Group of Elderly or old age People

Oldest Areas May Be Hardest Hit: The Importance of Age Structure in Understanding Mortality During the Coronavirus Pandemic

While we have much to learn yet about the novel coronavirus SARS-CoV2, and the COVID-19 disease it causes, evidence to date suggests that deaths among people who have tested positive for the coronavirus are highest at older ages and near zero for young children. Higher mortality rates at older ages may be associated with the increased prevalence of chronic conditions at older ages, such as cardiovascular diseases, diabetes, and chronic respiratory diseases. These chronic conditions appear to be associated with more severe illness and worse patient outcomes. The age pattern of mortality means that areas with higher proportions of older adults are likely at risk of higher rates of severe illness or death than those with younger populations.

Many factors may affect the intensity of a COVID-19 outbreak in a given country, including underlying health conditions in the population, the effectiveness of government response, and the availability of health care resources. Age structure (the share of the total population in each age group) alone cannot tell us which countries will be hardest hit in the pandemic but can provide important context in understanding and responding to the crisis. If two countries have the same age-specific mortality rates from COVID-19, the country with an older population would have more deaths per 1,000 people—a higher crude death rate—from the disease than the country with the younger population.

For example, social distancing measures in Italy may have helped that country reduce transmission of the disease, but its high proportion of older adults, in combination with high case fatality rates (proportion of the confirmed cases with COVID-19 that were fatal) at older ages has contributed to a large number of COVID-19-related deaths. In 2020, Italy was one of the oldest countries in the world; nearly 30% of Italy’s population is ages 60 and older and nearly 4% is ages 85 and older (see table). In contrast, China—where the virus started and the number infected spiraled until recently—has 17% of population ages 60 and older and less than 1% ages 85 and older. With the death rate from the disease reported to be six times higher among those above age 80 compared with the rate overall, the number of COVID-19-related deaths could have been much higher if China had an older age structure.1

Click on the table and interactive figure to compare age structures across countries.

Table. Comparing Age Structures Across Countries, 2020

wdt_ID Country Ages 60 and Older Ages 85 and Older
1 Argentina 15.5 1.2
2 Australia 21.8 2.1
3 Brazil 14.0 0.9
4 Chile 17.4 1.4
5 China 17.4 0.7
6 Egypt 8.2 0.3
7 France 26.8 3.4
8 Germany 28.6 3.1
9 Iran 10.3 0.4
10 Italy 29.8 3.7

Source: PRB analysis of data by the United Nations, Department of Economic and Social Affairs, World Population Prospects: 2019 Revision, https://population.un.org/wpp/.

Figure. Population by Age Group, 2020

Click on image to view interactive.

motion chefs  of a restaurant kitchen

Workers at Risk During the Coronavirus Pandemic: Four in 10 Food Preparers and Servers Are Low-Income

The coronavirus pandemic sweeping the globe in 2020 will have long-term and widespread effects on the U.S. economy and labor force. A PRB analysis finds that workers in one of the hardest-hit sectors—food preparation and server-related occupations—are among the most economically vulnerable.

Food preparers and servers include cooks, waitstaff, and others who help prepare and serve our meals in restaurants, coffee shops, hospitals, and school cafeterias. As businesses begin to reopen, the economic challenges facing lower-income workers may have a negative ripple effect. Financial pressures may compel people to work when sick, and those who are uninsured may delay or avoid seeking care for an illness.

In 2018, the United States had 8.8 million food preparers and servers, and more than four in 10 of them (41%) were low-income, meaning they had family incomes below 200% of the official poverty threshold ($50,930 for a family with two adults and two children). Nationwide, 19% of workers were low-income in 2018 (see table).

TABLE. Low-Income Status of U.S. Workers in Selected Occupations, 2018

wdt_ID Occupations Total Workers Low-income Workers Percent Low-Income
1 All occupations 155,982,549.0 30,059,749.0 19
2 Food preparers and servers 8,803,519.0 3,596,843.0 41
3 Personal care and service workers 4,404,322.0 1,388,299.0 32
4 Sales workers 15,709,547.0 3,432,743.0 22

Notes:Families with incomes below 200% of the official poverty threshold are classified as low-income. These estimates are subject to both sampling and nonsampling error.
Source:PRB analysis of data from the U.S. Census Bureau’s American Community Survey Public Use Microdata Sample (PUMS).

Food preparers and servers face additional challenges:

  • A high housing cost burden: In 2018, more than three in 10 workers in food preparation and server-related occupations (31%) had a high housing cost burden—defined as spending more than 30% of household income on housing costs such as mortgage or rent payments, utilities, and other expenses. The national average for all workers was 20%. (Housing costs can impact household composition, as PRB reports.)
  • Lack of health insurance: About 21% of food preparers and servers lacked health insurance coverage in 2018—more than double the national average (10%). Health insurance coverage is important not only so low-income families have access to affordable health care when they need it, but also because persistent health issues and chronic conditions can affect their ability to work and provide for their families.
  • Very low pay for unskilled workers: Among workers in restaurants and other locations that serve meals, dishwashers are among the most economically vulnerable. In 2018, more than 300,000 people worked as dishwashers in the United States, and nearly half of them (49%) were low-income. Chefs and head cooks were among the least likely to be low-income, at 28%.

African Americans and Native Americans Are More Likely to Be Low-Income

The novel coronavirus is affecting people across the nation, but African Americans and Native Americans are among the most economically vulnerable populations, as PRB notes in an analysis. Those who work in food preparation and server-related occupations are particularly vulnerable (see Figure 1). In 2018, over half of African American food preparers and servers were low-income, compared with 37% of white workers in those jobs. American Indians/Alaska Natives also had a high share of food preparers and servers who were low-income (49%).

FIGURE 1. Low-Income Status of Food Preparers and Servers and All Workers, by Race/Ethnicity, 2018

wdt_ID Risk Status, 2014-2018 Number of Young Children Percent of Young Children
1 Low risk of undercount or potential overcount 3,095,045.0 19
2 High risk of undercount 9,290,040.0 56
3 Very high risk of undercount 4,065,149.0 25
4 Total 16,450,234.0 100

Note: Individual racial groups include only single-race non-Hispanics. Hispanics/Latinos
may be of any race. Families with incomes below 200% of the official poverty threshold are classified as low-income. These estimates are subject to both sampling and nonsampling error.

Source: PRB analysis of data from the U.S. Census Bureau’s American Community Survey PUMS.

Workers in the South Are More Likely to Be Low-Income

Food preparers and servers are faring better in some states than others (see Figure 2). In three states—Hawaii, New Hampshire, and Rhode Island—fewer than 30% of workers in food preparation and server-related jobs were low-income in 2018. Food preparers and servers were most likely to be low-income in Arkansas and Mississippi, at more than 55% each. In general, states in the South have higher shares of low-income workers than states in other regions.

FIGURE 2. Food Preparers and Servers Who Are Low Income, 2018


Note: Families with incomes below 200% of the official poverty threshold are classified as low-income. These estimates are subject to both sampling and nonsampling error.
Source: PRB analysis of data from the U.S. Census Bureau’s American Community Survey PUMS.

Personal Service and Sales Workers Are Also Vulnerable

Personal care and service workers—including child care workers, personal and home care aides, workers in hotels and casinos, fitness instructors, and others—are also expected to be hit hard by lost wages and unemployment stemming from the coronavirus pandemic. In 2018, the United States had 4.4 million personal care and service workers and 32% were low-income. Salespeople, also at high risk of layoffs and lost earnings, make up a larger group of workers—15.7 million in 2018—but were less likely to be low-income, at 22%.

In combination, food preparers and servers, personal care and service workers, and salespeople make up 28.9 million workers, or about 19% of the total U.S. workforce. Yet they account for 28% of all workers who are low-income.

Policymakers Can Help COVID-19-Affected Workers and Businesses

Low-income workers face significant challenges—including housing stability and access to affordable health care and child care—under normal circumstances. The pandemic crisis puts these workers at a double disadvantage. Lack of health insurance may discourage low-income workers from seeking health care when they need it, and treatment may result in medical debt. The risk of lost wages may lead people to go to work when sick, increasing the health risk for others. Workers who are laid off due to illness or government-imposed distancing measures may not have enough money to meet basic needs, including food and housing.

Policymakers can help by providing direct cash transfers to affected workers and the businesses that employ them. Some jurisdictions and service providers are also implementing moratoria on evictions and utility shut-offs, and making other accommodations to address the COVID-19 crisis. By providing an adequate safety net for workers who are most economically affected by the pandemic, policymakers can improve the economic outlook for millions of people and speed the recovery of the U.S. economy.


More information about the economic divide between working families at the top and bottom of the economic ladder is available in Low-Income Working Families: Rising Inequality Despite Economic Recovery, by Beth Jarosz and Mark Mather.

 

Older man and woman sitting on bench looking at mountains

Which U.S. States Have the Oldest Populations?

One Quarter of Older Americans Live in California, Florida, and Texas

About 52 million Americans are age 65 or older, according to the Census Bureau’s 2018 population estimates. One quarter of these older Americans live in one of three states: California, Florida, and Texas. Seven other states—Georgia, Illinois, Michigan, New York, North Carolina, Ohio, and Pennsylvania—account for another one-quarter of Americans age 65 or older. These 10 states are also the most populous and include over half of the total U.S. population. Sparsely populated states such as Alaska, North Dakota, South Dakota, Wyoming, and Vermont have very small older populations—less than 150,000 each in 2018.

Percent of Population 65 or Older by U.S. State


But the states with the most adults age 65 or older do not necessarily have the oldest population age profiles. California is a relatively young state even though it has the largest number of older residents: Only 14.3% of the state’s total population was age 65 or older in 2018. In contrast, 20.6% of Maine’s population was age 65 or older, the highest share of any state, followed closely by Florida with 20.5% (see table).

States Ranked by Percent of Population Age 65 or Older, 2018

Rank State Total Resident Population (thousands) Population Ages 65+ (thousands) Population Ages 65+ (percent)
1 Maine 1,338 276 20.6
2 Florida 21,299 4,358 20.5
3 West Virginia 1,806 360 19.9
4 Vermont 626 121 19.4
5 Delaware 967 181 18.7
6 Montana 1,062 199 18.7
7 Hawaii 1,420 261 18.4
8 Pennsylvania 12,807 2,336 18.2
9 New Hampshire 1,356 246 18.1
10 South Carolina 5,084 900 17.7
11 Oregon 4,191 739 17.6
12 Arizona 7,172 1,258 17.5
12 New Mexico 2,095 366 17.5
14 Connecticut 3,573 615 17.2
14 Michigan 9,996 1,717 17.2
14 Rhode Island 1,057 182 17.2
17 Iowa 3,156 540 17.1
17 Ohio 11,689 1,995 17.1
19 Arkansas 3,014 512 17.0
19 Wisconsin 5,814 985 17.0
21 Alabama 4,888 827 16.9
21 Missouri 6,126 1,034 16.9
23 South Dakota 882 147 16.6
24 Massachusetts 6,902 1,139 16.5
24 Wyoming 578 95 16.5
26 Kentucky 4,468 731 16.4
26 New York 19,542 3,214 16.4
26 Tennessee 6,770 1,110 16.4
29 North Carolina 10,384 1,689 16.3
30 New Jersey 8,909 1,439 16.1
31 Idaho 1,754 278 15.9
31 Kansas 2,912 462 15.9
31 Minnesota 5,611 890 15.9
31 Mississippi 2,987 474 15.9
35 Indiana 6,692 1,055 15.8
36 Nebraska 1,929 304 15.7
36 Nevada 3,034 476 15.7
36 Oklahoma 3,943 620 15.7
39 Illinois 12,741 1,993 15.6
40 Louisiana 4,660 718 15.4
40 Maryland 6,043 931 15.4
40 Virginia 8,518 1,315 15.4
40 Washington 7,536 1,164 15.4
44 North Dakota 760 117 15.3
45 California 39,557 5,669 14.3
46 Colorado 5,696 808 14.2
47 Georgia 10,519 1,460 13.9
48 Texas 28,702 3,602 12.6
49 Alaska 737 87 11.8
50 Utah 3,161 350 11.1

Note: Older adults (ages 65+) made up 12.1% of the District of Columbia’s population and 20.7% of Puerto Rico’s population in 2018.

Source: U.S. Census Bureau, Vintage 2018 Population Estimates.


While southern states are regarded as retirement magnets, states in the Northeast and Midwest have among the largest proportional shares of older adults. What’s driving these regional patterns?

Migration, both internal and international, has a large impact on the distribution of older adults. States that have attracted older retirees, such as Arizona, Florida, New Mexico, and South Carolina, have larger proportions of older residents. Many states in the Midwest and Northeast also have large shares of older adults, but for different reasons. As young adults in these states have moved south and west looking for educational and job opportunities, the older population is left to age in place. In contrast, California and Texas have been popular destinations for state-to-state and international migrants, which has kept their populations relatively young.

The share of older adults will continue to increase as more members of the large baby boom cohort reach retirement age. By 2040, 26 states are projected to have age profiles similar to those of Florida and Maine today, with at least 20% of their residents age 65 or older. This demographic shift has implications for many federal and state programs that support older adults. As more Americans become eligible for federal entitlement programs like Medicare and Social Security, spending reductions and tax increases may be inevitable.


Excerpted from PRB’s Population Bulletin, “Elderly Americans,” by Christine L. Himes, and updated in 2019.

Crowd from overhead

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

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

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

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

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

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

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

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

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

 

 

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

 

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

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

 

TABLE 

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

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

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

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


 

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

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

 

 

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

 

The Undercount Risk Affects Every State

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

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

 

 

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

An Accurate Count of the Population Is Essential

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

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

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

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

References

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

Suicides have become the second-leading cause of death among teenagers in the United States, surpassing homicide deaths, which dropped to third on the list (see Figure 1). The teenage suicide rate increased from 8 deaths per 100,000 in 1999 to 8.7 deaths per 100,000 in 2014.

Higher suicide rates are driven in part by changes in the method of suicide. Suffocation, which includes hanging and strangulation, and is highly lethal, increased as a method of suicide. A rising suicide rates among teenage girls is driving the higher overall suicide rate.

Despite the rise in suicide, the overall mortality rate among teenagers has fallen from 68.6 deaths per 100,000 in 1999 to 45.5 deaths per 100,000 in 2014, as a result of declining homicide and traffic accident death rates during the past 15 years. Data are based on Population Reference Bureau (PRB)’s analysis of mortality statistics from the U.S. Centers for Disease Control and Prevention (CDC).

Figure 1


Lethal Methods Contribute to Rising Suicide Rate

A higher rate of suicide attempts does not appear to be driving the increasing teenage suicide rate. Data from the Youth Risk Behavior Surveillance System show that among high school students, the prevalence of attempting suicide remained flat from 1999 to 2013.1 Rather, suicide attempts today appear more likely to result in death because teenagers have shifted to more lethal methods of self-harm—a trend that has alarming implications.2

The frequency of suffocation (a particularly lethal form of self-harm, which includes hanging) as the reported cause of suicide-related death among teenagers nearly doubled over the past 15 years. The share of teenage suicides due to suffocation rose from more than one-quarter (27 percent) in 1999 to nearly one-half (45 percent) in 2014.

Both teenage boys and teenage girls are increasingly likely to commit suicide by suffocation. From 1999 to 2014, the overall suicide rate among teenage girls increased by 1.5 deaths per 100,000, while the suffocation-related suicide rate increased by 1.4 deaths per 100,000 (see Figure 2). In other words, the increased rate of suffocation deaths accounted for virtually all of the increase in the suicide rate among teenage girls.

Figure 2


Among teenage boys, the suicide rate by suffocation increased by almost 60 percent across this period, but their overall suicide rate remained stable at 13 deaths per 100,000 due to a drop in the rate of suicide by other methods including firearms.

Increasing use of highly lethal methods of self-harm presents a significant public health challenge. The reasons teenagers are using more lethal methods to attempt suicide remain unclear. Some researchers hypothesize that social contagion—more exposure to suicide could induce at-risk individuals to attempt suicide—may be to blame, but there are no definitive answers. More research is needed to understand the underlying factors behind this trend. In the meantime, suicide prevention programs should continue working to address root causes, while also recognizing that the risk of death from a suicide attempt is rising.

Teenage Suicide Rates Rose for Nearly Every Demographic Group

The suicide rate for teenage boys was three times the rate for teenage girls in 2014. However, the rise in the overall teenage suicide rate between 1999 and 2014 was driven by the 56 percent increase in the suicide rate among teen girls—from 2.7 deaths per 100,000 to 4.2 deaths per 100,000.

Suicide rates rose for girls in every racial/ethnic category between 1999-2001 and 2012-2014.3 Rates rose fastest for American Indian and Alaska Native girls (60 percent increase), and rates rose by more than 50 percent for both non-Hispanic Black/African American and non-Hispanic white teenage girls.

Among boys, only non-Hispanic Black/African American teenagers had lower suicide rates in 2012-2014 than in 1999-2001. As with girls, rates rose fastest for American Indian and Alaska Native teenage boys, and rates also increased for non-Hispanic white boys. Rates remained stable for Asian/Pacific Islander and Hispanic teenage boys.

Overall, the highest teenage suicide rates are among American Indian and Alaska Native teenagers. This may be partially explained by their greater concentrations in rural areas, where the risk of suicide is much greater (see map). Yet, even in rural areas, American Indian and Alaska Native teenagers have extraordinarily high rates of suicide, especially as compared with other racial/ethnic groups living in those areas.

Teenage Suicides Highest in Rural Areas

Suicide rates are higher in rural areas for a variety of reasons including social isolation, prevalence of firearms, economic hardship, and limited access to mental health and emergency health care services.

The teenage suicide rate in rural areas is nearly double the rate in highly urbanized areas (11.9 deaths per 100,000 in rural areas and 6.5 deaths per 100,000 in the most urban counties).4 All of the states with the highest rates of teenage suicide—Alaska, South Dakota, Wyoming, and North Dakota—have relatively high proportions living in rural areas (see map). Conversely, the four states with the lowest teenage suicide rates—California, Connecticut, New Jersey, and New York—have predominantly urban and suburban populations.

 

In addition to having lower teenage suicide rates overall, the most urbanized areas saw no increase in suicide rates between 1999-2001 and 2012-2014. Rates rose in less urbanized areas and rural areas.

Looking Ahead

The recent decline in the overall teenage death rate shows that the United States is making progress in keeping children safe from harm. Yet the rise in suicide rates represents a significant and growing public health threat, and requires action. Suicide prevention strategies include depression/suicide awareness programs, expanded access to mental health services, and programs that support vulnerable populations (such as Native American teenagers, teenagers struggling with gender and sexual identity, and those with mental health or substance abuse problems).

These troubling trends should serve as a reminder to health practitioners, hotline workers, and the public that teenage suicide risk should be taken seriously. Expanding mental health and other social and strengthening social connections with at-risk teenagers can help prevent these deaths.5


References

  1. Centers for Disease Control and Prevention (CDC), “Trends in the Prevalence of Suicide-Related Behavior National Youth Risk Behavior Survey: 1991-2013,” accessed at www.cdc.gov/healthyyouth/yrbs/pdf/trends/us_suicide_trend_yrbs.pdf, on May 27, 2016; and Child Trends, “Suicidal Teens: Indicators on Children and Youth,” (August 2014), accessed at www.childtrends.org/wp-content/uploads/2012/07/34_Suicidal_Teens.pdf, on May 27, 2016.
  2. Centers for Disease Control and Prevention (CDC), Morbidity and Mortality Weekly Report, “Suicide Trends Among Persons Aged 10-24 Years, United States, 1994-2012,” (March 2015), accessed at www.cdc.gov/mmwr/pdf/wk/mm6408.pdf, on May 27, 2016.
  3. We use three-year rates for race/ethnic and state detail to improve the stability of rate estimates over time.
  4. Highly urbanized areas are defined here as “Large Central Metropolitan” counties, and rural areas are defined as “NonCore (non-metro)” counties, based on the 2013 National Center for Health Statistics “Urban-Rural Classification Scheme for Counties.” For more information, see www.cdc.gov/nchs/data_access/urban_rural.htm#update.
  5. Substance Abuse and Mental Health Services Administration, “Suicide Prevention,” accessed at www.samhsa.gov/suicide-prevention, on May 27, 2016.
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Occupational Earnings Gap

In the United States, full-time working women earn less than men, on average—even in female-dominated occupations (those in which women comprise 70 percent or more of workers), such as nurse practitioners, office clerks, and flight attendants. There are no occupations in which women’s median annual earnings are significantly higher than men’s. Using the latest and most detailed data available, the graphic below shows more than 50 occupations with the biggest gaps in pay parity, where full-time working women earn 75 cents or less, on average, for every dollar made by men in the same job.

 

 

gallery-gender-gap-2

 

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In U.S., New Data Show Longer, More Sedentary Commutes

Sitting in traffic, waiting for a train or bus, navigating bike lanes and sidewalks—these are things that can be stressful for commuters getting to work. The average full-time worker in the United States spends almost 26 minutes commuting to work, according to just-released data from the 2013 American Community Survey.1 As traffic congestion and commuting distance have increased, average travel time to work has also increased (see top map).



In addition to psychological stress, commuting can affect overall health. Longer commutes are associated with less physical activity, lower cardiorespiratory fitness, higher rates of obesity, and elevated blood pressure.2 Automobile commutes are associated with higher levels of stress than active (walk or bike) commutes.3 Although biking to work can be associated with risks such as traffic accidents and inhaled pollutants, a 2010 study found that the exercise benefits of biking versus driving outweigh these risks.4

The number of bike commuters in the United States has gone up sharply in the past decade from 488,497 to 882,198 since 2000, although bicyclists still account for less than 1 percent of all commuters. However, over the long term, commuting has become more sedentary (see table). Walking to work has dropped sharply, from 10 percent in 1960 to less than 3 percent in 2013. While the share of commuters taking a car, truck, or van to work dropped slightly in the past decade, the change is due to a decline in carpooling. The share of workers driving alone continues to rise. In 2013, more than three-quarters of workers drove alone as their primary means of commuting to work.


Workers in the United States Are Spending More Time Traveling to Work

    Car, Truck, or Van      
  Average Commute Time to Work (minutes) Drive Alone (%) Carpool (%) Public Transit (%) Walk (%) Bike (%)
2013 25.8 76.4 9.4 5.2 2.8 0.6
2000 25.5 75.7 12.2 4.7 2.9 0.4
1990 22.4 73.2 13.4 5.3 3.9 0.4
1980 21.7 64.4 19.7 6.4 5.6 0.5
1970 8.9 7.4
1960 12.1 9.9

Note: “-” indicates data not available.
Sources: U.S. Census Bureau, Decennial Census 1960, 1970, 1980, 1990, 2000, and American Community Survey 2013.


While 4 percent of workers are telecommuters and have no commute at all, a sizeable proportion of U.S. workers have lengthy commutes. More than 8 percent of U.S. commuters have a commute to work of 60 minutes or more, and almost 3 percent have extreme commutes of 90 minutes or more one way. Almost 600,000 full-time workers are “mega” commuters, traveling at least 90 minutes and at least 50 miles one way to work.5 In 2013, nearly 24 percent of workers worked outside their county of residence, up from 18 percent commuting across county lines in 1980; and nearly 4 percent worked outside their state of residence, up from 3 percent commuting across state lines in 1980.

  • Teens are more likely than those in other age groups to be active (walk or bike) commuters. About 9 percent of workers ages 16 to 19 walk to work. However, walk-commuting declines with age. For all commuters age 25 or older, less than 3 percent walk to work. Workers ages 45 to 64 drive alone more often (79 percent) and walk to work less often (2 percent) than workers under age 45.
  • Workers with the lowest annual earnings, less than $10,000, are most likely to walk (6 percent) and are least likely to drive alone (67 percent) to work. Walk-commuting dips below 2 percent for those with earnings above $35,000. Driving to work, whether alone or as part of a carpool, peaks for those with annual earnings between $35,000 and $65,000. In that earnings range, nearly 90 percent of workers drive or carpool. Driving declines a bit among those with higher levels of earnings. Workers with earnings of $75,000 or more are slightly more likely to walk, bike, or take public transit to work or to work from home than workers in the middle of the earnings distribution.
  • Residents of Maryland and New York face the nation’s longest commutes; each has an average one-way commute of more than 32 minutes. Commutes are shortest, fewer than 18 minutes on average, in North Dakota and South Dakota.

Residents of Alabama, Arkansas, Mississippi, Ohio, Oklahoma, and Tennessee are most likely to drive alone to work. Residents of the District of Columbia are least likely to drive alone to work.

The District of Columbia boasts the nation’s highest rates of active (walk or bike) commuting, with more than 18 percent of commuters biking or walking to work (see bottom map). The District almost quadrupled bike use from 2000 to 2013, owing to investments in biking infrastructure and planning policies. Despite extreme weather, Alaska ranks second as the most active-commute state, with nearly 10 percent of commuters walking or biking to work. In Alaska, residents in small communities often work near home and they may not have a vehicle.


Beth Jarosz and Rachel T. Cortes are research associates in U.S. Programs at PRB.


References

  1. The American Community Survey asks respondents about the average number of minutes they commute one-way to work.
  2. Christine M. Hoehner et al., “Commuting Distance, Cardiorespiratory Fitness, and Metabolic Risk,” American Journal of Preventative Medicine 42, no. 6 (2012): 571-78, accessed at www.ajpmonline.org/article/S0749-3797%2812%2900167-5/abstract, on Aug. 15, 2014.
  3. Erik Hansson et al., “Relationship Between Commuting and Health Outcomes in a Cross-Sectional Population Survey in Southern Sweden,” BMC Public Health 11, no. 834 (2011), accessed at www.biomedcentral.com/1471-2458/11/834, on Aug. 15, 2014; and Birgitta Gatersleben and David Uzzell, “Affective Appraisals of Daily Commute: Comparing Perceptions of Drivers, Cyclists, Walkers, and Users of Public Transport,” Environment and Behavior 39, no. 3 (2007): 416-31, accessed at http://eab.sagepub.com/content/39/3/416, on Aug. 15 2014.
  4. Jeroen Johan de Hartog et al., “Do the Health Benefits of Cycling Outweigh the Risks?” Environmental Health Perspectives 118, no. 8 (2010): 1109-16, accessed at http://ehp.niehs.nih.gov/0901747/, on Aug. 15, 2014.
  5. “Megacommuters: 600,000 in U.S. Travel 90 Minutes and 50 Miles to Work, and 10.8 Million Travel an Hour Each Way, Census Bureau Reports” (March 2013), U.S. Census Bureau press release, accessed at www.census.gov/newsroom/press-releases/2013/cb13-41.html, on Aug. 15, 2014; and Melanie A. Rapino and Allison K. Fields, “Mega Commuting in the U.S.: Time and Distance in Defining Long Commutes Using the 2006-2010 American Community Survey,” accessed at www.census.gov/hhes/commuting/files/2012/Paper-Poster_Megacommuting%20in%20the%20US.pdf, on Aug. 15, 2014.
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Understanding Population Projections: Assumptions Behind the Numbers

Policymakers and program planners rely on population projections to assess future demand for resources such as food, water, and energy, as well as services such as health and education. Projections alert policymakers and planners to major trends that may affect social and economic development and help them craft appropriate policies and programs.

Many governments periodically make population projections for their own countries. In addition, organizations like the United Nations Population Division (UNPD) and the U.S. Census Bureau regularly prepare population projections for the world, regions, and individual countries. To develop these projections, demographers must make assumptions about future trends related to fertility, mortality, and migration. These assumptions, though based on research and expert opinions, are not certain.

Population projections represent the future size of a population and the age and sex distribution if the assumptions used hold true. Many users of projections, however, may not be aware of exactly how they are made and do not consider the assumptions and limitations that underlie them. It is essential that users have a basic understanding of these assumptions and their plausibility before using them.

Uncertainty in projections can result from a variety of sources, such as in the estimate of a current population size that serves as the “starting” population for projections. Time also increases uncertainty: Projections over longer periods are less certain than short-term projections because of the compounding effects of inaccuracies in assumptions over time. This brief aims to improve understanding of population projections by highlighting some of the key assumptions on which they are based. The brief examines and discusses the population projections produced by UNPD (hereafter referred to as UN projections) as an example.1

Components of Population Projections

The population of a country or area grows or declines through the interaction of three demographic factors: fertility, mortality, and migration. To project future population, demographers make assumptions about how the current rates of births, deaths, and immigration and emigration will change in the future. Based on these assumptions, age- and sex-specific population increases or decreases over a future period are calculated and added to census results or an estimate of the population at the beginning of the period.

Each set of projections produced by an organization or government is based on its own set of assumptions about fertility, mortality, and migration, and will likely differ from each other. Some groups, most notably the UNPD, identify uncertainty in projections by showing estimates of the likelihood that the future population size will fall within a certain range. The UNPD and others also develop multiple projections to reflect several possible scenarios of future levels of fertility and mortality.

Fertility

Fertility is expressed as the total fertility rate (TFR), a measure of the number of children on average that a woman will have in her lifetime. (More specifically, the TFR is a measure of how many children women would bear in their lives if the rate of childbearing in a given year remained unchanged across their lives.) Of the three components, fertility often has the largest effect on future population size, especially in developing countries with high birth rates.

Globally, fertility fell during the latter half of the 20th century, and though it has not decreased at the same pace everywhere, today the world’s total fertility rate stands at 2.5 children per woman. Where fertility is high, demographers generally assume that fertility will follow a similar pattern of decline and eventually stabilize in every country at about two children per woman. This level of fertility is referred to as “replacement level” fertility (2.1 children per woman) as couples who have two children merely replace themselves without increasing the size of succeeding generations.



One common misunderstanding about population projections is that when fertility declines to replacement level, the population will immediately stop growing. In previously high-fertility countries, however, population will continue to grow for many decades even after fertility reaches replacement level. Years of high fertility result in a young population age structure, which generates momentum for future population growth as the increasing number of young people begin having children of their own. Brazil, for example, had fertility decline to below-replacement level in the mid-2000s, but the UNPD projects its population to continue growing until mid-century. Not only does fertility decline affect population size, it also profoundly affects age distribution. Declines in fertility result in a growing proportion of elderly, now seen in most developed and many developing countries.

In most developed countries, fertility is now below replacement level, often quite far below. The majority of developing countries, however, still have fertility above replacement level. In the least developed countries, women have on average more than four children. Additionally, fertility has remained high in most countries of sub-Saharan Africa, often declining slowly or not at all. As such, the fertility assumptions for this region tend to be less reliable. Future population size and age distribution for a country can vary substantially based on when a fertility decline begins, the pace of the decline, and whether the decline continues all the way to replacement fertility or stalls at a higher level.

Because of the possible discrepancy between assumptions and actual trends, the UNPD publishes multiple projections every two years with differing fertility assumptions, including Low, Medium, and High Fertility variants. The Medium Variant, most often cited among the series, assumes a growth in the use of family planning that will result in reductions in fertility in patterns similar to what occurred in other countries. The Low Variant simply assumes that in each country the TFR is one-half child less than the Medium Variant at most periods in time, while the High Variant assumes that the TFR is one-half child more than the Medium Variant. Under these three variant scenarios, the assumed fertility in Kenya in 2050, for example, would range from 2.2 to 3.2 children per woman—down from 4.6 in 2010. Kenya’s population totals in 2050 corresponding to these different assumptions about fertility would range from 85 to 110 million (see Figure 1).

Another common misunderstanding is that a path of fertility decline is more or less automatic and is continuous, as projections assume. Declines in fertility, however, often depend on increased investments in family planning services, health, and education—particularly for women and girls. Many countries that have not adequately invested in these areas have not experienced the fertility declines assumed in past projections and have had subsequent projections continuously revised upward. Over one-half of the countries in the Africa region, for example, had their UN population estimates for 2010 revised upward between the 2010 and 2012 revisions, increasing the total population projected for the region under the Medium Variant scenario by 8.8 million. In other cases, countries that have invested adequately have seen fertility decline more rapidly than originally assumed and population projections have been revised downward.

Some users incorrectly assume that population levels stabilize in the final year for which a population is projected. For many years, the UNPD developed projections to the year 2050 and some users incorrectly interpreted the numbers to mean that world population growth under the Medium Variant would slow and stabilize in 2050. More recently the UNPD has developed population projections to 2100, and while the uncertainty in the underlying assumptions grows over time, population growth for the world and in many countries continues well beyond 2050. In fact, in all of the current population projections except the Low Variant, world population continues to grow past 2050.



Mortality
Mortality is incorporated into projections by estimating death rates by age group and sex. Where mortality is relatively high and the resulting life expectancy at birth relatively low, changes in mortality play an important role in future population size. Where mortality is already low and life expectancy has risen, mortality has much less effect. Throughout developing countries, infant mortality has declined substantially over the last several decades; the general assumption underlying population projections for all countries is a continued decline in death rates and an increase in life expectancy.

The HIV pandemic and its substantial impact on mortality in countries with high prevalence created the need to consider the future course of HIV infection and its treatment in mortality assumptions and population projections. In those countries with growing HIV epidemics during the 1990s, death rate assumptions were revised upward in population projections. Despite the rise in mortality, population growth continued, albeit at a slower rate due to the impact of HIV. Malawi’s population projection for 2050, for example, is 49.7 million, 8.2 million lower than the projection without the impact of HIV on mortality (see Figure 2). Recently, the UN projections show that life expectancies in the seriously affected countries of southern Africa are beginning to rise as a result of slowing the spread of HIV and improving the chances of survival among people living with HIV. Nonetheless, HIV will have a lasting impact on mortality for several decades: The extent to which HIV affects future mortality will depend on continued investments in both prevention and treatment of the disease. In fact, the UNPD assumes mortality from HIV will continue to decline due to improved access to antiretroviral therapy and fewer new infections.



For many developed countries, low fertility combined with declining mortality among older adults is of considerable interest because of the impact on population aging. For example, the UN projections for many developed countries show the proportion of the population ages 65 and over rising as high as 30 percent to 40 percent by mid-century, an unprecedented development. Over 90 countries are projected to have life expectancy at age 65 reach 20 years or more by mid-century.

Many people often wonder whether demographers incorporate other possible increases in mortality into projections, such as future conflict, natural disasters, or changing lifestyles like increases in obesity and lack of exercise. Because of the uncertainty about where conflict and natural disasters might occur, what the impacts might be, and how mortality rates might be affected, demographers do not incorporate such factors into projections. In the case of changing lifestyles, data on the impact on mortality are still largely unavailable or just emerging in most countries and are not yet included in projection assumptions. In general, demographers have not assumed other changes in mortality beyond declining infant mortality, the continued impact of HIV, and increased longevity.

Migration
International migration can be particularly unpredictable and difficult to incorporate into projection assumptions. Migration flows often result from short-term changes in economic, social, political, or environmental factors that are difficult to anticipate. Moreover, for many countries, reliable information on the number of immigrants and emigrants is not available.

Nonetheless, migration can have a significant effect on population change in specific countries and regions. For many years, the most common pattern of migration has been the movement of people from developing countries to developed countries and from poorer developed countries to wealthier ones. Populations of countries and regions with low fertility, where deaths exceed births, will decline without net migration gains. For example, international migration accounted for over one-half of the population growth in developed countries in the 2000s. The movement of people between developing countries because of economic opportunities, environmental disasters, or political or civil unrest has also altered the demographic landscape.

Migration assumptions often take into account the experience of countries with historically high immigration, such as the United States. Given its unpredictable nature, however, it is usually assumed that current migration levels will persist for a time and slowly decline. For example, the UNPD assumes that the current estimated annual flow of about 2.6 million people from less developed countries to more developed countries will slowly decrease to about 2.1 million by 2050. But national policies on immigration and the future economic appeal of developed countries could certainly change that figure in either direction. UNPD assumes net migration will eventually reach zero by 2100 in all countries. This highly unlikely scenario suggests how difficult it is to predict the levels of migration over such a long period.

How Can Decisionmakers and Program Planners Best Use Projections?

The accuracy of population projections depends primarily on the accuracy of the underlying assumptions. Demographers attempt to make the best assumptions possible based on the existing evidence and revise them as new information becomes available through various sources, such as national censuses, vital registrations, immigration statistics, and demographic surveys. Therefore, it is critical that policymakers and planners understand the assumptions behind different projection series. Understanding the causes of uncertainty in population projections and their implications for plans and policies that span different time horizons is essential
for successful planning.

It is also important that users reject the common misunderstandings about population projections, and instead understand that:

  • Countries do not immediately stop growing when fertility reaches replacement level.
  • Fertility does not decline automatically, as assumed in projections, without investments in areas such as family planning, health, and education of women and girls.
  • Population levels do not necessarily stabilize in the final year for which a population is projected.

Correctly understanding these critical points allows policymakers and planners to have a more realistic assessment of the impact of future population growth.

At the same time, policymakers and planners can contribute to improving the accuracy of population projections by supporting national and international efforts to collect more accurate demographic data that would lead to more accurate assumptions, ultimately improving projections and increasing their value for policy and planning purposes.

 

References

  1. United Nations, Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision, Volume I: Comprehensive Tables (New York: United Nations, 2013).
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U.S. Energy Boom Fuels Population Growth in Many Rural Counties

A population boom in energy-rich counties is breathing new life into parts of the Midwest and Appalachia that have experienced long-term population decline or slow growth compared with the rest of the United States, according to new data released by the U.S. Census Bureau.1

Between 1950 and 2010, North Dakota had one of the slowest population growth rates among the 50 states, second only to West Virginia. Since 2010, North Dakota has been the fastest-growing state, with a 7.6 percent increase in population, compared with a 2.4 percent increase nationwide. The increase in North Dakota’s population from 2010 to 2013 (50,802) nearly matched population growth in the state during the entire 60-year period from 1950 to 2010 (52,995).

Although the population rebound has been most striking in North Dakota, growth is not limited to that state. Many other parts of the Midwest and Great Plains—especially parts of South Dakota, eastern Montana, western Kansas, and parts of West Texas—are also experiencing a population boom, or at least a reprieve from the population losses that have plagued the region during previous decades (see map).



In most of these areas, population growth was preceded by a sharp increase in shale oil extraction, as identified on this map by the Energy Information Administration.2 The U.S. Census Bureau reports that nationwide, employment in oil and gas extraction, mining, and quarrying increased by 23 percent from 2007 to 2012, making it among the fastest-growing industries in the country.3 Much of this growth has been concentrated in the Bakken Oil Field in the western part of North Dakota, resulting in rapid population gains in that region. This population growth is not without controversy, given that the industry responsible for it has both supporters and detractors. The industry is called hydraulic fracturing, or more commonly “fracking.”

In Appalachia—another historically slow-growing region—natural gas extraction from shale deposits has been a key driver of recent economic growth.4 However, the population gains in the Marcellus Shale Region (primarily in Pennsylvania and West Virginia) have been relatively modest compared with the gains in North Dakota. This difference may reflect the scale of the oil boom in North Dakota, compared with natural gas drilling in Appalachia. Between 2007 and 2012, employment in the oil and gas industry in North Dakota increased by 354 percent, compared with a 259 percent increase in Pennsylvania.5 Population growth in North Dakota also stands out because the boom occurred primarily in sparsely populated rural counties, while many of the shale extraction sites in Pennsylvania and West Virginia were in counties located within or adjacent to metropolitan areas.

New Residents, New Challenges

In areas with slow-growing or declining populations, a sudden influx of new residents can jump-start the economy by adding to the tax base and creating demand for new products and services. Data from the Census Bureau’s American Community Survey can help identify emerging social and economic trends in North Dakota and other fast-changing states and local areas. For example, between 2000 and 2013, the population in western North Dakota increased 19 percent, compared with a 5 percent increase in the Midwest overall. Employment in agricultural and extraction-based industries in western North Dakota increased sharply, and the unemployment rate in that region fell from 5 percent in 2000 to only 3 percent in 2012. In the Midwest, unemployment rose from 5 percent to almost 9 percent during that same period.

Median income in North Dakota increased and poverty fell with the availability of higher-paying jobs; in 2012, the U.S. average annual income for workers in the oil and natural gas industry was $107,198, compared with $49,289 across all industries.6 Between 2000 and 2012, median income in western North Dakota grew by 40 percent, while income fell 14 percent in the Midwest. Poverty rates also diverged, dropping from 13 percent to 9 percent in western North Dakota, but increasing from 10 percent to 15 percent in the Midwest.

However, there are potential downsides to rapid population and economic growth. Adding new residents creates a greater demand for housing, health care, transportation, adequate roads, and police protection.7 In North Dakota, the violent crime rate increased 8 percent between 2011 and 2012, with a sharp increase in crime in the western part of the state.8 Traffic deaths are up sharply, from 92 deaths in 2010 to 147 deaths in 2012.9 Local schools may not have enough classrooms or teachers to meet the needs of the growing student population. After more than a decade of little change in the school-age population, the number of children under age 5 is increasing rapidly, and kindergarten enrollment could grow by nearly 30 percent in coming years.10

Migrants often have different characteristics compared with the local population, resulting in a clash of cultures. Many of those moving to North Dakota are young adults looking for work, while long-term residents are more likely to be older, and include many retirees. Young men in their 20s accounted for 29 percent of the recent population growth in western North Dakota. Median age in the region fell from 37 in 2000 to 36 in 2012, while the median age increased in the Midwest as a whole, from almost 36 to 38.

The influx of younger oil-field workers has also resulted in a gender imbalance in western North Dakota. Among young adults (ages 20 to 39), men outnumber women by 24 percent. But at the time of the 2000 Census, there were only 3 percent more men than women in the same age group.

The Big Picture for Rural Areas

Despite the population gains in North Dakota and other parts of the Midwest, rural areas in general are still lagging behind the rest of the country in population growth. Of the 1,649 counties that lost population from 2010 to 2013, 77 percent are located outside of metropolitan areas, and 54 percent rely heavily on farming, manufacturing, or mining. Just as the strong economy during the 1990s created new opportunities for people to live and work in rural areas, the weak economy since 2000 has been pushing many people back to cities to find jobs with decent wages.

Many counties in rural areas have aging populations and too few births to sustain population growth. However, an influx of baby boomers—many of whom are now reaching retirement age—could help provide economic relief to areas experiencing population loss, by increasing the demand for services, housing, and transportation. For this reason, many rural counties are leveraging their natural resources, such as lakes, forests, or mountains —rather than energy resources—to attract new residents and boost their economies.11

 

References

  1. U.S. Census Bureau, “Energy Boom Fuels Rapid Population Growth in Parts of Great Plains; Gulf Coast Also Has High Growth Areas, Says Census Bureau,” accessed at www.census.gov/newsroom/releases/archives/population/cb14-51.html, on March 27, 2014.
  2. In the case of Kansas, oil and gas exploration has occurred in a western area of the state referred to as the Mississippian Limestone Play. For more information, see Catherine S. Evans and K. David Newell, “The Mississippian Limestone Play in Kansas: Oil and Gas in a Complex Geologic Setting,” accessed at www.kgs.ku.edu/Publications/PIC/pic33.html, on March 25, 2014.
  3. U.S. Census Bureau, “Mining, Quarrying, Oil and Gas Extraction Booming, According to First Results From the Census Bureau’s 2012 Economic Census,” accessed at www.census.gov/newsroom/releases/archives/economic_census/cb14-52.html, on March 25, 2014.
  4. Amy Higginbotham et al., “The Economic Impact of the Natural Gas Industry and the Marcellus Shale Development in West Virginia in 2009,” accessed at www.be.wvu.edu/bber/pdfs/BBEr-2010-22.pdf, on March 26, 2014.
  5. U.S. Bureau of Labor Statistics, “The Marcellus Shale Gas Boom in Pennsylvania: Employment and Wage Trends,”Monthly Labor Review (February 2014), accessed at www.bls.gov/opub/mlr/2014/article/pdf/the-marcellus-shale-gas-boom-in-pennsylvania.pdf, on March 24, 2014.
  6. U.S. Bureau of Labor Statistics, “The Marcellus Shale Gas Boom in Pennsylvania.”
  7. Ronald L. Little, “Some Social Consequences of Boom Towns,” North Dakota Law Review 402, no. 53 (1976-1977): 405.
  8. Jenny Michael, “Crime Up 7.9 Percent Last Year in North Dakota,” Bismarck Tribune, July 30, 2013, accessed at http://bismarcktribune.com/, on March 24, 2014.
  9. North Dakota Department of Transportation, “2012 North Dakota Crash Summary,” accessed at www.dot.nd.gov/divisions/safety/docs/crash-summary.pdf, on March 24, 2014.
  10. PRB estimate based on analysis of data from the U.S. Census Bureau, Census 2000 and Census 2012, and American Community Survey.
  11. International City/County Management Association, Center for Sustainable Communities, “Asset-Based Economic Development and Building Sustainable Rural Communities, Part 2: Natural Resources and Amenities,” accessed at www.nado.org/wp-content/uploads/2012/11/Asset-Based-Economic-Development-Part-2.pdf, on March 25, 2014.