1-21f-racial_inequality-death

U.S. Racial Inequality May Be as Deadly as the Coronavirus

The mortality rate for Black Americans in non-pandemic years is higher than the mortality rate for white Americans who died from COVID-19 and all other causes in 2020.

Coronavirus Pandemic Temporarily Shortens Average U.S. Lifespan by About a Year

While the pandemic is shortening the average U.S. lifespan—temporarily—its effects will be felt most heavily by Black Americans, whose mortality rate in ordinary years is higher than the rate for white Americans during the pandemic. Each coronavirus-related death will likely impact about nine close family members.

These impacts are among the findings of new research supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) that aims to put the pandemic’s magnitude in context and inform responses.

Extreme Racial Inequality in COVID-19 Deaths Layers on Top of Existing Stark Disparities

Black Americans experience a higher mortality rate every year than white Americans are experiencing during the coronavirus pandemic, finds Elizabeth Wrigley-Field of the University of Minnesota.Her analysis focuses on death rates and compares the scale of this pandemic to racial inequality, which she calls “another U.S. catastrophe.”

Using demographic models, Wrigley-Field estimates how many deaths of white Americans would be needed to raise the white age-adjusted mortality rate to the best-ever (lowest) Black age-adjusted rate.

At least 400,000 excess deaths of white Americans—deaths above and beyond the number expected in a non-pandemic year—would be needed to reach the best mortality rate ever recorded for Black Americans, which occurred in 2014, she finds.

Black Americans’ age-adjusted, confirmed COVID-19 deaths are more than 2.5 times higher than that of white Americans, she reports.2

Social factors rather than innate vulnerabilities drive these mortality differences: Mounting research suggests these stark disparities are driven by differences in exposure to the coronavirus. In particular, Black Americans are overrepresented in service jobs with high public contact and are particularly overrepresented among low-paid workers who may lack the power to demand adequate protection.3

For white mortality to reach levels that Black Americans experience outside of the pandemic, excess mortality in 2020 for white Americans would need to increase by 5.7 times the level of cumulative COVID-19 mortality reached in July 2020 (when the research findings were published), Wrigley-Field reports.

Final analysis of 2020 is likely to reveal “a deadly pandemic causing a spike in mortality for whites that nevertheless remains lower than the mortality Blacks experience routinely, outside of any pandemic,” she suggests.

This disparity in mortality rates has an impact on life expectancy during the pandemic as well. For white Americans, life expectancy in 2020 will remain higher than life expectancy for Black Americans has ever been unless nearly 700,000 excess white deaths occur, Wrigley-Field finds.4

“If Black disadvantage operates every year on the scale of whites’ experience of COVID-19, then so too should the tools we deploy to fight it,” she argues. “Our imagination should not be limited by how accustomed the United States is to profound racial inequality.”

COVID-19 Expected to Shorten the Average U.S. Lifespan in 2020

With the U.S. population as a whole experiencing nearly 350,000 COVID-19 deaths in 2020 and more to come in 2021, life expectancy may appear to be plummeting.5

But in estimating the magnitude of the pandemic, demographers at the University of California, Berkeley have found that COVID-19 is likely to shorten the average U.S. lifespan in 2020 by about a year.6

In July 2020, demographers Ronald Lee and Joshua Goldstein calculated the consequences of U.S. lives lost to COVID-19 that year in order to put COVID-19 mortality rates into historic, demographic, and economic perspective. They used two scenarios: One based on a projection of 1 million deaths for the year, the other on 250,000 deaths, which is closer to the current estimate of 345,700 deaths by Johns Hopkins University.7

One million deaths in 2020 would cut about three years off the average U.S. life expectancy, they conclude, while 250,000 deaths would reduce lifespans by about 10 months.

That said, without the societal efforts implemented to lessen COVID-19’s impact, 2 million deaths were projected by the end of 2020—a reduction of the average U.S. lifespan by five years, the researchers point out.

Their estimated drop in life expectancy is modest, in part because 250,000 deaths is not a large increase on top of the 3 million non-COVID-19 deaths expected for 2020. The study also notes that older people, who typically have fewer remaining years of life than others do, represent the most COVID-19 fatalities.

Still, while COVID-19 mortality rates in general remain lower than those of the 1918 Spanish flu pandemic, the toll of the coronavirus in the United States could be just as devastating as the country’s longer-lasting HIV and opioid epidemics if mitigation efforts fail, the researchers said.

“The death toll of COVID-19 is a terrible thing, both for those who lose their lives and for their family, friends, colleagues, and all whom their lives touched. Those are real people, not abstract statistics,” says Lee.

“But the population perspective helps put this tragedy in a broader context. As we work to contain the coronavirus, it is important to know that the United States has been through such mortality crises before,” he adds.

About Nine Close Relatives Suffer Grief With Each COVID-19 Fatality

The ripple effects of each COVID-19 death will impact the mental and physical health of about nine surviving close family members, a study of kinship networks shows.8

For example, when 190,000 people were dead from the disease in September 2020, 1.7 million Americans experienced the loss of a close relative, explains Ashton Verdery of Penn State University. A kinship network includes grandparents, parents, siblings, spouses, and children. Black Americans had a slightly higher number of close relatives than white Americans, averaging an estimated 9.2 people compared with 8.9, they found.

If 1 million people eventually die from COVID-19, then 8.9 million—or about 3 out of 100 Americans—would be in mourning.

These findings can help raise awareness about the scale of the disease and the ripple effects that deaths may have on a community, as well as prepare officials and business leaders to manage those effects, according to Verdery.

“It’s very helpful to have a sense of the potential impacts that the pandemic could have,” he says. “And, for employers, it calls attention to policies around family leave and paid leave. At the federal level, it might inform officials about possible extensions for FMLA (Family and Medical Leave Act). There could also be some implications for caretaking. For example, a lot of children grow up in grandparent-led houses and they would be impacted.”

Many people are also facing the loss of a close loved one at a younger age because of the disease, according to Verdery, who worked with Emily Smith-Greenaway of the University of Southern California, Rachel Margolis of the University of Western Ontario, and Jonathan K. Daw at Penn State.

“There are a substantial number of people who may be losing parents that we would consider younger adults and a substantial number of people may be losing spouses who are in their 50s or 60s,” he suggests.

Their findings could help local officials understand and prepare for the waves of grief that may affect specific geographic areas and regions of the country.


This article was produced under a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Yasmin Anwar of the University of California Berkeley and Matt Swayne of Penn State University contributed to this article. The work of researchers from the following NICHD-funded Population Dynamics Research Centers was highlighted: University of California, Berkeley (2P2CHD073964-05A1); University of Minnesota (5P2CHD041023-19); and Penn State University (5P2CHD041025-19).




A list of newly published research on the pandemic by NICHD Population Dynamics Research Centers can be found here.



References

  1. Elizabeth Wrigley-Field, “U.S. Racial Inequality May Be as Deadly as COVID-19,” Proceedings of the National Academies of Sciences 117, no. 36 (2020): 21854-6.
  2. Centers for Disease Control and Prevention, “COVID-19 Hospitalization and Death by Race/Ethnicity,” updated Nov. 30, 2020, https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html.
  3. Noreen Goldman et al., “Racial and Ethnic Differentials in COVID-19-Related Job Exposures by Occupational Status in the US,” MedRxiv (2020), https://doi.org/10.1101/2020.11.13.20231431.
  4. This study does not examine life expectancy for non-Hispanic Black Americans and non-Hispanic White Americans separately by sex. PRB notes that in 2017, non-Hispanic Black females had a longer life expectancy (78.1 years) than non-Hispanic White males (76.1 years). Data on life expectancy are from Kenneth D. Kochanek et al., “Deaths: Final Data for 2017,” National Vital Statistics Reports 68, no. 9 (2019).
  5. Johns Hopkins University, Coronavirus Resource Center, accessed on Jan. 4, 2021, https://coronavirus.jhu.edu/us-map.
  6. Joshua R. Goldstein and Ronald D. Lee, “Demographic Perspectives on the Mortality of COVID-19 and Other Epidemics,” Proceedings of the National Academies of Sciences 117, no. 36 (2020): 22035-41.
  7. Johns Hopkins University, Coronavirus Resource Center, accessed on Jan. 4, 2021, https://coronavirus.jhu.edu/us-map.
  8. Ashton M. Verdery et al., “Tracking the Reach of COVID-19 Kin Loss With a Bereavement Multiplier Applied to the United States,” Proceedings of the National Academies of Sciences 117, no. 30 (2020): 17695-701.
Pouring Cola

Taxes, Health-Warning Labels May Help Limit Consumption of Sugary Beverages and Improve Health

To combat obesity and diabetes, lawmakers in a number of U.S. cities have taxed sodas, sports drinks, and sweetened tea, and many are now considering health warning labels.

Growing evidence suggests that both strategies—taxes and warning labels—can reduce the purchase and consumption of sugary drinks, research supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) shows.

Health-Warning Labels Influence What People Buy and Consume

Even brief exposure to health warnings on sugar-sweetened beverages reduces purchases of those beverages, providing evidence that such warnings can promote healthier drink choices, a new study demonstrates.1

A team of researchers from the University of North Carolina at Chapel Hill (UNC)—including Anna Grummon, Lindsey Smith Taillie, Shelley Golden, Marissa Hall, and Noel Brewer—examined how health warnings influence what consumers actually buy in real settings. This randomized controlled trial assigned 400 consumers of sugary beverages to groups that saw either a health warning or a label that looked like a barcode.

“We worked in a convenience-store laboratory that allowed us to control whether the sugary drinks had warnings,” explains Grummon, now at Harvard University. “We are also one of the first studies to measure what consumers actually buy after seeing warnings, when they have their own money on the line.”

Participants who saw the health warning labels purchased about 22% fewer calories from sugary drinks compared with participants who saw a neutral label. The study also found that the warnings were influential across diverse groups: The effect of health warnings on beverage purchases did not differ by participants’ race/ethnicity, education, age, gender, sexual orientation, income, body weight, or health-literacy level.

According to Grummon, critics of health warning labels argue that consumers won’t notice or pay attention to the warnings. However, three-quarters of the participants in this study reported noticing the health warnings, and most of those participants also reported that they read and looked closely at the labels.

In another study, Grummon and Hall synthesized the findings of 23 studies and found that health warnings labels not only reduced purchases of sugary drinks but also caused stronger emotional responses, increased perceptions that sugary drinks contribute to disease, and reduced intentions to purchase or consume sugary drinks.2 All these responses are key indicators when it comes to long-term behavior change, they note.

“Our findings suggest that sugary drink warnings help consumers better understand products’ healthfulness and encourage them to make healthier choices about what drinks to buy,” says Grummon.

In a related mathematical simulation, UNC researchers show that a national policy requiring health labels on sugar-sweetened beverages could reduce obesity prevalence by about 3.1 percentage points over five years, if sustained.3

“While three percentage points might sound modest, on a national scale it equates to more than five million fewer people with obesity,” says Grummon. “Warnings are a highly scalable strategy for helping consumers make healthier choices. These findings suggest that warnings are also promising for addressing obesity in the U.S.”

Improved Child Health Projected in Wake of Mexico’s Soda Tax

The Mexican government enacted the first national tax on sugar-sweetened beverages after a 2012 study indicated that more than 70% of the country’s population was overweight or obese, and that in excess of 70% of the added sugar calories in the Mexican diet were coming from sugary drinks.

In the two-year period spanning 2014 to 2015, a research team that included Barry M. Popkin and Shu Wen Ng of UNC found that:

  • The one-peso-per-liter excise tax on sugar-sweetened beverages in Mexico resulted in a 6% reduction in purchases of taxed beverages during the first year and continued to decline, with a 10% decrease in purchases in the second year.
  • During the same study period, purchases of untaxed beverages such as bottled water increased 2.1%.
  • Residents of households with lower socioeconomic levels, for whom health care costs are most burdensome, reduced their purchases of sweetened beverages the most.4

The findings run counter to initial reports from the sugar-sweetened soda industry, which said that the purchases of sugary drinks actually went up after the initial tax year. However, the researchers found those reports did not account for numerous significant factors, including inflation and shifts in population.

In addition, a new analysis co-authored by Popkin estimates that Mexico’s sugar-sweetened beverage tax could result in meaningful weight control and life-long health benefits for the country’s children and adolescents, particularly those who had been high consumers of the beverages before the tax.5 Childhood obesity is a strong predictor for obesity later in life, which can also lead to chronic illnesses such as diabetes, hypertension, and heart disease, the researchers emphasize.

To estimate the one-year effect of the tax on the body weight of children ages 5 to 17, by taking into account patterns of childhood growth and obesity in Mexico and assuming that the known reductions in sugar-sweetened beverage purchases would reflect changes in consumption.

Findings show that one year after the implementation of the current tax, children and adolescents should experience an average reduction in body weight of 0.26 and 0.61 kg (one kilogram equals about 2.2 pounds). For those who had been high consumers of sugary drinks, the team estimates the positive impact on body weight would be even greater, with an average body weight reduction of 0.50 kg for children and 0.87 kg for adolescents. Sustained over several years, such weight loss could mean some children and adolescents would not longer be considered obsese.

“Taxation represents one of the most effective ways to reduce consumption of unhealthy sugar-sweetened beverages, which can make a meaningful impact on future excessive weight gain and significantly reduce the long-term risks of becoming obese,” says Popkin. “If the taxation revenue is used to support child and adolescent healthy eating, then the benefits of such taxes are enhanced.”

Public Support Is Key to Policies Limiting Sugary Beverages

For taxes on sugary beverages to become a widely used strategy for improving public health, public support and acceptance are key.

Public opinion on the policies’ unintended consequences may affect attitudes toward the policy, argue Melissa Knox, Jessica Jones-Smith, and Vanessa Oddo of the University of Washington, who analyzed perceptions of the effects of Seattle’s 2017 sugary beverage tax.6

“We find that a majority of participants (59%) support the sugary beverage tax in Seattle and correspondingly, most people believed that the tax will positively impact health, and will not negatively affect general and personal economics in Seattle,” they report. “However, lower-income, versus higher-income, respondents were more concerned about the possible negative economic consequences of the tax,” such as job loss or increased financial costs for their family and friends.

A related study shows that attitudes toward sugary beverage taxes may be difficult to accurately estimate in phone surveys.7 Phone respondents (but not web respondents) under-report their sugary beverage consumption by about 25% and over-report positive attitudes toward the tax by about 11%, the researchers determined. These differing results likely reflect respondents’ answering interviewers’ questions in ways they believe are more socially desirable or acceptable rather than choosing responses that reflect their true thoughts or feelings, a tendency known as social desirability bias.

The researchers offer advice to lawmakers implementing soda taxes.

  • Policymakers “should be wary of solely relying on self-reported measures of intake when evaluating the effectiveness of these policies,” they write, noting that consumers may consume more sweetened beverages than they report.
  • Lawmakers should strengthen “their public messaging regarding the health and economic benefits of sweetened beverage taxes, even if they believe that attitudes are generally positive. Without a pro-tax messaging campaign that informs the public about the positive health and economic effects of these taxes, the taxes may eventually lose public support.”

The researchers point out that “recent successful efforts to block U.S. municipalities from enacting future beverage taxes by banning the taxes at the state level have relied heavily on informational campaigns that focused on the negative economic effects of the taxes. These campaigns, often funded by the beverage industry, may ultimately shift social norms in the direction of more favorable attitudes toward sweetened beverages, with unpredictable effects on public health.”


This article was produced under a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The Communications and Marketing team at the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill contributed to this article. The work of researchers from the following NICHD-funded Population Dynamics Research Centers was highlighted: University of North Carolina at Chapel Hill (P2CHD050924) and University of Washington (5P2CHD042828-18).


 

References

  1. Anna H. Grummon et al., “Sugar-Sweetened Beverage Health Warnings and Purchases: A Randomized Controlled Trial,” American Journal of Preventive Medicine 57, no. 5 (2019): 601-10.
  2. Anna H. Grummon and Marissa G. Hall, “Sugary Drink Warnings: A Meta-Analysis of Experimental Studies,” PLOS Medicine (2020), https://doi.org/10.1371/journal.pmed.1003120.
  3. Anna H. Grummon et al., “Health Warnings on Sugar-Sweetened Beverages: Simulation of Impacts on Diet and Obesity Among U.S. Adults,” American Journal of Preventive Medicine 57, no. 6 (2019): 765-74.
  4. M. Arantxa-Colchero et al., “In Mexico, Evidence of Sustained Consumer Response Two Years After Implementing a Sugar-Sweetened Beverage Tax,” Health Affairs 36, no. 3 (2017): https://doi.org/10.1377/hlthaff.2016.1231
  5. Rossana Torres-Álvarez et al., “Body Weight Impact of the Sugar-Sweetened Beverages Tax in Mexican Children: A Modeling Study,” Pediatric Obesity 15, no. 8 (2020): e12636, https://doi.org/10.1111/ijpo.12636.
  6. Vanessa M. Oddo et al., “Perceptions of the Possible Health and Economic Impacts of Seattle’s Sugary Beverage Tax,” BMC Public Health 19 (2019): 910.
  7. Melissa A. Knox et al., “Is the Public Sweet on Sugary Beverages? Social Desirability Bias and Sweetened Beverage Taxes,” Economics & Human Biology 38 (2020): 100886.
New_1120f-cohabiting

Cohabiting Couples in the United States Are Staying Together Longer but Fewer Are Marrying

More unmarried couples today are living together, and doing so for longer than in the past, but fewer of these relationships lead to marriage, new research finds. This change may in part reflect shifting attitudes toward cohabitation, and it results in more separations and re-partnering during young adulthood.

Most young women today will live with a romantic partner at least once, compared with just one-third of young women in the late 1980s.1 During that decade, most cohabiting relationships were short-lived and frequently led to marriage.

The new research, conducted by graduate students and faculty at the Center for Family and Demographic Research at Bowling Green State University, examined how cohabitation and marriage patterns have changed for young women over the past four decades. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

In their study, Esther Lamidi, now at the University of Colorado Colorado Springs, and colleagues Wendy Manning and Susan Brown at Bowling Green, drew on data from the National Survey of Family Growth (NSFG) to compare women ages 15 to 39 who lived with a first romantic partner in 1983-1988 and in 2006-2013.They examined changes in whether couples who lived together had married or split up within five years.

They found that while cohabiting relationships are still relatively short-lived, couples today are cohabiting longer—increasing from about 12 months in the 1983-1988 cohabitation cohort to 18 months in the later cohort—and that this longer duration is linked to couples delaying or forgoing marriage altogether. After five years, similar shares of women in both cohorts were still living with their partner, but the distribution of those still cohabiting as compared to those who had married had shifted. Among the early cohort, 23% of women were still cohabiting five years later, and 42% had married their partner. These shares were reversed among the later cohort—43% were still cohabiting and only 22% had married.

Women With Less Education Experience More Changes in Cohabitation

Over the past five decades, changes in family behaviors such as declining rates of marriage have been more pronounced among women with less education compared with women who have more education. Lamidi and her colleagues confirmed this divergence—similar to what’s been observed in other family behaviors and frequently termed “diverging destinies”—when they examined patterns of cohabitation across different sociodemographic groups.

Their analysis found that the more recent cohort was much less likely to marry their cohabiting partner, and while this pattern was observed across all sociodemographic groups, it occurred more frequently among women with less education.

After accounting for women’s educational attainment, their results show that between the two cohorts only women with less than a college education experienced a decline in marrying their cohabiting partner. In addition, women having one or more children while cohabiting—an occurrence more common among women with less education—delayed or inhibited marriage more for the later cohort than the earlier cohort, they found.

Cohabitation Changes Reveal a Widening Social Class Divide

Sociodemographic characteristics are associated with the pathways out of cohabitation—break ups or marriages—and changes among the cohabiting population’s characteristics can be reflected in changes in cohabitation outcomes. Yet while the researchers noted that the cohabiting population grew in size, became more racially and ethnically diverse and more highly educated, and had more births while living together, they found these compositional changes had little impact on the changes in cohabitation outcomes across the two cohorts.

What does this finding mean? The researchers conclude that the limited impact of population composition changes on cohabitation outcomes, combined with the decline in marrying a cohabiting partner among women with less education, suggests that the social class divide in the American family appears to be widening.

Their findings also “diminish the traditional view of cohabitation as a prelude to marriage” for women with less education and show, particularly for this population, that “cohabitation is increasingly serving a role similar to that of traditional marriage in offering a viable context for childbearing and child-rearing.”

Young Women Today Are Increasingly Likely to Experience a Breakup

Although cohabiting relationships may be lasting longer, they remain relatively unstable. Kasey Eickmeyer, now at the Center for Policing Equity, reports, “Millennials experienced more relationship instability during young adulthood than earlier birth cohorts of women.” She found that cohabitation experience accounted for this instability.

Eickmeyer asked whether young women see their intimate live-in relationships (either marriage or cohabitation) end more frequently today than earlier generations.3 She analyzed data from multiple cycles of the NSFG to examine women’s experience of ending marriages and cohabiting relationships when they were ages 18 to 25 across several five-year birth cohorts from 1960 to 1985.

She found that among women who had ever married or cohabited, the share breaking up with a live-in partner increased from 31% among women born between 1960 and 1964 to 44% among women born in 1985 to 1989.

Cohabitation explains this increasing likelihood of experiencing a breakup. Compared to women in the 1985-1989 birth cohort, women in the earlier birth cohorts from 1960-1964 through 1975-1979 were significantly less likely to have one or more live-in partnerships end. Once Eickmeyer accounted for women’s cohabitation experience, she found that young women’s increased likelihood of having an intimate partnership end is because union formation during young adulthood shifted from marriage—a relatively stable union—to cohabitation, a relatively unstable union.

More Breakups and Re-Partnering in Young Adulthood Suggest Changing Attitudes About Cohabitation

As more young women enter into and end cohabiting relationships, they have more opportunities to live with multiple partners in a pattern of serial cohabitation. The growing practice of serial cohabitation reflects in part changing attitudes about couples living together without marriage.

Eickmeyer and Wendy Manning wanted to know whether contemporary young adult women who had ever cohabited are more likely to re-partner than prior cohorts of young women.4 Using data from the 2002 and 2006-2013 NSFG, they compared the cohabitation experience of young women ages 16 to 28 across five-year birth cohorts beginning in 1960 through 1980 to examine trends in serial cohabitation.

They found that early Millennial women (born 1980-1984) were 53% more likely to live with more than one romantic partner during young adulthood compared with the late Baby Boomers (born 1960-1964), even after taking into account sociodemographic characteristics such as race and ethnicity and educational level, and relationship characteristics such as their age when their first cohabiting relationship ended and whether they had children.

Not only were early Millennial women more likely to live with more than one partner without marriage, they also formed subsequent cohabiting relationships more quickly than the late Baby Boomers—dropping from nearly four years between live-in relationships to just over two years.

The characteristics most strongly associated with serial cohabitation—such as identifying as non-Hispanic white, having less than a college education, and growing up with a single parent—remained stable across birth cohorts, Eickmeyer and Manning found. And, much like the cohabiting population, the composition of women who had previously lived with a partner changed across cohorts, but this shift does not explain the increase in serial cohabitation.

The researchers conclude that the increase stems from more young adults cohabiting, the continued instability of cohabiting relationships, the increasing length of time between first cohabitation and first marriage, and the growing acceptance of cohabitation during young adulthood.

Their findings highlight the instability in many contemporary young adults’ lives and the increasing role cohabitation plays in relationship churning. Although multiple live-in romantic relationships could have negative consequences for young adults’ well-being (and any children they may have), Eickmeyer and Manning suggest “that young adult relationships may be evolving, and young women may be learning to end coresidential relationships that are not working.”


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 NICHD-funded population dynamics research center at Bowling Green State University (P2CHD050959) was highlighted in this article.


References

  1. Paul Hemez and Wendy D. Manning, Twenty-Five Years of Change in Cohabitation in the U.S., 1987-2013, National Center for Family and Marriage Research Family Profiles, No. FP-17-02 (2017), http://www.bgsu.edu/ncfmr/resources/data/family-profiles/hemez-manning-25-years-change-cohabitation-fp-17-02.html
  2. Esther O. Lamidi, Wendy D. Manning, and Susan L. Brown, “Change in the Stability of First Premarital Cohabitation Among Women in the United States, 1983-2013,” Demography, 56 (2019): 427-50.
  3. Kasey J. Eickmeyer, “Cohort Trends in Union Dissolution During Young Adulthood,” Journal of Marriage and Family 81 (2019): 760-70.
  4. Kasey J. Eickmeyer and Wendy D. Manning, “Serial Cohabitation in Young Adulthood: Baby Boomers to Millennials,” Journal of Marriage and Family 80 (2018): 826-40.
Crowds in a stadium with cots.

Disasters Raise Risk of a Homeless Undercount in 2020 Census

Counting people experiencing homelessness is not an easy task during any census, and the coronavirus pandemic, wildfires, and hurricanes have made the process even more complicated in 2020.

Despite rigorous efforts by the Census Bureau to count everyone in the United States, many people experiencing homelessness are likely to be missed in the 2020 Census count. The Census Bureau had already identified people experiencing homelessness as a hard-to-count population because they have more difficulty accessing the typical modes of responding to the survey—by internet, phone, or mail—than people with a permanent address.1 Then the pandemic’s effects on everyday life delayed census field operations. The rescheduled dates to count people experiencing homelessness—Sept. 22-24, 2020—place count efforts in the middle of peak wildfire and hurricane seasons. This confluence of crises magnifies the challenge of conducting a complete and accurate count of an often-overlooked population.

Counting People Where They Sleep

The goal of the 2020 Census is to count everyone once, only once, and at their “usual residence.” Usual residence is defined as the location where a person lives and sleeps most of the time. If people do not have a permanent home, they should be counted at the location where they are on Census Day, April 1, 2020. For people experiencing homelessness, their location on Census Day may be unsheltered (including living in a vehicle), at a shelter or transitional housing, at a hotel or motel, or doubled up in another household.

People who are doubled-up on Census Day should be counted in the household where they are staying on that day, and the census questionnaire includes prompts to help ensure everyone is counted. But the Census Bureau has identified the doubled-up population as hard-to-count because, despite prompts in the questionnaire, they may still be missed.

For people staying at shelters and those who are unsheltered, the Census Bureau aims to count people at the locations where they receive services, like soup kitchens, shelters, and mobile food vans, and at targeted outdoor locations like parks, under bridges, at bus depots, and other areas. To identify such count locations, the Census Bureau uses a combination of internet research and outreach to local elected officials and advocacy organizations.

Due to the coronavirus pandemic, however, both service-based and outdoor count operations were delayed. Service-based enumeration, originally scheduled for March 30 to April 1, has been delayed to September 22-24. The outdoor count, originally scheduled for April 1, will now take place from September 23-24. Because of these delays, people will no longer be counted where they were on April 1 but rather where they are staying in September. This shift—and the reasons for it—raise questions: Are the same service locations open? Are the same people using those services? Are people avoiding services to reduce their risk of exposure to the coronavirus that causes COVID-19, making them even harder to enumerate? All of these issues complicate the count of people experiencing homelessness in 2020.

Counting During Crises Heightens the Risk of Undercount

The shift in operations has also moved the count into peak hurricane and wildfire seasons. Just as census enumerators are preparing to count people outdoors and at service-based locations, serious natural disasters are ravaging parts of the nation. Wildfires have led to hazardous air quality across much of the West Coast, forcing many people to stay indoors. Hurricane Sally, which hit the Gulf Coast on Sept. 16, 2020, has severely damaged infrastructure across several states. People who would normally be outdoors may not be in their “usual” locations, and services may be dramatically disrupted in areas that have been affected by disasters.

The closure of libraries and other community facilities further compounds the challenge of counting. If people experiencing homelessness are missed in service-based and unsheltered enumeration, they can still self-respond to the census through September 30. Public facilities such as libraries, schools, and community centers were expected to serve as outreach locations for 2020 Census self-response. At libraries, for example, people without access to the internet or a computer could learn about the census and use a computer to respond online. However, due to the coronavirus pandemic, many of these facilities are closed. Even if a facility is open, people may avoid public places to lower their risk of contracting the coronavirus.

Communities Could Lose Crucial Funding For Services

Official estimates of homelessness in the United States range from 568,000 to more than 1.5 million people (and both are underestimates). Given those staggering numbers, the Census Bureau faces a major challenge in enumerating this large, hard-to-count population. And because of ongoing natural disasters and the economic effects of the coronavirus pandemic, it’s likely that the number of people experiencing homelessness has increased in 2020.

The 2020 Census will be used to reapportion congressional seats, redraw voting districts, and allocate more than $1 trillion in federal funds each year—including funds that assist people experiencing homelessness. Data from the 2020 Census will be used by governments and nonproft organizations to determine needs for roads, hospitals, and other public services. An undercount of people without a permananent address would reduce funds for programs that serve the United States’ most vulnerable residents. Such an undercount will be magnified in local areas with large numbers of people experiencing homelessness and in areas where the count will be most disrupted. Communities hit the hardest by the pandemic and natural disasters could lose crucial funding.

As one of several vital nationwide operations destabilized by the coronavirus pandemic, wildfires, and hurricanes, the 2020 Census faces more challenges than usual in counting people experiencing homelessness. But a complete and accurate count is vital to supporting these populations and the communities that serve them.

References

1U.S. Census Bureau, “Counting the Hard to Count in a Census,” Select Topics in International Censuses, July 2019, https://www.census.gov/content/dam/Census/library/working-papers/2019/demo/Hard-to-Count-Populations-Brief.pdf

New_0820f-incarceration

When a Parent Is Incarcerated, Partners and Children Also Pay a Price

“We live in a country where we have huge numbers of children exposed to parental incarceration. When we talk about the need to reform the criminal justice and mass incarceration systems, we also need to talk about the unintended victims of the current system,” says Christine Leibbrand of the University of Washington. “Incarceration exposes families to poverty and disadvantage, and the system can self-perpetuate inequality.”

About 3.5% of U.S. children under age 18—or one child in every classroom of about 29 students—had a parent behind bars in 2015, mainly their fathers.1

Black children were more than five times more likely than white children to be separated from a parent by incarceration, report sociologists Bryan Sykes of University of California, Irvine and Becky Pettit of University of Texas at Austin. These patterns reflect a system that disproportionately imprisons disadvantaged and minority men, they argue.

A growing body of research documents the toll U.S. incarceration takes on the families of those imprisoned, widening disparities and exacerbating existing disadvantages. New research supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development provides further evidence on the wide-ranging ways a parent’s incarceration shapes the lives and life chances of their partners and children, from the neighborhoods where they live to the levels of adversity their children experience.

Children of Incarcerated Fathers Are More Likely to Live in High-Poverty Neighborhoods and Move More Often

Children whose fathers were incarcerated move more frequently and live in neighborhoods that are more socioeconomically disadvantaged than their peers whose fathers have never been in prison, find Leibbrand and Erin Carll of the University of Washington, Angela Bruns of the University of Michigan now at Gonzaga University, and Hedwig Lee of Washington University in St. Louis.2

Using data from the national Fragile Families and Child Wellbeing Study—research following thousands of families in 20 large U.S. cities since 1998—the team examined the neighborhoods of children whose fathers were in prison or recently released. Families with a father currently or recently in prison tend to live in neighborhoods with higher percentages of residents who are single mothers, receive public assistance, lack a high school diploma, and live below the poverty line, they show.

The financial hardship families with imprisoned members face, researchers say, perpetuates what they call “downward mobility.” A father in prison is one less wage earner at home or paying child support. Families with limited income have fewer choices of where to live, they may move often, and the neighborhoods they end up in may be marked by lower quality schools, greater unemployment, and higher rates of crime and violence, Leibbrand and her colleagues report.

“When we think about where people live or move to, we think of people weighing the pros and cons of different places. That’s far too simple. Many families may be forced to move because of eviction or budget constraints, for example, and these forced moves are often to worse neighborhoods where families have little choice of where they would like to live,” says Leibbrand.

Mothers With a Partner in Prison Are More Likely to Hold Multiple Jobs

Mothers with incarcerated partners are more likely to work multiple jobs than women in otherwise similar circumstances, finds Bruns in another study.3

Partner incarceration is linked to additional employment—a third shift—on top of the paid work and caregiving women already do, she finds, based on analysis of Fragile Families and Child Wellbeing Study data.

An additional job may cover basic expenses but also compounds the burden that women with incarcerated partners already shoulder, she points out.

“Staying in touch and supporting an inmate—responding to his requests for food, clothing and books, preparing packages to the correctional institution’s specifications, coordinating family member visits, and keeping up with legal cases and appeals—can feel like a second job in and of itself,” explains Bruns.

Mothers with partners who are incarcerated usually have sole responsibility for children who may be “struggling with the absence of their fathers,” according to Bruns. Holding multiple jobs is also a known stressor that could raise mothers’ risk of stress-related health conditions.

Low-skilled women are often stuck in low-wage, dead-end jobs that can barely pay the bills, she asserts.

“Balancing multiple work roles in addition to family member incarceration may keep women from going to school or participating in other activities that improve their socioeconomic standing over the long-term,” writes Bruns.

Youth With a Parent in Prison Face More Trauma and Adversity

Youth ages 11 to 17 who experience the incarceration of a parent are more likely to have behavioral problems or mental health issues than their counterparts whose parents have never been jailed, Samantha J. Boch, Barbara L. Warren, and Jodi L. Ford of Ohio State University show.4

The team finds that household poverty plays a role, as does the number of traumatic events the young person has experienced, including homelessness, eviction, foster care, and serious injury or death in the family. Overall, they find that youth who deal with the incarceration of a parent experience three times as many adverse childhood experiences (ACEs) as their unaffected peers.

The researchers base their analysis on interviews with more than 600 parents or other caregivers participating in the Adolescent Development in Context study, a representative sample of Columbus, Ohio, and its surrounding suburbs.

The behavioral problems and mental health issues exhibited more frequently in children who experience a parent’s incarceration include poor attention, excessive anxiety, and externalizing behaviors such as rule breaking, temper outbursts, and property destruction, the analysis finds.

The researchers examined a wide-ranging set of 30 ACEs that includes aspects of financial distress and household churning or instability such as changes in household composition (for example, when a parent or parent’s new partner leaves or joins the household or when a child goes to live with grandparents) and residential moves.

“Well-documented research investigating the cumulative effect of ACEs indicates that youth exposed to parental incarceration may have a much greater likelihood for engaging in maladaptive coping behaviors (such as cigarette, alcohol, and illicit drug use, or violent delinquent behaviors) and experiencing depression, anxiety and post-traumatic stress disorder across the lifespan,” the researchers report.

They argue that mental health providers should view a parent’s incarceration as an important consideration of the child’s and family’s well-being that warrants continued observation, support, and follow-up. More research is needed to determine the best ways to screen and identify these youths using non-stigmatizing approaches that build on their strengths, they suggest. 

A Parent’s Incarceration Can Shape a Child’s Identity and Influence Anti-Social Behavior

Among young adults with an incarcerated parent, those who had a high need for parental approval were more likely to identify themselves as a troublemaker or partier during young adulthood than those who were emotionally independent, a recent study finds.5

Self-identities influence behavior, including criminal activity, making understanding the precursors of self-identity important to interventions designed to improve the life prospects of children with incarcerated parents, according to the researchers Jessica G. Finkeldey of the State University of New York at Fredonia, and Monica A. Longmore, Peggy C. Giordano, and Wendy D. Manning of Bowling Green State University.

The team examined publicly available incarceration records and analyzed data from the Toledo Adolescent Relationships Study, a regional survey of more than 900 men and women ages 18 to 28 interviewed five times between 2001 and 2011.

Developing “high emotional independence, or values, beliefs, and identities in contrast to and separate from an incarcerated parent,” may set young adults on a path shaped by different choices than those made by their incarcerated parent, the researchers suggest.

“It is possible that exposing children of incarcerated parents to positive role models and mentors, such as through mentorship programs, might help to reduce the transmission of antisocial identities and behaviors and should be investigated,” says Finkeldey.


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 dynamics research centers was highlighted in this article: University of Washington, University of Michigan, Ohio State University, Bowling Green State University, and University of Texas at Austin.


 

References

  1. Bryan L. Sykes and Becky Pettit, “Measuring the Exposure of Parents and Children to Incarceration,” in Handbook on Children with Incarcerated Parents, ed. J. Mark Eddy and J. Poehlmann-Tynan, (Geneva: Springer, 2019): 11-23.
  2. Christine Leibbrand et al. “Barring Progress: The Influence of Parental Incarceration on Families’ Neighborhood Attainment,” Social Science Research 84 (2019): 102321
  3. Angela Bruns, “The Third Shift: Multiple Job Holding and the Incarceration of Women’s Partners,” Social Science Research 80 (2019): 202-15.
  4. Samantha J. Boch, Barbara L. Warren, and Jodi L. Ford, “Attention, Externalizing, and Internalizing Problems of Youth Exposed to Parental Incarceration,” Issues in Mental Health Nursing 40, no. 6 (2019): 466-75.
  5. Jessica G. Finkeldey et al. “Identifying as a Troublemaker/Partier: The Influence of Parental Incarceration and Emotional Independence,” Journal of Child and Family Studies 29, no. 3 (2020): 802-16.
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.


 

References

  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.
new_0420f-pandemic-covid-b

How Demographic Changes Make Us More Vulnerable to Pandemics Like the Coronavirus

The world is better equipped to fight a pandemic today than it was in 1918, when influenza swept the globe and infected up to one-third of the world’s population.1 While science and medical advances have given us new advantages in fighting disease, some demographic trends since 1918 may increase the risk for spreading contagions and our vulnerability to viruses.

Population Mobility Allows Viruses to Spread Quickly Around the World

The sheer volume of today’s population movements—from migration and travel as people fly within and across countries—may make it faster and easier for viruses to spread around the world.

During the 1918 flu pandemic, the airline industry was not yet established, and overseas travel was expensive and time consuming. Even with these limitations, the flu spread across multiple countries in a matter of months, mainly because of the large number of military personnel returning home from World War I (1914-1918).2

Since then, flight has become so established in our society that it’s commonplace. By 2018, the global airline industry had 4.3 billion scheduled passengers, up from 1.3 billion passengers in 1995. Similarly, international tourist visits globally have nearly doubled in the past decade, from 936 million visits in 2009 to 1.8 billion visits in 2018.3

The increased volume of travel—both international and domestic—presents a significant challenge to stopping or slowing the spread of the coronavirus and COVID-19, the disease caused by the virus. China’s first reported COVID-19 case occurred in December 2019, and Thailand reported its first confirmed case on Jan. 13, 2020. One week later, by January 20, South Korea and Japan also reported their first cases.4 More than 20 countries reported COVID-19 cases by the end of that month.5

As of April 7, 2020, more than 1.5 million cases of COVID-19 were confirmed across 183 countries and territories.6 For many of these countries, their initial cases developed from travelers from China (such as in Thailand, South Korea, Japan, and Italy) or, later, from travelers from other countries then experiencing an outbreak. Several countries in Europe reported their first cases originating with travelers from Italy, which has been one of the first and hardest hit countries in Europe.7

In the United States, the epicenter for the pandemic is New York City, a densely populated megacity with many international travelers. The first confirmed case in the city came from a traveler who had returned to the city from overseas.8

Population Density in Urban Areas Can Heighten Disease Transmission

As data from tracking the coronavirus shows, once a virus arrives somewhere, it can spread quickly within populations. One key factor to this spread is urbanization.

Population density has generally increased because of the quadrupling in the global population since the 1920s to the current 7.7 billion, but we’re more concentrated in urban areas today than we were back then—more than half the world’s population lives in urban areas.9 Between 1920 and 2020, the percent of the world population estimated to be living in urban areas increased from around 20% to 56%.10 This growth has been largest in Asia and Africa, where their shares are currently 51% and 41% respectively, around three times their percentages in 1950.11

The number of large cities has also soared: 548 cities had 1 million or more people in 2018, a nearly 50% increase from 371 cities in 2000, and a more than seven-fold increase from 77 cities in 1950. In 1950, only New York and Tokyo with their surrounding metropolitan areas were considered megacities, with a population of at least 10 million. By 2018, this number increased to 33, with 27 megacities in developing countries. China is home to six megacities, and India has five megacities. All except one of the 10 cities projected to become megacities between 2018 and 2030 are in developing countries.

The growth in and location of megacities is significant when considering public health crises like those posed by the coronavirus pandemic. While population density facilitates the spread of diseases even in developed countries, rapid urbanization in developing countries—often without proper planning—can result in unsafe crowding and areas with concentrated poverty (slums). This overcrowding and lack of infrastructure for sanitation and hygiene provide prime conditions for the spread of many diseases. Air pollution in large cities also weakens lung function, which may make residents more vulnerable to COVID-19.12

As population growth and urbanization continue, they generate another way in which a virus may spread more easily: As human settlements expand, animal habitats often shrink or are destroyed, increasing the risk of humans coming into contact with new animal-born disease vectors, as scientists believe is the case for the coronavirus (and is the case with many epidemics).

Population Aging Means More Older People Are at Risk of Infection

While the demographic changes in population movement and urbanization may make it easier for viruses to spread, population aging makes us more vulnerable to them. The number of older adults ages 60 and older globally has increased five-fold from 202 million to 1.05 billion between 1950 and 2020, while the total population increased three-fold.13 Coupled with declines in fertility rates during the same period, the result is a rapid aging of the global population—the share of the older population ages 60 and older has increased from 8% in 1950 to 13.5% in 2020.14

The likelihood of becoming severely ill with COVID-19 appears to be greater among those ages 60 and older. While people of all ages can become infected with the coronavirus, older adults may be more at risk partly because they’re more likely to have one or more noncommunicable diseases reported to increase vulnerability, such as cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases, PRB reports.

The age pattern of mortality indicates that places with more older adults are likely at risk of higher rates of severe illness or death than places with younger populations, if all else remain the same, PRB researchers explain. The case fatality rate from COVID-19 in China among those ages 80 and older is reported to be six times higher than the overall case fatality rate for that country.15

Italy’s population has been particularly hard hit by the coronavirus. It began with three cases in people who had traveled from China. Although social distancing is considered to have helped slow the spread in Italy, the country has suffered a significant number of deaths—nearly 18,000 as of this writing—partly because Italy’s population ranks as the oldest in Europe.

Sixteen percent of Italy’s population is ages 85 and older. That segment of the population—those age 85 and older, the group often referred to as the oldest-old—is the fastest-growing age group globally, increasing from less than 5 million people to 64 million people between 1950 and 2020.

The first confirmed case of COVID-19 in the United States was found with a man returning from Wuhan, China, to the state of Washington. It then quickly spread to elderly residents living in close quarters at a nearby nursing home, many of whom have underlying conditions and weakened immune systems. The disease claimed the lives of 35 people less than one month after the first patient was transferred to a hospital.

Demographic Changes Must Be Considered When Evaluating New Health Threats

These facts illustrate how demographic trends in the global mobility of populations, urbanization and population density, and longevity and population aging have likely made us more vulnerable to the coronavirus pandemic and pandemics in general.

Such demographic changes are important to consider as countries evaluate their risks for experiencing pandemics like the coronavirus or other infectious diseases and work to develop new ways to protect their populations. For example, decisionmakers can identify the areas with populations most vulnerable to a disease and increase stocks of protective gear like masks and hand sanitizers; medical supplies such as ventilators, surgical gowns, gloves, and face shields; and hospital beds.

Policymakers may also want to consider policies such as paid sick leave and enhanced unemployment benefits for wage workers in service industries and other industries where workers come into regular contact with the public. These policies will both cut down on the spread of the virus and ease the financial burden on individuals and the economic impact on society.

References

 

  1. Centers for Disease Control and Prevention, “History of the 1918 Flu Pandemic,” https://www.cdc.gov/flu/pandemic-resources/1918-commemoration/1918-pandemic-history.htm.
  2. “Influenza Pandemic of 1918-19,” Encyclopedia Britannica, updated March 20, 2020, https://www.britannica.com/event/influenza-pandemic-of-1918-1919.
  3. Smithsonian National Air and Space Museum, “America by Air: Airline Expansion and Innovation 1927-1941” (2007), https://airandspace.si.edu/exhibitions/america-by-air/online/innovation/innovation15.cfm.
  4. World Health Organization, “Novel Coronavirus (2019-nCoV) Situation Report – 1,” Jan. 21, 2020, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
  5. World Health Organization, “Novel Coronavirus (2019-nCoV) Situation Report – 12,” Feb. 1, 2020, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
  6. Johns Hopkins University & Medicine, “COVID-19 Map,” Coronavirus Resource Center (2020), https://coronavirus.jhu.edu/map.html.
  7. Derrick Bryson Taylor “A Timeline of the Coronavirus Pandemic,” The New York Times, updated April 2, 2020, https://www.nytimes.com/article/coronavirus-timeline.html.
  8. Carl Zimmer, “Most New York Coronavirus Cases Came From Europe, Genomes Show,” The New York Times, April 8, 2020, https://www.nytimes.com/2020/04/08/science/new-york-coronavirus-cases-europe-genomes.html.
  9. United Nations, Department of Economic and Social Affairs (UNDESA), Population Division, 2018 World Urbanization Prospects (2018), https://population.un.org/wup/.
  10. UNDESA, “Growth of the Urban and Rural Population, 1920-2000,” Population Studies, no. 44 (1969), https://population.un.org/wup/Archive/Files/studies/United%20Nations%20(1969)%20-%20Growth%20of%20the%20World%27s%20Urban%20and%20Rural%20Population,%201920-2000.pdf; UNDESA, Population Division, 2019 World Population Prospects (2019), https://population.un.org/wpp/.
  11. UNDESA, 2018 World Urbanization Prospects.
  12. Xiao Wu et al., “Exposure to Air Pollution and COVID-19 Mortality in the United States,” medRxiv, April 7, 2020, https://doi.org/10.1101/2020.04.05.20054502.
  13. UNDESA, 2019 World Population Prospects.
  14. UNDESA, 2019 World Population Prospects.
  15. Nurith Aizenman, “Why the Death Rate From Coronavirus Is Plunging in China,” NPR, All Things Considered, March 3, 2020, https://www.npr.org/sections/goatsandsoda/2020/03/03/809904660/why-the-death-rate-from-coronavirus-is-plunging-in-china.

 

Cheerful multi-ethnic friends having fun in party

Changing Race and Ethnicity Questions on the U.S. Census Form Reflect Evolving Views

Census questions about race and ethnicity have evolved over time, as have Americans’ views about racial and ethnic identification. Nearly a century ago, enumerators for the 1920 Census were instructed to identify people as “White,” “Black,” “Mulatto,” “Chinese,” “Japanese,” “American Indian,” “Filipino,” “Hindu” (Asian Indian regardless of religion), or “Other.”1 Enumerators’ personal observations, rather than individuals’ self-identification, determined most racial/ethnic classification through the 1950 Census.

The Census Bureau emphasizes that current race categories “reflect a social definition of race … not an attempt to define race biologically, anthropologically, or genetically.”2 The 2020 Census questionnaire will ask respondents to identify their race and whether they are of Hispanic origin in two separate questions. A majority of U.S. Hispanics are Hispanic and white under the federal government’s definitions, but many Hispanics do not distinguish between race and ethnicity in this way.3 In 2010, 37 percent of Hispanics marked the “Some Other Race” category to express their racial identification—for example, Mexican or Salvadoran—which for them has more meaning than the race categories on the census form (such as white, black, Asian, etc.).

To improve the accuracy of census data, Census Bureau staff tested a single question that combined the race and Hispanic ethnicity questions and allowed respondents to report more than one category (Hispanic and White, for example).4 Results from this 2015 test showed that Hispanics were significantly more likely to identify as Hispanic rather than choose the “Some Other Race” category. The White House’s Office of Management and Budget, however, has chosen to continue to use the two-part question on race and Hispanic origin for the 2020 Census.

City University of New York sociologist Richard Alba is critical of the two-question format because it categorizes “young people with mixed Hispanic and white origins only as Hispanic—and therefore ‘non-white’ in census terminology.”5 Research shows that most of these young people perceive and experience themselves as part of the white majority and are treated as such, he reports. In Alba’s view, this classification overstates the minority share of the population, ignoring the assimilation process.

How race is categorized has important political and social implications, argue Dowell Myers and Morris Levy of the University of Southern California.They measured whites’ attitudes toward demographic change after reading different randomly assigned versions of news articles describing the Census Bureau’s population projections. Whites who read an article emphasizing the decline of the white majority reported much higher levels of anxiety and anger than whites who read about the enduring white majority as a result of intermarriage and inclusive racial/ethnic identity—racial and ethnic categories that permit people to appear in more than one group. Those who read about the declining white majority were less likely to express support for immigrants or favor a hypothetical property tax increase for K-12 education than those who read about the enduring white majority.


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


 

References

  1. U.S. Census Bureau, Fourteenth Census of the United States, January 1, 1920: Instructions to Enumerators (Washington, D.C.: Government Printing Office, 1919), www.census.gov/history/pdf/1920instructions.pdf.
  2. U.S. Census Bureau, Questions Planned for the 2020 Census and American Community Survey (Washington, D.C.: U.S. Census Bureau, 2018).
  3. D’Vera Cohn, “Seeking Better Data on Hispanics, Census Bureau May Change How It Asks About Race” (April, 20, 2017), www.pewresearch.org/fact-tank/2017/04/20/seeking-better-data-on-hispanics-census-bureau-may-change-how-it-asks-about-race/.
  4. Cohn, “Seeking Better Data on Hispanics, Census Bureau May Change How It Asks About Race.”
  5. Richard Alba, “There’s a Big Problem With How the Census Measures Race,” Washington Post (February 6, 2018), www.washingtonpost.com/news/monkey-cage/wp/2018/02/06/theres-a-big-problem-with-how-the-census-measures-race/?utm_term=.96c240c6289d; and Richard Alba, “What Majority-Minority Society? A Critical Analysis of the Census Bureau’s Projections of America’s Demographic Future” Socius 4, no. 1 (August 30, 2018).
  6. Dowell Myers and Morris Levy, “Racial Population Projections and Reactions to Alternative News Accounts of Growing Diversity,” Annals of the American Academy of Political and Social Sciences 677, no. 1 (2018): 215-28.
USA, Vermont, Montpelier

The U.S. Population Is Growing at the Slowest Rate Since the 1930s

The pace of U.S. population growth is slowing, according to the Census Bureau’s 2018 estimates and 2020 projections, which provide a preview of 2020 Census results.

The U.S. population has increased each decade since the first census was conducted in 1790, surpassing 50 million by 1880, 100 million by 1920, and 200 million by 1970. The 2010 Census was the first head count in which the U.S. population exceeded 300 million. However, the rate of population growth from one decade to the next has declined since 2000 (see Figure 1).

The U.S. population increased by 10 percent between 2000 and 2010 and is projected to increase by 8 percent between 2010 and 2020, from 309 million to 333 million. An 8 percent gain would be the smallest percentage increase in the U.S. population between censuses since the 1930s; the projected numerical increase of 24 million people would be the smallest gain since the 1980s. Yet, between 2010 and 2018, the U.S. population only increased by 6 percent. Unless the rate of population growth increases over the next two years, the United States may not reach the Census Bureau’s projected population size in 2020.

Figure 1. The U.S. Population is Increasing but the pace of growth is slowing

U.S. Population and Percentage Increase in Population Between Census Years, 1790 to 2060


Source: U.S. Census Bureau, decennial censuses, and vintage 2018 population estimates

Growth in the number of households has also slowed, and population growth is on track to outpace household growth this decade for the first time since the 1930s. Between 2000 and 2010, the number of households increased by 11 percent, but household growth rates declined during the Great Recession of 2007 to 2009 and the slow economic recovery that followed. Between 2010 and 2017, the number of households increased by only 3 percent. For the household growth rate to equal the Census Bureau’s projected population growth rate of 8 percent, the number of households would have to increase by almost 6 million between 2017 and 2020. This level of growth seems unlikely given that the number of households only increased by 3.3 million over the seven-year period from 2010 to 2017. If the number of households continues to increase at the current average annual rate until 2020, the total increase for the decade is more likely to be around 4.8 million, representing a growth rate of only 4 percent—less than half the rate for the 2000 to 2010 period.

In the long term, slower population and household growth could negatively affect the future U.S. economy by reducing the supply of workers, the tax base, and the demand for goods and services. This slowdown could also reduce demand for new home construction and lead to declines in home values.

Rapid Growth Continues in the South and West

Although U.S. population growth has slowed, the rate of growth has been uneven across regions and states. The most recent estimates show that the South’s population grew 9 percent between 2010 and 2018, with the West right behind at 8 percent. Conversely, the population grew just 2 percent in the Midwest and 1 percent in the Northeast. Regional and state population trends are important not only from a demographic and economic perspective, but also because they affect the balance of political power in Congress. State population totals from the 2020 Census will determine how many congressional seats each state will have over the next decade, starting in January 2023 when the 118th Congress takes office.

Florida, with an estimated 21.3 million residents, has surpassed New York (population 19.5 million) as the nation’s third-largest state behind California and Texas. Between 2010 and 2018, 19 states (plus the District of Columbia) grew faster than the national average, and all but two (North Dakota and South Dakota) were in the South and West. In nine of those states and the District, the resident population increased by more than 10 percent.

Among the states, Utah, Texas, Florida, Colorado, and North Dakota grew the fastest between 2010 and 2018. North Dakota’s rapid population gains are linked to the oil boom earlier in this decade.1 The boom, however, has shown signs of slowing in recent years: Between 2016 and 2018, the state’s population growth rate was just under 1 percent—slightly below the national average (1.3 percent) and well below the 4 percent growth rate of Idaho, Nevada, and Utah.

The population declined in three states between 2010 and 2018: Connecticut, Illinois, and West Virginia. West Virginia’s population has declined every year since 2013, while the other two states have experienced net population loss each year since 2014. In addition, Alaska, Hawaii, Louisiana, Mississippi, New York, and Wyoming had fewer residents in 2018 than in 2016.

The post-2010 demographic situation is especially bleak in Puerto Rico. Between 2010 and 2018, Puerto Rico lost more than half a million residents, or 14 percent of its 2010 population. The rate of loss in the U.S. territory is nearly six times that of West Virginia, the state with the steepest population loss. Puerto Rico’s population decline is the result of both a financial crisis that first hit the territory in 2006 and the devastation wreaked by Hurricane Maria in 2017.2

These divergent population trends since 2010 have been even more pronounced at the county level. Between 2010 and 2018, nearly one-fifth of the nation’s 3,142 counties and county equivalents grew at or above the national rate of 6 percent; 340 of these counties grew 10 percent or more (see Figure 2). In contrast, more than half of U.S. counties (about 1,650) have experienced net population loss over the same period, with roughly 550 counties losing at least 5 percent of their residents. Most of the counties in the latter group started experiencing a net loss of residents as far back as the 1940s, and many have been declining in population since before the Great Depression.

Figure 2. The Fastest Growing Counties Are Located in the South and West

County Population Change, April 1, 2010 to July 1, 2018


Source: U.S. Census Bureau, vintage 2018 population estimates.

Counties in large metropolitan areas (1 million population or more) saw the largest population gains. As a group, their populations increased 8 percent between 2010 and 2018, and nearly half of them grew faster than the national average.

In contrast, noncore counties—those located outside metropolitan and micropolitan areas—have been the biggest demographic losers since 2010.3 Noncore counties as a group had a net loss of about 2 percent of their population between 2010 and 2018. While noncore counties comprise 42 percent of all U.S. counties, they accounted for 58 percent (967 of 1,656) of the counties that lost population.

Counties with diversified economies and access to recreational activities (entertainment industries or natural amenities) have also fared much better than those dependent on agriculture or manufacturing.


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).

References

  1. Mark Mather and Beth Jarosz, “U.S. Energy Boom Fuels Population Growth in Many Rural Counties” (March 28, 2014), accessed at www.prb.org/us-oil-rich-counties/, on Feb. 22, 2019.
  2. Mary Williams Walsh, “How Puerto Rico Is Grappling With a Debt Crisis,” The New York Times, May 16, 2017, www.nytimes.com/interactive/2017/business/dealbook/puerto-rico-debt-bankruptcy.html; and Edwin Meléndez and Jennifer Hinojosa, Estimates of Post-Hurricane Maria Exodus from Puerto Rico (New York: Center for Puerto Rican Studies, 2017).
  3. Metropolitan statistical areas have at least one urbanized area of 50,000 or more residents. Micropolitan statistical areas have at least one urban cluster of at least 10,000 but less than 50,000 residents.
3 generations on couch looking at tablet

A household is defined by the U.S. Census Bureau as all the people who occupy a single housing unit, regardless of their relationship to one another.

One person in each household is designated as the householder—the person, or one of the people ages 15 or older, in whose name the housing unit is owned, being bought, or rented. The relationships of all other household members are defined only in relation to the householder and then used to group households into different types. The two primary types are family households and nonfamily households.

Family households have a householder and one or more additional people who are related to the householder by marriage, birth, or adoption. Any children under age 18 who are the biological, adopted, or stepchildren of the householder are classified as “own children.” Family households include married couples with and without children under age 18, single-parent households with children, and other groupings of related adults such as two siblings sharing a housing unit or a married couple whose adult child has moved back home. Family households can also include additional people who are not related to the householder, such as a boarder.

Nonfamily households have a householder who lives alone or who shares the housing unit only with nonrelatives, such as roommates or an unmarried partner. Unmarried partner households can be either family or nonfamily households depending on which partner is designated as the householder and whether any additional household members are related to the householder. If an unmarried couple has a biological child together, then their household would be considered a single-parent family even though such a child would actually be living with both biological parents. However, if a child is related to only one partner of an unmarried couple, then the household can be either a single parent family or a nonfamily household depending on which partner is arbitrarily designated as the householder.

Census Relationship Categories Have Changed Over Time

Although the decennial census has always defined household types based on the relationships of household members to the householder, the number of possible relationships has expanded over time. In 1960 and 1970, respondents were asked to identify the “Head of the Household,” and in married couple households, only the husband could be designated as the “Head.” Response categories included “Wife of Head,” but not “Husband of Head.” Beginning in 1980, the term “Head of the Household” was replaced with “Person 1,” defined as the household member or one of the members in whose name the home is owned or rented. Response categories were also changed to include “Husband or Wife of Person 1”.1

With the rise in cohabitation in the 1980s, the 1990 Census was the first to include “Unmarried Partner” as a possible relationship to Person 1, in addition to “Housemate, roommate”. The 1990 form also added foster child to the “Roomer, boarder” category, and included “Grandchild” as a separate relationship type for the fi rst time. The 2000 Census listed “Foster child” as a separate relationship type, and although this category was excluded from the 2010 Census, it will be available again in the 2020 Census.

With the legalization of same-sex marriage by the U.S. Supreme Court in 2015, the 2020 Census will include “Same-sex husband/wife/spouse” and “Same-sex unmarried partner” relationship categories for the first time.2Separate categories will also be provided for “Opposite-sex husband/wife/spouse” and “Opposite-sex unmarried partner.” No changes will be made that would help clarify or consistently classify the appropriate household type for unmarried partners with children.


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).

References

1 U.S. Census Bureau, Decennial Census Questionnaires and Instructions, www.census.gov/programs-surveys/decennial-census/technical-documentation/questionnaires.html.

2 U.S. Census Bureau, Questions Planned for the 2020 Census and American Community Survey (Washington, D.C.: U.S. Census Bureau, 2018).