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

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.

Multi-Generation African hispanic family at home

U.S. Household Composition Shifts as the Population Grows Older; More Young Adults Live With Parents

Household size and composition play an important role in the economic and social well-being of families and individuals. The number and characteristics of household members affect the types of relationships and the pool of economic resources available within households, and they may have a broader impact by increasing the demand for economic and social support services. For example, the growth in single-parent families has increased the need for economic welfare programs, while a rising number of older adults living alone has led to greater demand for home health care workers and other personal assistance services. The decennial census provides the most comprehensive and reliable data on changing household size and composition, especially for less numerous household types such as same-sex married couples.

A Reversal of the Long-Term Decline in Household Size?

Average household size has declined over the past century, from 4.6 persons in 1900 to 3.68 persons in 1940 to only 2.58 persons by 2010.1 This decline is due to decreases in the share of households with three or more persons and increases in the share with only one or two persons. In 1940, for example, more than one in four households (27 percent) had at least five persons and less than one in 10 (8 percent) had only one person.2 By 2010, these shares had nearly reversed, with more than one-fourth of all households (27 percent) having only one person and slightly more than one-tenth (11 percent) having five or more persons.3

However, there are signs of a reversal in the decline in average household size. Although the trend away from large households has continued since 2010, average household size actually increased between 2010 and 2017 from 2.58 to 2.65 persons.4 If average household size remains larger than 2.58 in 2020, it will be the first such intercensal increase since the 1900 Census. The increase in average household size since 2010 appears to be driven by growth in the share of households with two persons—from 33 percent to 34 percent—and a decline from 40 percent to 38 percent in the share with three or more persons. Changes in household composition help explain these trends in household size.

Household Composition Continues to Shift From Family to Nonfamily Households

The shifts in U.S. household composition over the last five decades have been striking, as the share of family households has declined and the share of nonfamily households has increased. In 1960, 85 percent of all households contained families, but by 2017, this share had dropped to 65 percent (see Table). Conversely, the share of nonfamily households more than doubled from 15 percent to 35 percent during this period. The types of households within the family and nonfamily categories have also shifted, with a consistent decline in the share of married couples with children and a steep and consistent increase in the share of people living alone. Since 1960, the shares of single-parent families and other nonfamily households more than doubled.

TABLE. Share of Households With People Living Alone, Single-Parent Families Increases While Share of Married-Couple Households With Children Declines

wdt_ID Household Type 1960 1980 2000 2010 2020
1 Family Households 85 74 68 66 65
2 Married Couples w/ children 44 31 24 20 19
3 Married Couples w/out children 31 30 28 28 30
4 Single Parents w/ children 4 7 9 10 9
5 Other Family 6 6 7 8 9
6 Nonfamily Households 15 26 32 34 35
7 One Person 13 23 26 27 28
8 Other Nonfamily 2 4 6 7 7

Note: Percentages may not sum to 100 due to rounding.
Sources: James A. Sweet and Larry L. Bumpass, American Families and Households, Table 9.2 (New York: Russell Sage Foundation, 1987); U.S. Census Bureau, 2000 and 2010 decennial censuses; 2017 American Community Survey.

The Share of Married-Couple Households With Children Has Declined

In 1960, married-couple families made up 75 percent of all U.S. households, and 44 percent of these families had children. Single-parent families made up only 4 percent of all households, and other families accounted for 6 percent. By 1980, a significant shift in the composition of family households was underway. Married-couple families made up only 61 percent of all households, and the share with children dropped to 31 percent. The share of single-parent families nearly doubled from 4 percent to 7 percent of all households, while the share of married-couple families without children remained about the same at 30 percent.

Since 1980, the pace of change has slowed but the transformation of family households has continued. By 2017, married-couple families accounted for less than half of all households, and only about one-fifth (19 percent) of households were married couples with children. The share of married-couple families without children also declined slightly to 28 percent between 1980 and 2010, but increased to 30 percent between 2010 and 2017—almost back to the 1960 level of 31 percent. In contrast, the share of single-parent families continued to increase after 1980, rising to 10 percent by 2010, while the share of other families rose from 6 percent to 9 percent of all households by 2017.

The Share of One-Person Households Has Increased

In 1960, only 15 percent of all U.S. households were nonfamily households, and 13 percent were one-person households. Over the next 20 years, nonfamily households underwent dramatic shifts: The share of one-person households jumped to 23 percent, and the share of other nonfamily households doubled to 4 percent. The rapid growth in one-person households was largely due to increases in the share of older adults living alone, particularly women. The share of women ages 65 and older who lived alone rose from 23 percent in 1960 to 37 percent in 1980.5

The share of nonfamily households continued to rise after 1980, but at a slower pace. By 2017, more than one-third (35 percent) of all households were nonfamily households, and more than one-fourth (28 percent) were one-person households. The share of other nonfamily households also increased after 1980, reaching 7 percent by 2010. Beginning in the 1980s, the rise in cohabitation contributed to the growth in two-person nonfamily households; unmarried partners made up almost all of the households in this category in 2010. The share of other nonfamily households has not changed since 2010.

Household and Family Type Vary Widely Across Age Groups

Household composition varies among householders in different age groups and reflects the sequence of life-cycle stages that individuals experience as they age—from moving out on their own to marriage and family formation to empty nest to retirement. Changes in the share of householders in different age groups have contributed to shifts in household composition in the United States.

Most young adult householders in the United States live alone or with roommates. Three-fifths (61 percent) of households headed by an adult under age 25 were nonfamily households in 2017, while only 39 percent were family households (see Figure 1). One-third (33 percent) of householders under age 25 lived with unrelated roommates—including cohabiting partners—while an additional 28 percent lived alone. Only a small share (15 percent) headed married-couple families with or without children, but 14 percent of householders under age 25 headed single-parent families in 2017.

FIGURE 1. More Than Eight in 10 Older Adult Householders Are Living Alone or Are Empty Nesters, While Over Half of Young Adult Householders Live Alone or With Roommates


Percent Distribution of U.S. Household Types by Age of Householder, 2017

Notes: Percentages may not sum to 100 due to rounding. Among householders ages 65 and older, 0.4 percent headed married-couple households with children and 0.1 percent headed single-parent households with children.
Sources: U.S. Census Bureau, 2017 American Community Survey Public Use Microdata Sample (PUMS).

In contrast, the split between family and nonfamily households is reversed among householders ages 25 to 44—only 28 percent headed nonfamily households and 72 percent headed family households. While only one-fifth of households headed by an adult under age 25 included children, almost three-fifths (56 percent) of householders ages 25 to 44 headed families with children—both married-couple families (38 percent) and single-parent families (19 percent). Only 11 percent headed married-couple families without children. About one-fifth (19 percent) of householders in this age group lived alone in 2017, but less than one in 10 (9 percent) headed 2+-person nonfamily households—down from 33 percent among householders under age 25.

More than a third of householders ages 45 to 64 (37 percent) were empty nesters, heading married-couple households without children. Only about one-fifth (21 percent) of householders ages 45 to 64 headed families with children—16 percent were married-couple families and only 6 percent were single-parent families. However, a relatively high share of householders ages 45 to 64 were heading other family households (11 percent) and one-person households (26 percent).

Eight in 10 householders ages 65 and older were either heading married-couple families without children (44 percent) or living alone (42 percent). Only 10 percent of householders in this oldest age group headed other family households and only 3 percent headed other nonfamily households.

What’s Driving Changes in Household Composition?

Beginning in the 1960s—and accelerating over the last two decades—changes in marriage, cohabitation, and childbearing have played a key role in transforming household composition in the United States. More recently, population aging and shifts in the age distribution of householders are also contributing to these changes in composition.

Young Adults Continue to Delay Marriage and Childbearing

Delays in marriage and childbearing and increases in cohabitation among young adults have contributed to the decline in the share of family households—particularly married couples with children—and the steep rise in the share of nonfamily households. The median age at first marriage reached a new high in 2017—29.5 for men and 27.1 for women—and cohabitation rates have continued to increase.6 In 2011-2013, 65 percent of women ages 19 to 44 reported having had a cohabiting relationship, up from 33 percent in 1987.7

Birth rates among women under age 30 have continued to decline since 2010, although the rates for women ages 30 to 34 increased through 2016 before decreasing from 2016 to 2017.8 The share of births to women under age 40 that occurred outside of marriage increased from about 21 percent in 1980-1984 to 43 percent in 2009-2013; about 60 percent of the nonmarital births in 2009-2013 were to cohabiting couples—up from only 28 percent in 1980-1984.9

Between 2000 and 2010, the increase in cohabiting couples with children contributed to growth in the shares of both single-parent families and other nonfamily households due to the ways the Census Bureau classifies such couples by household type. However, between 2010 and 2017, the share of other nonfamily households stayed constant, and the share of single-parent families declined slightly from 10 percent to 9 percent. This decrease may be due to the drop from 18 percent to 14 percent in the share of householders under age 25 who were heading single-parent families. While declining birth rates among young women are partly responsible, this decline could also be related to more young couples with children living with their parents rather than in their own households. This explanation is supported by evidence of an increase in the number of multigenerational households, which rose from 4.4 million in 2010 to 4.6 million in 2017.

A Growing Share of Householders Are Ages 65 and Older

As fertility rates have fallen and baby boomers have aged, the distribution of the adult population ages 18 and older in the United States has shifted to older age groups. Between 2010 and 2017, the share of adults ages 45 to 64 declined from 35 percent to 33 percent, while the share ages 65 and older increased from 17 percent to 20 percent. About 22 percent of the adult population is projected to be age 65 or older by 2020.

These shifts in the age distribution of the adult population have been accompanied by changes in the age distribution of householders. Between 2010 and 2017, the shares of householders under age 25, ages 25 to 44, and ages 45 to 64 all declined by 1 or 2 percentage points, while the share of householders ages 65 and older increased by nearly 4 percentage points. This increase in the share of older householders is contributing to growth in the shares of both married-couple households without children and one-person households. These trends are likely to continue as more baby boomers enter older age groups in the coming decades.

Fewer Young Adults Are Forming New Households

Young adults forming new, independent households—alone, with a spouse or partner, or with unrelated roommates—has historically been an important factor in the overall household growth rate. Between 2010 and 2017, the young adult population (ages 18 to 34) increased by 4.2 million, accounting for nearly a quarter of the growth in the adult population (ages 18 and older).10 Yet, the household growth rate slowed to only 3 percent during this period—much lower than the 11 percent growth rate between 2000 and 2010. While the living arrangements of adults ages 35 to 64 have remained stable, recent changes in young adults’ living arrangements help explain the decline.

The share of young adults ages 18 to 34 who have formed an independent household has declined since 2010, while the share living with their parents has increased sharply. In 2010, less than one-third (32 percent) of young adults ages 18 to 34 were living with their parent(s), but this share jumped to 35 percent by 2017. The increase was sharpest among 25- to 29-year-olds, rising from 21 percent in 2010 to 26 percent in 2017 (see Figure 2). The share of 30- to 34-year-olds living with their parent(s) also increased by 4 percentage points across this period. In contrast, the share of young adults living in a married-couple family declined for all age groups between 2010 and 2017, with the largest drop among those ages 25 to 29.

FIGURE 2. Share of Young Adults Living With Their Parents Increases, While Share Living With a Spouse Declines


Selected Living Arrangements of Young Adults Ages 18 to 34 (%), 2010 to 2017

Notes: “Other living arrangements” include householders living alone, with an unmarried partner, with other relatives, or with nonrelatives. Percentages may not sum to 100 due to rounding.
Source: U.S. Census Bureau, 2010 and 2017 American Community Survey PUMS.

The Great Recession and the slow economic recovery, high student debt loads, and high relative housing costs have all likely contributed to the declining shares of young adults forming or maintaining independent households since 2010. Whether these patterns persist into 2020 and beyond is an open question. If the job market and earnings continue to improve, the ability of young adults to form new households may increase. If housing costs continue to rise, however, the resulting economic burden on young adults may counteract any improvements in employment and earnings and dampen household growth rates in the future.

This article is excerpted from Mark Mather et al., “What the 2020 Census Will Tell Us About a Changing America,” Population Bulletin 74, no. 1 (2019).



  1. Frank Hobbs and Nicole Stoops, Demographic Trends in the 20th Century (2002), Figure 5-3; and U.S. Census Bureau, 2010 Census Summary File 1.
  2. Hobbs and Stoops, Demographic Trends in the 20th Century, Figure 5-2.
  3. U.S. Census Bureau, 2010 Census Summary File 1.
  4. U.S. Census Bureau, 2017 American Community Survey.
  5. Hobbs and Stoops, Demographic Trends in the 20th Century, Table 5.
  6. U.S. Census Bureau, “Table MS-2. Estimated Median Age at First Marriage, by Sex: 1890 to the Present,” www.census.gov/data/tables/time-series/demo/families/marital.html.
  7. Wendy D. Manning and Bart Sykes, Twenty-Five Years of Change in Cohabitation in the United States, 1987-2013, FP-15-01 (Bowling Green, OH: National Center for Family and Marriage Research, 2015); Larry L. Bumpass and James A. Sweet, “National Estimates of Cohabitation,” Demography 26, no. 4 (1989): 615-25.
  8. Joyce A. Martin et al., “Births: Final Data for 2017,” National Vital Statistics Reports 67, no 8 (2018).
  9. Wendy D. Manning, Susan L. Brown, and Bart Sykes, Trends in Birth to Single and Cohabiting Mothers, 1980-2013, FP-15-03 (Bowling Green, OH: National Center for Family and Marriage Research, 2015); and Joyce A. Martin et al., “Births: Final Data for 2017.”
  10. U.S. Census Bureau, Vintage 2017 Population Estimates.

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

Product: Today's Research on Aging, Issue 39

Author: Mark Mather

Date: January 30, 2020

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.”


Majority of People Covered by Medicaid and Similar Programs Are Children, Older Adults, or Disabled

Medicaid provides health insurance coverage to more people than any other single program in the United States, with coverage for low-income children, adults, seniors, and those with disabilities.1 As of March 2017, there were 74 million Medicaid and Children’s Health Insurance Program (CHIP) enrollees, of which nearly 36 million were enrolled in CHIP or were children enrolled in Medicaid, according to the Center for Medicaid and CHIP Services.2

For a more detailed breakdown of people covered by Medicaid and other means-tested health insurance programs (like CHIP and others, listed in more detail below), we turned to the American Community Survey (ACS).3

As shown in the table below, children and youth represent nearly half of all people covered by means-tested public health insurance in the United States. Adults ages 65 and older, many of whom are low-income and participate in Medicaid to supplement Medicare, represent nearly 11 percent. Disabled and institutionalized adults account for another 14 percent, and women who have given birth in the past year represent just under 2 percent. These vulnerable groups account for more than seven in 10 participants in means-tested health insurance programs. Of those remaining, 12 percent work full time or part time. In short, Medicaid and CHIP participants are among the most vulnerable members of the U.S. population.

Table: The Majority of Publicly Insured Individuals Are Children, Older Adults, or Disabled.

wdt_ID Category of Participant Number in 2015 Percent Cumulative Percent
1 Child/Youth (under age 19) 30,419,902 4.6 4.6
2 Ages 65 and Older 7,155,401 10.8 56.6
3 Disabled (ages 19-64) 8,781,327 13.2 69.8
4 Institutionalized (ages 19-64) 380,517 0.6 70.4
5 Recent Mother (ages 19 and older) 1,040,193 1.6 71.9
6 Working Full Time Year Round (ages 19-64) 4,678,142 7.0 79.0
7 Working Part Time or Part Year (ages 19-64) 3,319,811 5.0 84.0
8 Other 10,641,447 16.0 100.0
9 TOTAL Means-Tested Public Health Insurance 66,416,740 100.0

Notes: Categories are mutually exclusive. Full-time, year-round work includes those working 35 hours or more per week, 50 or more weeks per year. Part time includes those working at least 10 hours per week, at least 47 weeks per year, excluding full-time, year-round workers.

Source: Population Reference Bureau analysis of U.S. Census Bureau, American Community Survey, 2015.

Using the ACS to Study Medicaid Recipients

In 2008, the Census Bureau added a question to the ACS asking about respondents’ current health insurance coverage. Individuals can have more than one type of insurance, and the data are broadly sorted into either private health insurance or public coverage. Public health insurance can be further broken down into Medicare, means-tested programs, and Veterans Administration health care. Means-tested health care includes:

  • Medicaid or Medical Assistance: Coverage for those with low-income or a disability (can vary by name in different states).
  • Children’s Health Insurance Program (CHIP): State-run programs for low-income children whose parents do not qualify for Medicaid.
  • Other means-tested programs: For example, state-specific plans that cover low-income uninsured individuals, such as county indigent services.

In the ACS, Medicaid health insurance is grouped with CHIP and with (considerably less common) other means-tested health care. This grouping occurs, at least in part, because survey respondents may not know which type of public coverage they have. Individual states may refer to their Medicaid and CHIP programs by different names (for example, the Medicaid program in California is known as Medi-Cal, and the CHIP program is referred to as Healthy Families). Further complicating matters, states may use the same name for more than one type of health insurance (for example, California manages both Medicaid and CHIP health insurance under the umbrella name Medi-Cal).

Taking these considerations into account, we analyzed the characteristics of people with means-tested public health insurance coverage (Medicaid, CHIP, or other public insurance) using 2015 ACS data.


Conducting Research With Medicaid Data

Various data sources are available to analyze the Medicaid program. Administrative data provide point-in-time counts and allow users to analyze trends in enrollment and program spending across time, but are limited in their ability to describe the people participating in the program. These data are not always available for public use or produced in a timely manner, making it difficult for researchers or policymakers to understand the program’s current effectiveness.

Conversely, surveys (such as the ACS) provide social and demographic data such as age, sex, education, and race/ethnicity, which can help researchers and policymakers better understand Medicaid recipients. Another added benefit is the capability for deeper analysis by adding population data as denominators to calculate rates and percentages.

There are also limitations to using survey data as a source for Medicaid-related research. Survey data tend to underestimate participation in social programs.4 Historical data may not be available, and national surveys may not accurately measure participation in state-specific programs. Despite these limitations, we used data from the ACS for this analysis because it provides detailed demographic data and is a nationally representative sample of the population.


Other Sources of Data on Medicaid Enrollment

National Survey Data

Current Population Survey (CPS): Provides demographic detail but cannot provide estimates of state-level coverage.

National Health Interview Survey (NHIS): Estimates both coverage status and length of time with coverage at time of interview but states must be combined to produce reliable annual estimates.

Medical Expenditure Panel Survey (MEPS): Contains detailed information covering two full calendar years that can be broken down into census regions (Northeast, Midwest, South, West).

Survey of Income and Program Participation (SIPP): Includes health care coverage status as well as disability status but cannot provide annual estimates.

Aggregate-Level Administrative Data

Medicaid Budget and Expenditure System (MBES): Aggregate enrolled data available quarterly, can be linked to claims data but lacks demographic detail.

Centers for Medicare & Medicaid Services (CMS) Performance Metric Data: Updated monthly and includes details by state and program.

Individual-Level Administrative Data

Medicaid Statistical Information System (MSIS): While not publicly available, can link enrollment and spending but lacks demographic characteristics.


Additional Resources

Census Bureau, “Health Insurance” https://www.census.gov/topics/health/health-insurance/about/glossary.html

Moving Medicaid Data Forward, Forum: Medicaid Enrollment—Overview and Data Sources https://www.mathematica-mpr.com/events/moving-medicaid-forward-part-2

Medicaid Pocket Primer



1. Henry J. Kaiser Family Foundation, “Why Does the Medicaid Debate Matter? National Data and Voices of People With Medicaid Highlight Medicaid’s Role,” (June 19, 2017), accessed at www.kff.org/medicaid/fact-sheet/why-does-the-medicaid-debate-matter-national-data-and-voices-of-people-with-medicaid-highlight-medicaids-role/, on June 26, 2017.
2. Medicaid.gov, “April 2017 Medicaid and CHIP Enrollment Data Highlights,” accessed at www.medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment-data/report-highlights/index.html, on June 26, 2017.
3. Means-tested health insurance programs are available to people on the basis of income, age, or other qualifying condition (such as disability).
4. Brett Fried, State health access Data Assistance Center, “Medicaid Undercount in the American Community Survey: Preliminary Results,” (August 7, 2013), accessed at www.shadac.org/sites/default/files/publications/ACS_Undercount_JSM2013_BFried.pdf, on June 26, 2017.


Least Segregated U.S. Metros Concentrated in Fast-Growing South and West

U.S. 2010 Census results show that black-white residential segregation declined modestly since 2000, continuing the gradual pace begun in 1980.

Among large metropolitan areas with a total population of 500,000 or more, the least segregated metros were located in the faster-growing South and West, while the most segregated metro areas were mainly concentrated in the slower-growing Northeast and Midwest (see table).


The 10 least-segregated metro areas all grew faster than the national average of 11 percent between 2000 and 2010, with seven of them seeing increases of 20 percent or more, reports Kelvin Pollard, PRB demographer and co-author of PRB’s Reports on America: “First Results From the 2010 Census.” Only one of the 10 most-segregated metros experienced growth rates that reached even half the national average.


“Least-segregated Raleigh and Las Vegas were among the nation’s fastest growing metros with growth rates topping more than 40 percent for the decade, while most-segregated Detroit, Cleveland, and Buffalo were among those that lost population,” he notes.


Demographers use the segregation (or dissimilarity) index to measure how racial groups are spread throughout a metro area’s census tracts. An index of 100 would mean blacks live in exclusively black neighborhoods and whites live in exclusively white neighborhoods, while a score of zero means each neighborhood has the same share of black and white residents as the metro area as a whole.


“Milwaukee’s index of 81.5 means that about eight out of 10 black residents would need to move to another Milwaukee neighborhood to be distributed throughout the metro area in the same way as whites,” Pollard explains. Demographers call levels of 60 and above highly segregated, 39 to 59 moderately segregated, and below 39 less segregated.

Ten Least and Most Segregated Metropolitan Areas With Population Change, 2000 to 2010

wdt_ID Least Black-White Segregated Metros Segregation Index, 2010 Percent Population Change, 2000-2010
1 Tucson, Ariz. 3.7 1.6
2 Las Vegas-Paradise, Nev. 37.6 41.8
3 Colorado Springs, Colo. 39.3 20.1
4 Charleston-North Charleston-Summerville, S.C. 41.5 21.1
5 Raleigh-Cary, N.C. 42.1 41.8
6 Greenville-Mauldin-Easley, S.C. 43.6 13.8
7 Phoenix-Mesa-Glendale, Ariz. 43.6 28.9
8 Lakeland-Winter Haven, Fla. 43.9 24.4
9 Augusta-Richmond County, Ga.-S.C. 45.2 11.5
10 Riverside-San Bernardino-Ontario, Calif 45.7 29.8

Note: Metro areas with fewer than 500,000 total residents or where non-Hispanic blacks made up fewer than 3 percent of the population were not included when ranking black-white segregation indices.
Sources: Segregation Indices: William H. Frey, Brookings Institution, and University of Michigan Social Science Data Analysis Network, Analysis of 1990, 2000, and 2010 Decennial Census tract data, accessed at www.psc.isr.umich.edu/dis/census/segregation2010.html, on Aug. 29, 2011. Population Data: Population Reference Bureau, analysis of 2000 and 2010 Decennial Census data.

Ten Most Segregated Metropolitan Areas With Population Change, 2000 to 2010

wdt_ID Most Black-White Segregated Metros Segregation Index, 2010 Percent Population Change, 2000-2010
1 Milwaukee-Waukesha-West Allis, Wisc. 8.2 0.4
2 New York-Northern New Jersey-Long Island, N.Y.-N.J.-Pa. 78.0 3.1
3 Chicago-Joliet-Naperville, Ill.-Ind.-Wisc. 76.4 4.0
4 Detroit-Warren-Livonia, Mich. 75.3 -3.5
5 Cleveland-Elyria-Mentor, Ohio 74.1 -3.3
6 Buffalo-Niagara Falls, N.Y. 73.2 -3.0
7 St. Louis, Mo.-Ill. 72.3 4.3
8 Cincinnati-Middletown, Ohio-Ky.-Ind. 69.4 6.0
9 Philadelphia-Camden-Wilmington, Pa.-N.J.-Del.-Md. 68.4 4.9
10 Los Angeles-Long Beach-Santa Ana, Calif. 67.8 3.7

Note: Metro areas with fewer than 500,000 total residents or where non-Hispanic blacks made up fewer than 3 percent of the population were not included when ranking black-white segregation indices.
Sources: Segregation Indices: William H. Frey, Brookings Institution, and University of Michigan Social Science Data Analysis Network, Analysis of 1990, 2000, and 2010 Decennial Census tract data, accessed at www.psc.isr.umich.edu/dis/census/segregation2010.html, on Aug. 29, 2011. Population Data: Population Reference Bureau, analysis of 2000 and 2010 Decennial Census data.

Rustbelt Ghettoes Not Replicated Elsewhere


Segregation persists in older cities in the Northeast and Midwest where a large share of the nation’s African American residents live, “buttressed by a history of poor race relations and continuing discrimination,” says John Iceland, a Penn State University demographer who studies segregation and poverty. Cities like Chicago, Detroit, and Philadelphia have long-established black communities—often called ghettos—that grew during the migration of African Americans from the South for industrial jobs during the first half of the 1900s.


Still, the 2010 Census results offer some good news: “The ghettoes of the Northeast and Midwest are not being reconstituted in the fast-growing areas of the South and West,” he notes.


U.S. cities with high levels of growth and new construction tend to be less segregated. One reason may be that newer housing lacks a reputation for discrimination, while in older areas the perception—widely held or not—that blacks are unwelcome in a particular suburb or area of the city can linger for decades. Metro areas in the South and West tend to have more mobile populations, fewer blacks, and sometimes no long-established black community, Iceland points out. “The world of difference is easy to see in a short drive through Milwaukee or Detroit where there are starkly black and starkly white neighborhoods that you don’t see to nearly the same extent driving in places like Tucson or Las Vegas,” he says.


The way city boundaries were drawn in the past also plays a role in regional differences today, according to Reynolds Farley, a University of Michigan sociology professor emeritus, who began studying segregation in the 1960s. The boundaries of the Rustbelt cities of the Northeast and Midwest were established decades ago and are surrounded by independent suburbs, some with a history of hostility to blacks. In the South and West, central cities annexed outlying land after World War II; metro areas like Tucson include much of what might be considered the suburban ring in the Midwest. In some parts of the West and South, public schools are often organized on a county-wide basis, limiting white suburban enclaves.


By the 1990s, more African Americans were calling the suburbs home, but in many places those suburbs were predominantly black. “The important finding revealed by Census 2010 is that many, many places within suburban rings in the Northeast and Midwest appear to be quite open to African American residents,” says Farley. “You can find almost all-black neighborhoods, but such segregation is certainly declining. In quite a few metro areas in the South and West and some in the Midwest, you could say that black-white segregation is not much more than moderate.”


Researchers are now tracking the impact of growing Hispanic and Asian populations on black-white segregation. “As a community becomes more diverse, racial mixing appears easier to achieve and a different dynamic seems to be playing out,” says Iceland.


Gradual, Steady, but Limited Progress


Black-white segregation has declined gradually and continuously over the last 40 years, but as the nation’s population has become more diverse some analysts anticipated more rapid change, even hoping for a breakthrough.


“The growth of the black middle class, the passage of time since Fair Housing Laws were enacted, and the evidence from surveys that white Americans are becoming more tolerant of black neighbors all point toward progress in overcoming the high level of segregation that had been reached in 1970,” says Brown University sociology professor John Logan.


A report from the US2010 Census Project, directed by Logan, found overall black-white residential segregation in U.S. metropolitan areas declined from a high of 79 in 1970 to 59 in 2010 (as measured by the segregation index).1 Logan calls the progress “mixed,” noting that at the current rate of change it will be 2030 before blacks reach the same level of segregation as Hispanics today (index of 48). The report also found that cities with the largest share of black residents registered the smallest declines in segregation over the past 30 years, while metro areas with black populations of less than 5 percent showed the greatest declines.


One cost of residential segregation for African Americans is quality of life. The neighborhoods where they live typically have fewer resources and higher poverty than neighborhoods where comparable non-Hispanic whites live, according to the US2010 Census Project.2 The average black household earning more than $75,000 was in a neighborhood with a higher poverty rate than the average white household earning less than $40,000.


Children More Segregated Than Adults


For children, segregation continues to be more pronounced than for adults. The Harvard School of Public Health’s Diversity Data project reports that segregation declined moderately for black children in most U.S. metropolitan areas since 2000, but remains high.3 Their analysis of 2010 Census data found that black child segregation relative to white children in the 100 largest metropolitan areas fell between 2000 and 2010 from 72 to 68, as measured by the segregation index. Child segregation declined most in larger, very highly segregated metros in the Midwest and smaller metros in Florida and the western United States, the researchers found.


“In very few instances do the very best neighborhoods where black and Hispanic children live have opportunities and amenities close to the average level of neighborhoods where white children live,” write the Diversity Data project authors.




Farley calls “residential segregation a lens to assess whether the U.S. has achieved the equality that some felt the 2008 election symbolized.”4 He points to declines in segregation, increases in interracial marriages, documented changes in racial attitudes, and widespread acceptance of equal housing opportunities as signs of weakened systemic discrimination.


Yet he acknowledges that while white attitudes have become more accepting over the past 30 years, full acceptance is a long way off. About half of whites surveyed in Detroit in 2004 told Farley’s research team they would move if the racial composition of their neighborhoods reaches 50-50, down from three-quarters in 1976. Still, he is cautiously optimistic: “The long trend toward lower levels of black-white segregation seems sure to continue.”




    1. John R. Logan and Brian Stults, “The Persistence of Segregation in the Metropolis: New Findings from the 2010 Census,” Project US2010 Census Brief (March 2011), accessed at www.s4.brown.edu/us2010, on Aug. 20, 2011.


    1. John Logan, “Separate and Unequal: The Neighborhood Gap for Blacks, Hispanics, and Asians in Metropolitan America,” Project US2010 Report (July 2011), accessed at www.s4.brown.edu/us2010/Data/Report/report0727.pdf, on Aug. 30, 2011.


    1. Nancy McArdle et al., “Segregation Falls for Black Children in Most Metro Area but Remains High; Fewer Metros Experience Declines for Latinos,” Diversity Data Issue Brief (July 2011), accessed at http://diversitydata.sph.harvard.edu/Publications/Child_Segregation_Issue_Brief_July_2011.pdf, on Aug. 1, 2011.


    1. Reynolds Farley, “The Waning of American Apartheid?” Contexts 10, no. 3 (2011), accessed at www.psc.isr.umich.edu/pubs/pdf/rp617.pdf, on Aug. 30, 2011.



Offshoring U.S. Labor Increasing

(October 2008) Offshoring is the movement of jobs and tasks from one country to another, usually from high-cost countries, such as the United States, to low-cost countries where wages are significantly lower. Offshoring is often confused with outsourcing, which is instead the movement of jobs and tasks from within a company to a supplier firm. The offshoring of manufacturing jobs has been occurring for decades, but the offshoring of service-sector jobs is an incipient phenomenon, emerging in substantial numbers since 2002 and growing rapidly.

Which Jobs and How Much

While there is widespread interest in measuring offshoring, available government data have significant limitations, making it nearly impossible to get an accurate picture of its scale and scope.1 The table below shows the results of an exploratory study by Princeton University economist Alan Blinder that attempts to fill this void. He estimates the 10 most vulnerable occupations, where U.S. workers in these jobs now face competition from overseas workers. Blinder estimates that about 30 million jobs, accounting for a little more than one-fifth of the U.S. workforce, are vulnerable to offshoring.

Occupations Most Vulnerable to Offshoring

wdt_ID Rank Occupation Annual Mean Wage Number Employed
1 1 Computer programmers 72,010 394,710
2 2 Data entry keyers 26,350 286,540
3 3 Electrical and electronics drafters 51,710 32,350
4 4 Mechanical drafters 46,690 74,260
5 5 Computer and information 100,640 28,720
6 6 Actuaries 95,420 18,030
7 7 Mathematicians 90,930 3,160
8 8 Statisticians 72,150 72,150
9 9 Mathematical science occupations (all other) 61,100 6,930
10 10 Film and video editors 61,180 17,410

Sources: Alan S. Blinder, “How Many U.S. Jobs Might Be Offshorable?” CEPS Working Paper 142 (March 2007); and Bureau of Labor Statistics, National Occupational Employment and Wage Estimates, May 2007 (www.bls.gov/oes/current/oes_nat.htm,accessed May 28, 2008).

Most studies identify whether the work can be done remotely and whether it can be easily reduced to a set of written rules and procedures as determining the likelihood that a job or activity may be transferred to another country. An occupation that requires being physically present with a customer is less vulnerable because it cannot be done remotely. Work which requires judgment combined with a deep understanding of the customer’s cultural context is difficult to do remotely because it cannot be easily written into a set of rules and protocols.

One important finding of many of the forecasts is that a large share of vulnerable jobs pay high wages and require advanced education (as shown in the table), making it more difficult to predict the overall impact of offshoring on the U.S. economy and to devise appropriate policy responses.

Why Offshore

Firms use offshore jobs to reduce costs. A typical accountant in India earns about $5,000 per year, whereas a U.S. accountant earns about $63,000.2 These large wage differentials make it very attractive for companies to lower costs by substituting U.S. workers with lower-cost overseas workers. As the CEO of a major technology company put it, “If you can find high quality talent at a third of the price, it’s not too hard to see why you’d do this [send jobs offshore].”3 By lowering costs through offshoring, firms can gain a business advantage over their competitors.

Some factors that influence offshoring are driven by markets while others are based on government intervention. Companies selling to an overseas market sometimes find it easier to use local workers to customize a product because they better understand the tastes of the customers. Also, the markets in many emerging countries with a burgeoning new consumer class, such as India and China, are growing at three to four times the rate of markets in developed countries in North America and Europe. In other cases, governments are actively pursuing offshore outsourcing of U.S. and European jobs by offering an array of incentives, such as tax holidays (where the firm pays no income or property taxes), new facilities at reduced rates, and training subsidies. And some countries require the transfer of technology and high-wage jobs as a condition for selling in their markets.

U.S. government tax and immigration policies are actually speeding up offshoring. U.S.-based multinational corporations that outsource work offshore receive tax breaks.4 And offshore outsourcing firms have exploited loopholes in U.S. immigration policy, particularly in the H-1B and L-1 guest worker visas, to facilitate the transfer of work overseas.5

Major changes in technology and social norms have enabled offshoring. Technological breakthroughs in telecommunications, the Internet, and collaborative software tools have dramatically lowered the costs of doing business remotely and across borders.

Additionally, shifts in employment relations and norms have made it much easier for firms to substitute foreign workers for U.S. workers.

Which Industries and Where

Information technology (IT) services was the first industrial sector to move a significant number of jobs offshore. Labor costs, which are often 70 percent of the net cost for IT firms, make the sector ripe for offshoring. Other information-intensive sectors, such as insurance and financial services, are aggressively offshoring. While not well publicized, occupations in a wide variety of other sectors (for example, journalism, law, medicine, and animation) are also moving offshore.

India has been the major beneficiary of white-collar offshoring from the United States, but almost every other developing country is trying to replicate India’s success. India has many advantages, including its large English-speaking educated workforce, its large diaspora living in the United States and the U.K., and its specialization in IT. Western Europe is about three to five years behind the United States in offshoring due to language barriers and greater protection for their domestic workers. But this phenomenon is growing in importance both economically and politically there as well.


Ron Hira is an assistant professor of public policy at Rochester Institute of Technology and co-author of Outsourcing America (AMACOM, 2008).

For More Information

This article appears in: Marlene A. Lee and Mark Mather, “U.S. Labor Force Trends,” Population Bulletin 63, no. 2 (2008).


  1. Timothy J. Sturgeon, “Why We Can’t Measure the Economic Effects of Services Offshoring: The Data Gaps and How to Fill Them,” Services Offshoring Working Group Final Report, (Cambridge, MA: Industrial Performance Center, Massachusetts Institute of Technology, Sept. 10, 2006).
  2. India wage based on author’s estimates. U.S. wage source: Bureau of Labor Statistics, National Occupational Employment and Wage Estimates, May 2007.
  3. Brian Jackson, “EDS Says Offshoring Great for Profitability, Promises to Continue,” ITBusiness.ca (Canada), April 23, 2008.
  4. Kimberly A. Clausing, “The Role of U.S. Tax Policy in Offshoring,” in Brookings Trade Forum: Offshoring White-Collar Work,  ed. Lael Brainard and Susan M. Collins (Washington, DC: Brookings Institution Press, 2006).
  5. Ron Hira, “Outsourcing America’s Technology and Knowledge Jobs: High-Skill Guest Worker Visas Are Currently Hurting Rather Than Helping Keep Jobs at Home,EPI Briefing Paper 187 (March 28, 2007).

The Social and Economic Isolation of Urban African Americans

(October 2005) Hurricane Katrina’s devastation in late August of much of the northern Gulf Coast followed by the slow institutional response to the crisis exposed the impoverishment and disempowerment of many African Americans. The media images of a predominantly African American population left to fend for itself in New Orleans demonstrated to many surprised observers the enduring color line in that city.

But striking disparities between urban blacks and whites in the United States are hardly unique to New Orleans. In large cities across the nation, African Americans are much more likely than whites to be living in communities that are geographically and economically isolated from the economic opportunities, services, and institutions that families need to succeed. These disparities have left African Americans disproportionately vulnerable to the next urban calamity, be it from terrorism or another natural disaster.

No Job, No Car, No Phone: An Entrapping Lack of Basic Resources

Of the 15 U.S. metropolitan areas with the most African Americans in absolute numbers in 2000, New Orleans had the highest black poverty rate, at 33 percent.1 But racial differences in poverty were stark in each of these metropolitan areas except New York. In Chicago, Newark, Memphis, and St. Louis, African Americans were about five times more likely than whites to be impoverished.

Higher poverty rates for African Americans are also linked to lower levels of education and employment—key elements in attaining economic well-being. In 2000, blacks in these large cities were also far less likely to own a car or a phone, and they were on average younger and more often female than their white counterparts.

Education. Nationwide, about 75 percent of African Americans age 25 or older do not have a college diploma, and 80 percent lacked college degrees in all but two of the 15 largest U.S. metropolitan areas—Washington, D.C. and Atlanta. Whites were more than twice as likely to be college graduates in a dozen of these cities, with the largest disparities (2.5 times) in Memphis, New York City, and Philadelphia.

Employment. One-third to one-half of African American males age 16 or older in the largest 15 U.S. cities were not employed in 2000. Some of these were “discouraged” workers who have left the labor force after numerous unsuccessful attempts to secure a job. In Chicago, Detroit, Philadelphia, Los Angeles-Long Beach, New Orleans, and St. Louis, only about one-half of African American males were employed (see table). Blacks in these cities were three-quarters as likely as whites to have a job.

Cars and phones. African Americans are also much more likely than whites to lack basic amenities—such as an automobile or a telephone—that facilitate economic mobility and that many Americans take for granted (see table). In each of the 15 largest U.S. metropolitan areas except New York (where many residents do not have personal transportation), African Americans were about three times as likely as whites to not have an automobile in 2000. In a dozen of these areas, African Americans were at least three times more likely than whites to not have a telephone, with the racial gap in telephone ownership being eight-fold in Newark and Chicago.

Age and sex ratio. African Americans in major U.S. cities are often younger and more likely to be female than their white urban counterparts. Sex ratios (the number of males per 100 females) as of 2000 were approximately 95 or higher among whites in 11 of the 15 largest metropolitan areas, while they were about 85 or lower among African Americans in 10 of the 15 localities. The relative absence of African American males in U.S. cities reflects their high mortality and incarceration rates—factors that weigh heavily in their social and economic entrapment.

Selected Demographic and Socioeconomic Characteristics Among African Americans and Whites in Selected U.S. Metropolitan Areas, 2000

Metropolitan area
civilian males
16+ employed
no automobile
no phone
Afr. Amer.
Afr. Amer.
Afr. Amer.
Los Angeles-Long Beach
New Orleans
St. Louis

Source: Census 2000 5% Public Use Microdata Sample (PUMS).

The Extreme Isolation of the African American Poor

African American Percentage of Total Population and Poverty Population in Selected U.S. Cities, 2000


Source: Census 2000 5% Public Use Microdata Sample (PUMS).

Not surprising, poor urban African Americans exhibit even greater levels of social and economic isolation in the United States than the general black population, even when compared with poor urban whites:

  • In all but two of the 15 largest U.S. metropolitan areas as of 2000, the presence of African Americans in the poverty population was 1.5 times greater than their representation in the cities’ overall populations (see figure). In six of the cities—St. Louis, Chicago, Detroit, Philadelphia, Baltimore, and Newark—African Americans were twice as likely to be part of the poverty population relative to their percentage of the total population of these areas.
  • Vulnerable populations such as African American children and the elderly were similarly overrepresented among the poor in major U.S. cities in 2000. In all but two of the 15 largest U.S. metro areas, the percentage of African American elderly who were poor was twice their percentage of the city’s total elderly population. The faces behind these percentages were made vivid by the stories and numbers of elderly blacks trapped in houses and nursing homes as the floodwaters from Katrina rose in New Orleans.
  • In all but two of the 15 largest African American metro areas, at least 40 percent of poor blacks did not own a car in 2000, with upwards of 60 percent lacking a vehicle in New York City, Baltimore, Philadelphia, and Newark. And poor blacks were often twice as likely as poor whites to lack a telephone in these cities.
  • White poor people were more than three times as likely as the African American poor to be college graduates in every city studied except Atlanta. Only 25 percent of the cities’ poor African American males on average had a job.

Reducing the Risk of Future Disasters for Urban African Americans

African Americans not only have the highest levels of poverty in the country, but they are also the group that is most residentially segregated from and least likely to intermarry with whites. Surveys also continue to reveal that many nonblack Americans express high levels of social distance (the degree to which people desire close or remote social relations with members of other groups) from African Americans.2 Given their limited social and economic resources along with their geographic isolation, poor urban African Americans—especially children and the elderly—are disproportionately vulnerable to being left behind during a crisis situation.

What measures need to be taken to improve the social and economic position of African Americans and to avoid future disasters such as the recent one in New Orleans?

  • Skills-development, employment, and health-maintenance programs need to be targeted to and strengthened for African Americans.
  • Funding and access to education—including Head Start—should be increased for African Americans in order to bolster their social and economic well-being and competitiveness in the labor market.
  • Additional policies, resources, and investment are needed to promote the development and relocation of businesses (and thus jobs) to African American urban neighborhoods.
  • Government agencies responsible for responding to natural disasters need to factor into their planning the economic and geographic isolation of African Americans—especially the African American urban poor.

Aggressive actions are needed to erase the marginalization of African Americans that Hurricane Katrina exposed. The failure to take such actions will have enormous economic and social costs—not just for African-Americans, but for a society living with a disjuncture between its ideals and the reality of continued stratification along the color line.3

Rogelio Saenz is a professor of sociology at Texas A&M University and author of “Latinos and the Changing Face of America,” in The American People: Census 2000, ed. Reynolds Farley and John Haaga (New York: The Russell Sage Foundation, 2005).nbsp;


  1. Data from the Census 2000 5% Public Use Microdata Sample (PUMS) are used to examine the standing of African Americans (relative to whites) in the 15 most populous African American Primary Metropolitan Statistical Areas (PMSAs) and Metropolitan Statistical Areas (MSAs). The statistics are based on the populations of the central cities and the suburbs comprising each PMSA and MSA.
  2. Tom Smith, Intergroup Relations in a Diverse America: Data from the 2000 General Social Survey (New York: The American Jewish Community, 2001).
  3. I acknowledge the helpful comments of Karen Manges Douglas and David Geronimo Embrick on an earlier draft of this report.