,199 11.4% $3,000,000,000
1990 248,718,301 9.8% $4,700,000,000 57%
2000 281,421,906 13.1% $9,400,000,000 100%
2010 308,745,538 9.7% $12,300,000,000 31%
2020* 332,555,000 7.7% $15,600,000,000 27%
*Projected population by July 1, 2020 and estimated cost for Census 2020.
*Projected population by July 1, 2020 and estimated cost for Census 2020.
Source: U.S. Census Bureau.


1Margo J. Anderson, Constance F. Citro, and Joseph J. Salvo, eds., Encyclopedia of the U.S. Census: From the Constitution to the American Community Survey (Washington, DC: CQ Press, 2012).

2U.S. Census Bureau, “Measuring America: The Decennial Censuses From 1790 to 2000” (September 2002), accessed at https://www.census.gov/history/pdf/measuringamerica.pdf on March 22, 2018.

3U.S. Census Bureau, “Census Bureau Releases Estimates of Undercount and Overcount in the 2010 Census” (May 22, 2012), accessed at https://www.census.gov/newsroom/releases/archives/2010_census/cb12-95.html on March 22, 2018.

4William O’Hare, “The Impact of the Undercount of Young Children in the Census on Poverty Estimates from the American Community Survey,” presentation delivered at the ACS Data Users Conference, Alexandria, VA, May 11, 2017, accessed at https://acsdatacommunity.prb.org/p/2017_acs_conference on March 22, 2018.

5Bryant Robey, “Two Hundred Years and Counting: The 1990 Census,” Population Bulletin 44, no. 1 (Washington, DC: Population Reference Bureau, 1989): 4-6.

6U.S. Department of Commerce: Office of the Inspector General, “Census 2010: Final Report to Congress” (June 27, 2011), accessed at https://www.oig.doc.gov/OIGPublications/OIG-11-030-I.pdf on March 22, 2018.

7U.S. Census Bureau, “2020 Census Operational Plan: A New Design for the 21st Century” (Nov. 2015), accessed at https://www2.census.gov/programs-surveys/decennial/2020/program-management/planning-docs/2020-oper-plan.pdf on March 22, 2018; U.S. Census Bureau, “2020 Census Life-cycle Cost Estimate Executive Summary” (Dec. 21, 2017), accessed at https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-management/planning-docs/cost-estimate.html on March 22, 2018.


U.S. Poverty Thresholds and Poverty Guidelines: What’s the Difference?

Have you ever worked with data about the federal poverty level and wondered what that level was? There are two different U.S. poverty measures, both often referred to as “poverty level”—one based on poverty thresholds and one based on poverty guidelines. Each has its own interpretation and application, but one can easily be confused for the other.

Poverty Thresholds Define and Quantify Poverty in the United States

What is it? The poverty thresholds, updated annually by the U.S. Census Bureau, are used to define and quantify poverty in the United States. A poverty threshold is a specified dollar amount considered to be the minimum level of resources necessary to meet the basic needs of a family unit1. Thresholds vary by the number and age of adults and the number of children under age 18 in the family unit, but they are the same for all states.2 If a family’s annual before-tax income is less than the threshold for their family size and type, all individuals in the family are considered poor.

How is it used? The thresholds are widely used to gauge the rise or fall in poverty over time and to compare poverty statistics across geographic areas and demographic groups.

What’s an example? A researcher might use poverty thresholds to assess how the poverty rate varies by age and sex. Poverty estimates from surveys like the Current Population Survey and American Community Survey are based on poverty thresholds.

Poverty Guidelines Determine Financial Eligibility for Some Programs and Benefits

What is it? The poverty guidelines, issued by the Department of Health and Human Services (DHHS), are simplified versions of the poverty thresholds. Like the thresholds, the poverty guidelines vary by family/household size. For instance, the 2018 poverty guideline for a family of four is $25,100. Unlike the poverty thresholds, the guidelines do not vary by the age of adults or number of children in a family/household. They do vary by geography—Alaska and Hawaii have separate guidelines.

How is it used? Poverty guidelines are used to determine financial eligibility for many programs and benefits.3 Typically, a percentage multiple of the guideline is used as the basis to determine program eligibility.

What’s an example? A researcher might use the poverty guidelines to estimate the number of adults in a community who are eligible for Medicaid. (Adults in Medicaid-expansion states qualify for Medicaid if their household income falls below 138 percent of their poverty guideline).

Confused Over Which Measurement Is Which?

Some surveys report data by “poverty level,” but may not clearly specify whether thresholds or guidelines were used. In these cases, the best approach is to read the technical documentation for details.

In addition, researchers often use income-to-poverty ratios to analyze the number of people in families at specified income levels. Similar to the program eligibility levels described above, these ratios may be expressed as percentage multiples (for example, the estimated number of people below 125 percent of poverty). But the ratios should not be confused with eligibility levels derived from the poverty guidelines. Again, the best approach, when in doubt, is to read the methodology or technical documentation for the report or survey you’re working with.

The following table shows how the two poverty measures can vary for a four-person household. Under the poverty guidelines, only one income value is provided for a household of a given size (column 3), regardless of the household’s composition (column 1). The poverty thresholds provide different values for given household sizes depending on the household’s composition (column 2). In households with more than one family, thresholds are determined independently for each family unit.

Poverty Thresholds Versus Poverty Guidelines for Households With Four People, 2017

Household Size and Composition

Poverty Threshold

Poverty Guideline

Two parents, two children



One adult, three children


Four unrelated adults

$12,752 (for each adult under age 65)

$11,756 (for each adult age 65 or older)

One adult (mother), two children (family 1), one unrelated adult under age 65 (family 2)

$19,749 (family 1)

$12,752 (family 2)

Two parents, one child, one relative (for example, aunt)


Notes : All children are related and are less than 18 years old. Guideline values are for the 48 contiguous states and the District of Columbia. (The guidelines would be $31,380 for Alaska and $28,870 for Hawaii.) The poverty guidelines issued in January 2018 are designated as the 2018 poverty guidelines, but only reflect inflation through calendar year 2017. As such, they are approximately equal to the Census Bureau poverty thresholds for calendar year 2017.

Sources: U.S. Census Bureau, “Poverty Thresholds,” accessed on March 20, 2018; U.S. Department of Health and Human Services, “Annual Update of the HHS Poverty Guidelines,” accessed on March 20.

Which Poverty Measure to Use Depends on Your Application

Here’s a quick reference for which measure to use based on the type of information you’re seeking. While not a comprehensive list, it may help orient you to the measure that will provide you with your desired data.

Use poverty thresholds if you want to:

  • Determine how many people (number or percent) are in poverty.
  • Identify how many families (number or percent) have income below 200 percent of poverty.

Use poverty guidelines if you want to:

  • Obtain an estimate of the number of families/households in a community eligible to participate in a particular program, such as the Supplemental Nutrition Assistance Program (formerly known as food stamps).

Check Survey Documentation!

When working with survey data to estimate poverty, make sure you check the documentation to determine which measure is being used. While many federal surveys such as the American Community Survey and the Current Population Survey report data based on poverty thresholds, others—such as the National Health and Nutrition Examination Survey—use the Department of Health and Human Services guidelines.

1. It is important to note that the official poverty measure has received criticism from policy analysts, academics, and others because its methodology has not changed since it was developed in the 1960s. Alternative poverty measures have been proposed and created. The Supplemental Poverty Measure is an example of an alternative measure that reflects modern spending patterns, geographic differences in housing costs, and noncash government benefits and tax liabilities.

2. It is assumed that all individuals who are living together and are related by birth, marriage, or adoption, share income.

3. No standard definition of income is used to determine eligibility. For example, one program may use before-tax income while another may use after-tax income. Not all means-tested programs use the poverty guidelines in determining eligibility. For examples of programs that do and do not use the poverty guidelines, reference the frequently asked questions page from ASPE.

Explore These Resources for More Information

Poverty (U.S. Census Bureau).

Frequently Asked Questions Related to the Poverty Guidelines and Poverty(Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services).

What Are Poverty Thresholds and Poverty Guidelines?(Institute for Research on Poverty).

Children in classroom with hands raised

Trends and Challenges Facing America's Latino Children

Latino children currently account for one-fourth of U.S. children under age 18, and by 2050 they are projected to make up nearly one-third of the child population.  Of the 18.2 million Latino children currently living in the United States, 95 percent are U.S.-born citizens.

How are Latino children faring and have their circumstances improved since the recession? A new publication, prepared in partnership between Population Reference Bureau (PRB) and the National Council of La Raza (NCLR), presents a snapshot of Latinos under age 18 to highlight areas of concern to policymakers. The report was released at an event in Washington, D.C. on September 29: “What’s New in Latino Child Well-Being? A Roundtable Discussion on the Emerging Trends and Remaining Challenges for America’s Hispanic Children.”

Results from the report show that during the past decade, Latinos have made important gains in several key areas of well-being—especially on measures of educational attainment, health insurance coverage, teenage births, and youth incarceration. But Hispanic youth continue to lag behind white youth on many key social and economic indicators. New projections by PRB show that the number of low-income Latino youth could increase by 45 percent—from 11 million today to nearly 16 million by 2050—if current levels of inequality persist in the future.

Reducing these disparities—especially by reducing racial/ethnic gaps in poverty and education—will not only improve economic conditions for millions of Latino parents and children, but will also fuel economic growth by creating a well-qualified workforce.

Rapid Increase in Latino Youth

In 2015, there were 18.2 million Latino youth living in the United States. The number of Latino children increased by 47 percent between 2000 and 2015 while the number of white and black youth declined (see Table). In fact, the total U.S. population under age 18 would have declined by 4.5 million between 2000 and 2015 without the increase in Latino children.


Change in the Population Under Age 18, by Race/Ethnicity, 2000 to 2015

2000 Population Under Age 18 (000s) 2015 Population Under Age 18 (000s) Population Change (000s) Percent Change
Total 72,294 73,645 1,351 1.9
Latino 12,342 18,150 5,808 47.1
White* 44,027 37,927 -6,100 -13.9
Black* 10,610 10,166 -444 -4.2
Other* 5,314 7,401 2,087 39.3

*Non-Hispanic. “Other” includes American Indian/Alaska Native, Native Hawaiian and Other Pacific Islander, Asian American, and Multiracial.

U.S. Census Bureau, 2000 Census and 2015 Population Estimates.

The rapid growth of the Latino youth population can be attributed to two main factors. First, past immigration of Hispanics to the United States—primarily from Latin America and Mexico—has resulted in a large number of Latinos who are now in their prime childbearing years, compared to other racial and ethnic groups. Even if U.S. borders were closed to all new immigrants, the number of Latino youth would continue to increase because of the young age structure of the Latino population, which creates population momentum through a large number of couples who are starting families.

Second, although the fertility rate among Latinas has fallen sharply in recent years, from 2.7 births per woman in 2008 to 2.1 births per woman in 2014, the Latina fertility rate remains higher than the rate among black women (1.9) and white women (1.8).  In the United States, the overall replacement-level fertility, or the rate needed for a generation to replace itself, is around 2.1 births per woman.

Latino Youth Population Growing Fastest in South

Historically, the Latino population has been highly concentrated in the Southwest and West, and in a few metropolitan areas outside these regions, such as Chicago, Miami, and New York. In 2015, 58 percent of Latino youth still lived in just four states: California, Florida, New York, and Texas. However, Latino families and children are increasingly dispersing to other parts of the United States, especially to states in the Sun Belt. Eight of the 10 states with the fastest-growing populations of Latino children between 2000 and 2015 were located in the South.

California and New Mexico stand out because they are the only two states where Latinos made up a majority of the population under age 18 in 2015, although Texas—at 49%—could soon pass this threshold.

Just three states—California, Florida, and Texas—accounted for 41 percent of the increase in the Latino youth population between 2000 and 2015. The rapid increase in Latino youth in these states reflects a combination of factors, including a rebounding economy that has fueled domestic and international migration to many Sun Belt states, and recent immigration trends that contributed to rapid population growth among first- and second-generation Latinos, especially from Mexico.

In 2014, states in parts of Appalachia, the Mid-Atlantic region, Florida, and New Hampshire had the highest proportions of first-generation Latino children. Second-generation Hispanic children were most highly concentrated in the Southeastern, Mid-Atlantic, and Western regions. And third- and higher-generation Latino children had the highest concentrations in the Northeast and several states in the Northern Midwest and Mountain West regions. The Northeast includes many families and children from Puerto Rico who are U.S. citizens by birth.

How Are Latino Youth Faring?

  • In 2015, more than three-fifths of Latino youth (62 percent) lived in low-income families (families with income below 200 percent of the official poverty line). While this was slightly lower than the share of black children in low-income families (65 percent), it was twice the proportion for white children (31 percent). Arkansas and North Carolina had the highest shares of Latino children in low-income families in 2014, at more than 75 percent each.
  • If the current proportions of children in low-income families persist in the future, the number of low-income Latino youth will increase from 11 million today to nearly 16 million by 2015, according to PRB projections (see figure). The number of low-income white children is projected to decrease over time with the changing composition of America’s youth population.
  • In 2014, a majority of Latino youth (58 percent) lived in married-couple families, but this represents a substantial decline from the level in 2000 (68 percent). Nationwide, the share of all children in married-couple families decreased from 72 percent to 65 percent during the same period. Latino youth in the Northeast were more likely to live in single-parent families compared to those living in other parts of the country.
  • In 2014, about two-thirds (64 percent) of Latino children under age 18 lived with mothers who graduated from high school, compared to 79 percent of black children and 90 percent of white children. However, maternal education levels have increased sharply among Latinas since the turn of the millennium, which has reduced the size of this racial and ethnic education gap.
  • A major success during the past decade has been the narrowing gap in high school graduation rates between white and Latino youth. In 2004, about 67 percent of Latinos who entered ninth grade completed 12th grade on time with a regular diploma, compared to 80 percent of whites—a 13 percentage-point difference. By 2013, the graduation gap had shrunk to 7 percentage points, with 78 percent of Latino youth and 86 percent of whites graduating from high school on time.
  • In 2015, about 21 percent of Latino eighth graders were proficient or advanced in reading, up from 17 percent in 2009. The 2015 reading proficiency rate among Latino eighth graders was higher than the rate for black eighth graders (16 percent), but less than half the rate for whites (44 percent).
  • Implementation of the Affordable Care Act has led to historic gains in Latino children’s health insurance coverage. Between 2008 and 2014, the percentage of Latino children without health insurance fell sharply, from 19 percent to 10 percent. Nonetheless, Latino youth still lag behind other groups in health coverage, and continue to face higher risks for some health outcomes, such as obesity.
  • The Latina teenage birth rate declined from 87 births per 1,000 Latina teenagers in 2000 to 38 in 2014. The recent drop in teenage birth rates among Latinas and girls in other racial/ethnic groups may reflect higher rates of contraceptive use as well as higher proportions of teenagers who are delaying sex.

Addressing the Needs of a Diverse Latino Population

While the report paints a comprehensive picture of Latino child well-being, it also shows that outcomes and trends are not uniform and vary across regions and states. States in the Southeast, for example, which have had newer influxes of immigrants over the past decade, also have higher rates of first- and second- generation Latinos. Young Hispanics in these states tend to have worse educational and economic outcomes than those whose families have lived in the United States for several generations. On the other hand, Southeastern states also have much lower rates of childhood obesity than states in the Southwest, which have more third-generation youth. Obesity, among other negative outcomes, tends to increase with time spent in the United States; these acculturation-related trends will be especially important to tackle as the number of third- and higher-generation Latino youth increases over time.

Understanding how Latino children have been faring over time and across states can help us ensure that our nation—our schools, our clinics, our practitioners and policymakers—make the right decisions to support these children so that they may thrive and develop into healthy, productive adults.

For easy access to the data described in the report, disaggregated by race/ethnicity, state, and year, visit the UNIDOS Latino Kids Data Explorer.


Nigerian Parliamentarian Pushing for New Population Policy Cites PRB Data As Evidence

(December 2015) In late November, Nigerian parliamentarian Babatunde Gabriel Kolawole spoke in the National Assembly and implored his colleagues to come up with a viable policy to “curb the population explosion in Nigeria.” Already the seventh-largest country by population in the world, Nigeria is on track to be the fourth-largest in 2050, with nearly as big a population as the United States. Nigeria’s total fertility rate is a high 5.5 children per woman.1

According to news reports, Kolawole backed a proposed motion for population policy legislation with projections from PRB’s World Population Data Sheet as evidence of a brewing crisis and the need to take quick policy action to avert it.2

This was a timely example of the relevance of the Population Reference Bureau (PRB)’s work to provide practical, evidence-based knowledge of family planning and other critical policy issues that affect the well-being of current and future generations, both globally and in the United States. The event also highlighted the politically charged nature of family planning discussions in Nigeria. According to the reports, some members of the National Assembly saw Kolawole’s motion as a potential threat to the country’s Muslims, who reside mainly in northern Nigeria and make up roughly half of the country’s population.3 The reports cited Zakara Mohammed, another parliamentarian, as saying that in Islam and in northern Nigeria generally, having more than one wife and bearing as many children as one likes is acceptable practice.

In a subsequent interview with the National Mirror, Kowawole sought to clarify his position. He expressed his surprise that the motion was viewed as an attack on Muslims. “That was not the intention of the motion. The motion was simply asking that the federal government should take steps to manage the population, and…educate Nigerians on the benefit of family planning,” he said. He also emphasized that an “…unbridled and unmanageable population has negative consequences. Everyone knows that. If at an estimated 166 million we are still adding about 5 million births per annum, there should be cause to worry.”

Diverging Population Trends in Nigeria

The parliamentary exchange comes in the context of diverging population trends among Muslims and non-Muslims in Nigeria (see Table). In 1990, Muslim women of childbearing ages (15 to 49) had on average 6.5 births, compared to 5.6 for non-Muslim women (the latter are mostly Christians, but also a small percent of people practicing traditional religions). By 2013, non-Muslim fertility had fallen to 4.5 births, while Muslim fertility remained the same, leading to a two-child difference between Muslims and non-Muslims, according to a recent report by Princeton’s Charles Westoff and PRB’s Kristin Bietsch, published by the Demographic and Health Surveys.5 This difference in fertility will lead to faster population growth among Muslims than non-Muslims in Nigeria.


Fertility Trends Among Muslim vs. Non-Muslim Women in Nigeria


Muslim Non-Muslim
Total Fertility Rate (TFR) (1990)* 6.5 5.6
TFR (2013) 6.5 4.5
Age at First Marriage (years) 16 21
Never Attended School (%) 65 9
Use of Contraception (any method) (%) 6 29
Desire to Stop Childbearing (%) 11 31

*Note: All data except for 1990 TFR are from 2013.

Adapted from Charles F. Westoff and Kristin Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa,” DHS Analytical Studies No. 48 (Rockville, MD: ICF International, 2015), accessed at www.dhsprogram.com/pubs/pdf/AS48/AS48.pdf, on Dec. 9, 2015.

Muslims girls and women tend to marry much younger than their Christian counterparts: The average age at first marriage is 16 for Muslim women, compared to 21 for non-Muslims.6 Also, 44 percent of married Muslim women are in polygynous unions, compared to 17 percent of non-Muslim wives.7 The differences in age at marriage also lead to differences in educational attainment for women: While only 9 percent of non-Muslim women in Nigeria have never attended school, the share is 65 percent for Muslim women.8

Only 6 percent of married Muslim women are currently using any form of contraception, compared to 29 percent of married, non-Muslim women.9 If family planning were widely available, fertility would not decline much among Muslims. On average, Muslim women in Nigeria wish to have more than eight children each, and also express less desire to halt childbearing: Three times as many non-Muslims wish to have no more children compared to Muslims (31 percent compared to 11 percent, respectively).10

Even when accounting for the socioeconomic differences between Muslims and non-Muslims in the country, the report finds that Nigerian Muslims are more likely to marry younger, to desire larger families, and to never have used contraception.

Note that this is not intended as a condemnation of reproductive preferences or practices of Muslims or any other group in Nigeria. Population growth rates should also not be forcibly changed and people’s rights should be respected. Rather, as Kolawale is cited as saying, the focus should be on public education about and availability of family planning options to help people decide when and how many children to have.


  1. Toshiko Kaneda and Kristin Bietsch, 2015 World Population Data Sheet (August 2015), accessed at www.prb.org/pdf15/2015-world-population-data-sheet_eng.pdf.
  2. “Birth Control Motion Tears Reps Apart” (Nov. 25, 2015), accessed at http://nationalmirroronline.net/new/birth-control-motion-tears-reps-apart/, on Dec. 9, 2015.
  3. National Population Commission (NPC) and ICF International, Nigeria Demographic and Health Survey 2013 (Abuja, Nigeria, and Rockville, MD: NPC and ICF International, 2014), accessed at www.dhsprogram.com/pubs/pdf/FR293/FR293.pdf, on Dec. 9, 2015.
  4. Ojo Oyewamide, “House of Reps Sensitive to Peoples’ Needs – Babatunde” (Dec. 8, 2015), accessed at http://nationalmirroronline.net/new/house-of-reps-sensitive-to-peoples-needs-babatunde/, on Dec. 9, 2015.
  5. Charles F. Westoff and Kristin Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa,” DHS Analytical Studies No. 48 (Rockville, MD: ICF International, 2015), accessed at www.dhsprogram.com/pubs/pdf/AS48/AS48.pdf
  6. Westoff and Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa.”
  7. Westoff and Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa.”
  8. Westoff and Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa.”
  9. Westoff and Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa.”
  10. Westoff and Bietsch, “Religion and Reproductive Behavior in Sub-Saharan Africa.”

Narrowing Old-Age Gender Gap in U.S. Linked to Smoking Trends

Women live longer than men in the United States and in nearly every country in the world. But in the United States and many other developed countries, this gender gap is narrowing, resulting in an increase in the number of men, relative to women, surviving to old age.

In 1990, there was a seven-year gap in life expectancy between U.S. men and women. By 2013, this gap had narrowed to less than five years. It’s not that women are dying sooner, but that men’s life expectancy is increasing at a faster pace. In 2013, life expectancy at birth was 76.4 years for males and 81.2 years for females. But if current trends continue, men’s life expectancy could approach that of women within the next few decades.

The growing number of men surviving to older age groups, relative to women, is contributing to a rising sex ratio—the number of males per 100 females—at older ages. The latest data from the U.S. Census Bureau show that in 2014, there were 79 men ages 65 and older for every 100 woman in that age group—up from 67 older men per 100 older women in 1990 (see figure). By 2030, the Census Bureau projects that the sex ratio for older adults will increase to 82 men per 100 women.

The data for those ages 85 and older are even more striking. The sex ratio has increased sharply among the “oldest old,” from 39 older men per 100 older women in 1990 to 52 in 2014. Census Bureau projections show that the sex ratio for those ages 85 and older could rise to 62 by 2030. That represents 24 additional men for every 100 women in the oldest-old category, compared with the 1990 estimate.


In the United States, the decline in the gender gap in mortality has been linked to male and female patterns of smoking, which increases the risk of death from lung cancer, heart disease, chronic obstructive pulmonary disease, and stroke. During the first half of the 20th century, smoking prevalence rates among men and women converged, as men’s rates declined from their earlier peaks and women’s rates increased—leading to a rise in smoking-related deaths among women relative to men.1 Smoking prevalence peaked among women born in the early 1940s, whereas prevalence rates peaked for men born in the 1910s. The result has been a steady reduction in smoking-related deaths among older men, and an increase in deaths among older women.

Similar declines in the gender gap in life expectancy have been reported in several countries in Europe. In France, the narrowing gap has been linked to reductions in male deaths due to heart disease and lung cancer. Improvements in men’s cardiovascular health, relative to women, has also helped reduce the gender gap in mortality in England and Wales, Sweden, Switzerland, and Italy.2

Missing African American Men

The gender gap at older ages is narrowing across all racial/ethnic groups, but the trend is most evident for non-Hispanic whites. Between 2000 and 2014, the number of white men ages 65 and older per 100 white women in that age group increased from 71 to 80—the largest increase among any of the major racial/ethnic groups (see Table 1).


Table 1
Sex Ratio at Older Ages (65+), by Race/Ethnicity, 2000 and 2014

2000 2014 Increase
Total 70 79 9
White* 71 80 10
Black or African American* 61 67 5
American Indian and Alaska Native* 75 82 7
Asian* 74 76 2
Native Hawaiian and Other Pacific Islander* 81 87 6
Two or more races* 72 79 8
Hispanic/Latino 72 76 4

Source: U.S. Census Bureau.


The sex ratio is also increasing among African Americans, but the number of older African American men is still lagging far behind the number of older women. In 2014, there were just 67 African American men per 100 African American women ages 65 and older. This imbalance is linked to the high rates of premature death among African American men. In 2013, the average life expectancy for black males at birth was just 72 years—well below the average for black females (78 years) and the U.S. average (79 years).

More Older Men in West, Fewer in South

There is a striking regional pattern in the sex ratio among older adults, with more men relative to women in parts of the West, and more women relative to men in the South (see map). The relatively low sex ratio for older adults in the South reflects the high concentration of older African American women, who are more likely to outlive their male counterparts.



The pattern in the West may reflect historical migration patterns of men moving to Western counties to work in male-dominated jobs, including agriculture and mining. The total male population in the Western Region outnumbered the female population until the 1960s, while the number of females outnumbered males in the Northeast and South by the 1930s, and in the Midwest by the 1940s.3 These historical patterns continue to shape the regional distribution of the U.S. population today. Among the 50 states, Alaska had highest sex ratio for older adults in 2014 (98 older men per 100 older women).

Many of the counties with the highest sex ratios are located in sparsely populated rural areas. Others, such as Noble County Ohio, have imbalanced sex ratios because they have large prison populations. Among larger counties with at least 10,000 people ages 65 and older,  Monroe County Florida—which includes the Florida Keys—had the highest sex ratio for older adults in 2014 (115 older men per 100 older women) (see Table 2). The Florida Keys is also home to a large number of divorcees: as of 2013 about 17 percent of men ages 65 and older in Monroe County were divorced, compared with 11 percent in Florida and 10 percent nationwide.


Table 2

Large Counties With the Highest Sex Ratios for Older Adults

(Counties With at Least 10,000 Adults Ages 65 and Older)

Monroe County, Florida 114.7
Nye County, Nevada 104.9
Lyon County, Nevada 101.0
Camden County, Missouri 100.6
Douglas County, Nevada 100.1
Mohave County, Arizona 98.4
Sumter County, Florida 95.9
Calaveras County, California 95.8
Pinal County, Arizona 95.3
Lake County, California 94.9
Wayne County, Pennsylvania 94.2
Brunswick County, North Carolina 94.0
Carroll County, New Hampshire 93.5
Gila County, Arizona 93.4
Klamath County, Oregon 93.4
Mason County, Washington 93.1
Gallatin County, Montana 92.9
Flathead County, Montana 92.8
Pike County, Pennsylvania 92.4
Cochise County, Arizona 92.3

Source: PRB analysis of data from the U.S. Census Bureau.

Four other large counties had sex ratios for older adults that exceeded 100, including three in Nevada (Douglas, Lyon, and Nye Counties) and one in Missouri (Camden County).

Implications for Older Adults

Historically, older adults have relied heavily on their adult children to provide support and care when they needed assistance. This is especially true among older women, who are much more likely than older men to be living alone. However, recent trends in marriage and family patterns may limit the availability of adult children who are available—or willing—to provide care for elderly parents in the coming decades. More young adults are delaying marriage, and in 2013, 41 percent of births occurred outside of marriage, up from 33 percent in 2000. As a result, more children are growing up in single-parent families and “blended families” made up of a couple and their children from their current and previous relationships. Children’s complex living arrangements may lead to weaker family ties and less support for aging parents.

Spousal care could potentially help fill this gap. With more men surviving to old age, there are more potential partners and caregivers available for older adults. However, the reality of caregiving is more complex because of the rising number of older adults who are single. It’s estimated that nearly half of female baby boomers will have been divorced by age 65.4 Although many of these women have remarried, there has also been a rapid increase in older adults who are single or cohabiting.5 Given these changes in marriage and family patterns, it’s too soon to tell whether men’s longer life expectancy will translate into better support mechanisms for older women, or if these gains will be offset by broader social trends.

Mark Mather is associate vice president of U.S. Programs at PRB.


  1. Samuel Preston and Haidong Wang, “Sex Mortality Differences in the United States: The Role of Cohort Smoking Patterns,” Demography 43, no. 4 (2006): 631-46.
  2. France Meslé, “Gender Gap in Life Expectancy: The Reasons for a Reduction of Female Advantage,” Revue d’Epidémiologie et de Santé Publique 52, no. 4 (2004): 333-52.
  3. Frank Hobbs and Nicole Stoops, Demographic Trends in the 20th Century, Census 2000 Special Reports, Series CENSR-4 (Washington, DC: U.S. Census Bureau, 2002).
  4. Andrew Cherlin, “Family Care for an Aging Population: The Demographic Context,” accessed at www.prb.org/pdf10/cherlin_presentation.pdf, on July 8, 2015.
  5. Susan L. Brown, Jennifer Roebuck Bulanda, and Gary R. Lee, “Transitions Into and Out of Cohabitation in Later Life,” Journal of Marriage and Family 74, no. 4 (2012): 774–93.