PRB

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For More Information

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

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

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

How Is Census Accuracy Measured?

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

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

How Accurate Was the 2020 Census at the National Level?

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

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

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

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

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

Children Were Undercounted More Than Any Other Age Group

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

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

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

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

Historically Undercounted Groups Were Underrepresented—Again

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

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

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

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

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

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

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

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

Renters Were Undercounted, While Homeowners Were Overcounted

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

Widespread Errors Found in Data for Group Quarters Population

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

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

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

How Accurate Was the 2020 Census at the State Level?

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

Table 3. PES Estimates of Net Coverage Error by State

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

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

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

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

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

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

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

Omission Rates Increased in 2020, Especially for Historically Undercounted Groups

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

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

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

Note: Race is Alone or in Combination.

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

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

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

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

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

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

Box 

Coverage of Total Housing Units Improved in the 2020 Census

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

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

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

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

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

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

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

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

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

What’s Next?

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure 1. State Policies Have Become More Polarized Since 1980

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

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

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

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

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

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

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

Figure 3. Life Expectancy Differences Have Widened Among States

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

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

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

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

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

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

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

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

References

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

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

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

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

[5] Karas Montez et al.

Anti-poverty tax credits provide more than financial relief for families living on tight budgets—they also appear to help prevent trauma, violence, and crime among children and youth, three studies from the University of Washington (UW) show. And these credits may cut child poverty without affecting parents’ workforce participation, other new studies show.

Researchers with the UW Center for Studies in Demography and Ecology found that the child tax credit (CTC) and earned income tax credit (EITC) are related to declines in reports of child maltreatment, youth violence, and juvenile convictions.

The CTC and EITC are among the largest anti-poverty programs in United States, notes Ali Rowhani-Rahbar, a UW epidemiology professor and study co-author. “While not originally designed to prevent violence, we find meaningful reductions in several forms of violence per each $1,000 increase in EITC provided,” he adds.

This new research comes as policymakers at both the national and state levels are considering proposals to expand these tax credits. Child poverty fell more than 40% between 2020 and 2021 thanks to a temporary one-year expansion of the CTC—part of the government’s pandemic response.¹

In 2022, the EITC lowers moderate- and low-income workers’ tax bills by between $560 and $6,935, depending on family size, income, and filing status; taxpayers receive a payment if the EITC they qualify for is higher than their tax bill.² The CTC lowers parents’ taxes by $2,000 per child in 2022; families who owe little or no taxes may still receive a partial payment of up to $1,500.

Tax Credits Linked to Immediate, Short-Term Reductions in Reports of Child Maltreatment

The UW researchers found a significant drop in state-level reports of child maltreatment in the period after tax filers received refunds including the EITC and CTC.³ Specifically, for each additional $1,000 in per-child EITC and CTC refund, state reports to child welfare authorities declined 5% in the five weeks following the payments, they report.

Child maltreatment disproportionately affects children in poverty, the researchers note. “Because refundable tax credits are delivered to families as a lump-sum payment with their tax refunds, these credits create unusual ‘financial slack’ at tax time for low-income families living on a tight budget,” they write.

“Most victims of child maltreatment experience neglect,” explains Heather Hill, a UW professor of public policy and management and study co-author. “The definition of neglect overlaps substantially with poverty and material hardships—for example, not providing children with sufficient food, clothing, or medical care. For that reason, income support may reduce child maltreatment directly by increasing income and reducing material hardships or indirectly by reducing parental stress and improving parenting.”

“Increased income could also help parents afford an improved child care arrangement and prevent mothers from re-partnering with someone who is not the child’s biological father out of economic necessity,” Rowhani-Rahbar adds.

For the study, the research team compared EITC and CTC refund data from the Internal Revenue Service to state-specific child maltreatment report data from the National Data Archive on Child Abuse and Neglect for 48 states and the District of Columbia. They examined tax seasons 2015 through 2018, a period when the timing of payments changed because of the PATH (Protecting Americans Against Tax Hikes) Act. The drop in child maltreatment reports continued to correspond to the receipt of tax refunds despite the change in timeline.

“The generosity of these anti-poverty programs matters,” Rowhani-Rahbar says. “Even relatively small increases in income may lower the risk of maltreatment by reducing economic stress and supporting parents’ capacity to engage in nurturing behaviors.”

Youth Report Fewer Physical Fights and Juvenile Convictions With Higher Family EITC Benefits

More than half of states offer working families additional EITCs—with some state payments more generous than others. The UW researchers found that high school students report significantly less physical fighting in states with higher EITCs.⁴ Specifically, a 10-percentage point greater state EITC was associated with nearly 4% fewer physical fights. The effect was particularly notable among males.

Previous research shows higher levels of household poverty make it more likely that children will experience physical aggression, fighting, and bullying, according to the researchers. Residents of disadvantaged neighborhoods are more likely to be assaulted, robbed, and carry a weapon, they note.

The researchers suggest that EITC benefits “could reduce youth violence by relieving parental stress, preventing harsh parenting and family conflict, allowing families to move to safer and more cohesive neighborhoods with more economic opportunity, and enabling families to invest in ways that protect youth from violence.” These investments could include tutoring, youth sports, or enrichment programs that “provide more supervision and fewer opportunities for delinquency,” notes Hill.

For the study, the researchers analyzed state-level EITC generosity and self-reports of physical violence among high schoolers in the previous year using Youth Risk Behavior Surveillance System data from 2005 to 2019. They accounted for changes in state EITC levels and differences in state GDP, welfare payments, and minimum wages.

This study provides “some of the first evidence that a cash transfer program can serve as a prevention strategy for youth violence,” Rowhani-Rahbar says.

A related UW study links each additional $1,000 in total EITC benefits a child’s family receives before their 14th birthday with an 11% lower risk of self-reported criminal conviction during adolescence.⁵

Those findings are based on an analysis of the 1979 National Longitudinal Study of Youth, which interviewed U.S. adolescents born between 1979 and 1998 at ages 15 to 19. The researchers estimated total family EITC benefits based on federal, state, and family-size eligibility differences and payment levels during the study period.

Income support for low- and middle-earning families may reduce a teenager’s risk of involvement with the criminal justice system, particularly among boys, the research team concludes.

“Multiple forms of violence—child maltreatment, youth violence, intimate partner violence, suicide—are interconnected and often share the same root causes, such as lack of economic opportunities and unemployment,” Rowhani-Rahbar explains. “Policies that support income conceivably could reduce the risk of all these forms of violence.”

Payments May Cut Child Poverty Without Reducing Parents’ Workforce Participation, Says New Evidence

For the largest reduction in child poverty, expand the EITC, argue researchers with Columbia University’s Population Research Center who analyzed state anti-poverty programs.⁶

They assessed the potential impact on child poverty levels if all states adopted policies that mirrored the most generous and inclusive states on the EITC and three other key state-administered programs—state CTC, Supplemental Nutrition Assistance Program (SNAP, formerly Food Stamps), and Temporary Assistance for Needy Families (TANF).

Adopting the most generous state EITC policy from 2010 would have the biggest effect on child poverty—nearly 2.7 million children’s families would no longer live below the poverty line, they found.

And a total of 5.5 million children would be pulled out of poverty “if all states adopted the most generous or inclusive state policy in all four policy areas,” they reported, noting that the true number is likely higher.

Another recent study found that the temporary 2021 CTC expansion reduced food insecurity and material hardship for the lowest-income families without affecting their workforce participation.⁷

Researchers with the University of Michigan’s Population Studies Center report that the CTC recipients they surveyed—predominantly single mothers with monthly incomes below $2,000—were much less likely to report they had trouble providing food for their families and somewhat less likely to have problems paying other bills such as for utilities. The researchers report they found no effect on recipients’ labor force participation, which they suggest should reassure policymakers concerned that the expanded CTC payment are a disincentive to work.

While the UW studies examining the impact of tax credits on violence were not designed to compare the full costs and benefits of EITC and CTC expansion, in Hill’s view, these anti-poverty policies can be cost effective, as childhood experiences have lifelong implications for health and well-being.

“Interventions in childhood have the largest potential for returns on investment,” explains Hill. “Reductions in child maltreatment and youth convictions have substantial benefits for both children, parents, and society because the systems designed to deal with those problems are so costly.”

The costs of administering the EITC and CTC are low compared to other types of programs such as a preschool or employment training, Hill argues. “When you put together relatively low administration costs with potentially huge savings on interactions with the child welfare and criminal justice systems, we can be optimistic about the benefits outweighing the costs in the long run.”

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: University of Washington, Columbia University, and University of Michigan.

References

[1] Kalee Burns, Liana Fox, and Danielle Wilson, “Child Poverty Fell to Record Low 5.2% in 2021,” U.S. Census Bureau, Sept. 13, 2022.

[2] Internal Revenue Service, “Earned Income and Earned Income Tax Credit (EITC) Tables.”

[3] Nicole L. Kovski et al., “Short-Term Effects of Tax Credits on Rates of Child Maltreatment Reports in the United States,” Pediatrics 150, no. 1 (2022): e2021054939.

[4] Kimberly Dalve et al., “Earned Income Tax Credit and Youth Violence: Findings from the Youth Risk Behavior Surveillance System,” Prevention Science 23, no. 8 (2022): 1370-1378.

[5] Caitlin A. Moe et al., “Cumulative Payments Through the Earned Income Tax Credit Program in Childhood and Criminal Conviction During Adolescence in the US,” Journal of the American Medical Association 11, no. 5 (2022): e2242864.

[6] Jessica Pac et al., “Reducing Poverty Among Children: Evidence From State Policy Simulations,“ Children and Youth Services Review 115 (2020): 105030.

[7] Natasha Pilkauskas et al., “The Effects of Income on the Economic Wellbeing of Families With Low Incomes: Evidence From the 2021 Expanded Child Tax Credit,” National Bureau of Economic Research (NBER), Working Paper 30533, October 2022.

Half of older American parents who need help at home with daily activities are not getting that assistance, new analysis of the nationally representative National Health and Aging Trends Study (NHATS) data shows.¹

“We find that unmet needs are quite high among older adults with care needs,” says Sarah Patterson of the University of Michigan’s Population Studies Center and lead author of the study. Unmet needs refer to going without things like showering, getting dressed or having clean laundry, or eating hot meals because of a lack of help, she explains.

Older people with paid in-home caregivers are more likely to go without such help than their peers in residential care facilities and are more likely to miss medication, sit in soiled clothing, or skip meals, finds another new study of the NHATS data.² And older people with dementia face an especially high risk of unmet need, a third new study shows.³

Spouses and adult children provide most of the care for older Americans who need help; however older adults in stepfamilies are half as likely to get help from adult children than those with only biological children, a difference Patterson and colleagues call the “step gap” (see figure).

FIGURE. Older Parents With Only Biological Children Are More Than Twice as Likely to Receive Care From Their Adult Children Than Those in Stepfamilies

Source: Sarah E. Patterson et al., “Care Received and Unmet Care Needs Among Older Parents in Biological and Step Families,” The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 77, Supplement 1 (2022): S51-62.

“The step gap may show up because of complex relationships within stepfamilies, but it could also reflect a dilution in resources,” explains Patterson. “When there is remarriage, there are simply more parents who need care—pulling some adult children in multiple directions and forcing them to choose whom to support based on the time and money they have available.

“In the future, parents may have to shift how they find care—that is, begin to change their expectations of their children and instead rely on friends or other sources of care,” she suggests.

Older Adults With Paid Home Caregivers Are More Likely to Have Unmet Needs Than Those in Residential Care

Older adults receiving paid care in their homes face twice the odds of experiencing the consequences of unmet need than those living in residential care settings, such as a personal care homes or assisted living facilities, Meghan Jenkins Morales and Stephanie Robert of the University of Wisconsin – Madison find.⁴

Their study used 2015 and 2017 NHATS data to explore the relationship between negative consequences of unmet need and type of care arrangement, focusing on older adults receiving assistance with at least one self-care, mobility, or household activity because of their health or physical functioning.

In both years, the risk of having persistent unmet needs for care was more than four times higher among older adults receiving paid care in their homes compared with their peers in residential facilities.

Improving paid care arrangements to meet the needs of older adults should be a top priority, Jenkins Morales and Robert argue.

“Older adults receiving paid care face significant and consequential gaps in care, particularly in comparison to other care arrangements,” they write. Simple solutions such as installing grab bars and shower seats could improve access and independence, particularly for those who may be less comfortable receiving help bathing, they note.

Often, unmet needs involve not receiving enough hours of care or receiving poor quality care or care that does not match individual needs, such as help with laundry but not food preparation. Evidence shows that care is often insufficiently coordinated and that better communication among paid and unpaid caregivers and other health care providers is needed, they report.

Because of high costs, residential care is usually only an option for older adults with significant financial resources, Morales and Robert point out. Their findings can “provide additional impetus for advocates and policymakers to consider how to promote equitable access to quality residential care,” they argue.

Older Adults With Lower Incomes and Dementia Are More Likely to Face Consequences of Unmet Need

Due to the long and costly course of dementia, older adults with the condition often deplete their financial resources and ultimately become dual-enrollees, or participants in both Medicaid and Medicare, says Chanee Fabius of Johns Hopkins University.⁵ Dual enrollees typically have more limited financial resources and social support than those on Medicare alone, she explains.

Their study used 2011 to 2015 NHATS data on dual-enrollees with disabilities living in the community rather than residential care facilities.  Among those receiving paid help, those with dementia faced higher odds of experiencing adverse consequences related to unmet care needs than those without dementia, they found. In addition, those with dementia were more likely to use paid help if they lived in a state with more generous Medicaid-related home- and community-based services.

These findings underscore the complexity of supporting dual-enrollees with dementia living in the community, Fabius explains.

Although Medicaid has shifted funds from nursing home services to home-based services, more than 700,000 people were on waitlists in 40 states in 2017, she reports. “Dual-enrollees may be unable to afford all the care they need, particularly the extensive assistance needed by people with more advanced dementia,” she says.

“Caregiving is often a collaborative effort between paid helpers and family and other unpaid caregivers,” she says, seconding Jenkins Morales and Robert’s call for better coordination and communication among those providing care.

“When there are gaps in care, family and unpaid caregivers are often left to help, especially those assisting an older adult living with dementia,” Fabius reports. “Caregivers may feel unprepared for this role and may be juggling other responsibilities, such as child care and paid employment.”

Fabius says the findings also demonstrate the need for more generous and accessible Medicaid home- and community-based services for low-income people with dementia, including expanded training and wage increases for paid caregivers.

Older People in Stepfamilies Are Less Likely to Receive Help From Adult Children

Increases in divorce and remarriage and declines in fertility mean that older parents today have fewer biological children and more stepchildren than previous generations, Patterson reports. “About one in eight older adults with activity limitations has a stepchild,” she says.

Adult children may feel less obligated to care for elderly stepparents or for parents they did not live with during childhood, Patterson notes.

Older adults in need of care who have only biological children are more than twice as likely to receive care from their adult children than older adults with any stepchildren, Patterson and colleagues show (see figure).⁶ Despite this “step gap,” they found the same high rate of unmet needs—about 50%—among the two groups.

“We know that family relationships don’t exist in isolation—we all operate within a family system,” says Patterson. “When research only looks at individual relationships, like between a mother and a daughter, it might miss the dynamics of the larger family system.”

The researchers used 2015 NHATS data on more than 2,000 older parents, examining the kind of care they receive, including who is providing care and whether they have unmet needs. The researchers also considered whether the parents received any paid care over the previous month, whether they were married or living with a partner, and whether they had received care from their partner over the previous month.

Even among those with partners who could care for them, older adults with only biological children were more likely to receive help from their adult children than those with a stepfamily, they found. But those living with partners had the same level of unmet need, whether they had any stepchildren or just biological children.

“Even if older people have a partner or an adult child to care for them, older adults in the U.S. still have high rates of unmet need for care,” Patterson says. “Partners and children are seen as front-line caregivers. We expect they will take care of older family members, and I think what our study says is that partners and children might need help doing so.”

That help could take many forms, from programs offering respite care and home modifications to skills training and counseling on benefits. Policies such as paid family leave, paid sick leave, and tax credits to help cover family caregiving expenses could make a difference, she notes.

References

[1] Sarah E. Patterson et al., “Care Received and Unmet Care Needs Among Older Parents in Biological and Step Families,” The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 77, Supplement 1 (2022): S51-62.

[2] Meghan Jenkins Morales and Stephanie A. Robert, “Examining Consequences Related to Unmet Care Needs Across the Long-Term Care Continuum,” The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 77, Supplement 1 (2022): S63–73.

[3] Chanee D. Fabius et al., “Associations Between Use of Paid Help and Care Experiences Among Medicare-Medicaid Enrolled Older Adults With and Without Dementia,” The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences (2022).

[4] Morales and Robert, “Examining Consequences Related to Unmet Care Needs Across the Long-Term Care Continuum.”

[5] Fabius et al., “Associations Between Use of Paid Help and Care Experiences Among Medicare-Medicaid Enrolled Older Adults With and Without Dementia.”

[6] Patterson et al., “Care Received and Unmet Care Needs Among Older Parents in Biological and Step Families.”

Read in French

Since the 1980s, PRB’s Policy Fellows program has been grounded in the belief that while research often has profound policy implications, it must be communicated effectively to a variety of nontechnical audiences to have an impact. The program, with nearly 400 alumni from 65 countries, builds the skills of young researchers, typically Ph.D. students, to translate evidence into action.

Until recently, PRB administered the Policy Fellows program as an individual capacity strengthening initiative, preparing fellows for effective policy communication leadership wherever their careers may take them. Fellows complete an intensive training course based on the policy communication toolkit of the USAID-funded PACE project, which focuses on population and reproductive health. Today, many former fellows hold successful careers in the policy space, and many also serve in leadership roles.

However, PRB recognized that involving institutions in the program would help promote local leadership and sustainability. In 2020, PACE rolled out the program in Francophone Africa under a new vision: to cultivate teams of policy communication experts based at regional universities and research institutions who can then directly administer the training year after year.

To build our institutional partnerships, PRB explored and received interest in the program from three prominent West African research institutions: the Higher Institute of Population Sciences (Burkina Faso); the Institute for Demographic Training and Research (Cameroon); and the Institute of Population, Development, and Reproductive Health (Senegal). The Ouagadougou Partnership Coordination Unit, a partnership that supports the nine Francophone countries in West Africa to accelerate the implementation of family planning interventions, and PACE selected 15 Policy Fellows from the region, including graduate students at the partner institutions. Participants, representing five Ouagadougou Partnership countries and Cameroon, conducted research on demographic transitions, family planning and reproductive health, and maternal and child health.

Facilitators for the program, alongside PACE, were identified from faculty at each of the three institutions. Professor Parfait Eloundou-Enyegue, a renowned demographer at Cornell University and a Policy Fellows alumnus, taught the facilitators how to administer PRB’s training using an innovative teaching method—designed in collaboration with the three professors to reinforce the professional relationship between the students and professors in a Francophone context.

The trained facilitators, PACE, and the Ouagadougou Partnership co-hosted a virtual policy communication training for the 15 fellows from October 22 through Nov. 6, 2020. The training was followed by six months of practical work during which students wrote scientific articles and produced analyses of political landscapes, presenting them during discussions with policy decisionmakers.

In August 2021, PRB advanced the program with a training-of-trainers led by Professor Eloundou-Enyegue, working with teams of 2020 program fellows affiliated with the Institute for Demographic Training and Research (IFORD), the Higher Institute of Population Sciences (ISSP), and the Center of Excellence for Research in Generational Economics (CREG), based in Senegal. Over the next eight months, IFORD, ISSP, and CREG each adapted the training materials for their local contexts and priorities and cascaded the training to Ph.D. and master’s-level students and professionals in their respective institutions, with PACE providing technical support, mentoring, and feedback to course facilitators. The Policy Fellows program was also expanded to institutions in Anglophone Africa.

This transitioned approach enabled PRB’s policy communication methods to reach actors at multiple levels of the policy development process. While ISSP and IFORD train the current and next generation of researchers and civil servants, respectively, CREG reaches senior researchers who already occupy positions as advisers to decisionmakers. In a promising sign for sustainability, all three institutions plan to offer the program independently in future years, long after the end of the PACE project.

The integration of PACE’s policy communication modules in the curriculum of three renowned institutions revealed an appetite to strengthen the interface between research and practice and informed policy change in West Africa. Gervais Beninguisse, a professor at IFORD, explained: “This training provides significant added value in the face of the gap observed among graduates who are called upon to play a role in steering the statistical information systems of their countries, as well as in the face of the persistent and worrying insufficiency of the use of research results to enlighten the decision-making processes in the area of public policy in Francophone Africa.”

Read in English

Depuis les années 1980, le programme de Communication pour les Politiques de PRB est ancré dans la conviction que si la recherche a souvent de profondes répercussions sur les politiques, elle doit être communiquée efficacement aux divers publics non techniques pour avoir un impact. Le programme, qui a formé près de 400 étudiants issus de 65 pays, renforce les compétences de jeunes chercheurs, généralement des doctorants, afin qu’ils puissent traduire les données en actions.

Jusqu’à récemment, PRB a administré ce programme comme une initiative de renforcement des capacités individuelles, préparant les étudiants à un leadership efficace en matière de communication pour le changement politique, où que leur carrière les mène. Ces derniers ont suivi un cours intensif basé sur la boîte à outils de communication pour les politiques du projet PACE, financé par l’USAID, qui se concentre sur la population et la santé reproductive. Aujourd’hui, de nombreux anciens étudiants mènent une carrière réussie dans la sphère des politiques publiques, et nombre d’entre eux occupent également des fonctions de direction.

Cependant, PRB a reconnu que l’implication des institutions de recherche dans le programme contribuerait à promouvoir le leadership local et la durabilité. En 2020, PACE a déployé le programme en Afrique francophone suivant une nouvelle vision : cultiver des équipes d’experts en communication pour les politiques, basées dans les universités et les institutions de recherche régionales pouvant ensuite dispenser directement la formation année après année.

Pour établir ses partenariats institutionnels, PRB a exploré et suscité l’intérêt de trois institutions de recherche ouest-africaines de renommée : l’Institut supérieur des sciences de la population (ISSP – Burkina Faso) ; l’Institut de formation et de recherche démographiques (IFORD – Cameroun) ; et l’Institut de la population, du développement et de la santé reproductive (IPDSR – Sénégal). L’Unité de Coordination du Partenariat de Ouagadougou (UCPO), soutenant les neuf pays francophones d’Afrique de l’Ouest pour accélérer la mise en œuvre des interventions de planification familiale, et PACE ont sélectionné quinze doctorants de la région, incluant des étudiants diplômés des institutions partenaires. Les participants, représentant cinq pays du Partenariat de Ouagadougou et le Cameroun, menaient des recherches sur les transitions démographiques, le planning familial et la santé reproductive, ainsi que la santé maternelle et infantile.

Les enseignants du programme, aux côtés de PACE, ont été identifiés parmi les professeurs de chacune des trois institutions. Le professeur Parfait Eloundou-Enyegue, démographe de renom à l’université de Cornell et ancien étudiant du programme de communication pour les politiques, a préparé les enseignants à dispenser la formation de PRB suivant une méthode pédagogique innovante – conçue en collaboration avec les trois professeurs pour renforcer la relation professionnelle entre étudiants et professeurs dans un contexte francophone.

Les enseignants formés, PACE, et l’UCPO ont co-organisé une formation virtuelle en communication pour les politiques pour les quinze doctorants du 22 octobre au 6 novembre 2020. La formation a été suivie de six mois de travaux pratiques au cours desquels les étudiants ont rédigé des articles scientifiques, produit des analyses de paysages politiques et les ont présentées lors de discussions avec des décideurs.

En août 2021, PRB a fait évoluer le programme avec une formation de formateurs dirigée par le professeur Eloundou-Enyegue, travaillant avec des équipes d’étudiants du programme 2020 affiliés à l’IFORD, l’ISSP et au Centre d’excellence pour la recherche en économie générationnelle (CREG – Sénégal). Au cours des huit mois suivants, l’IFORD, l’ISSP et le CREG ont chacun adapté le matériel de formation à leur contexte et priorités locales et ont dispensé la formation à des étudiants de niveau doctorat et master et des professionnels dans leurs institutions respectives, PACE fournissant un soutien technique, un encadrement et un retour d’information aux enseignants. Le programme de communication pour les politiques a également été étendu aux institutions d’Afrique anglophone.

Cette approche transitionnelle a permis aux méthodes de communication pour les politiques de PRB d’atteindre les acteurs à plusieurs niveaux du processus d’élaboration des politiques. Alors que l’ISSP et l’IFORD forment respectivement la génération actuelle et la prochaine génération de chercheurs et de fonctionnaires, le CREG touche les chercheurs seniors qui occupent déjà des postes de conseillers auprès des décideurs. Signe prometteur pour la durabilité, les trois institutions prévoient de dispenser le programme de manière indépendante dans les années à venir, bien après la fin du projet PACE.

L’intégration des modules de communication pour les politiques de PACE dans le cursus de trois institutions renommées a révélé un appétit pour renforcer l’interface entre la recherche et la pratique, et le changement de politique informé en Afrique de l’Ouest. Gervais Beninguisse, professeur à l’IFORD, explique : “Cette formation apporte une valeur ajoutée significative face au déficit observé chez les diplômés appelés à jouer un rôle dans le pilotage des systèmes d’information statistique de leurs pays, ainsi que face à l’insuffisance persistante et préoccupante de l’utilisation des résultats de la recherche pour éclairer les processus de décision en matière de politiques publiques en Afrique francophone.”

Prior to the COVID-19 pandemic, Appalachia’s median household income and labor force participation were both on the rise, and poverty rates were declining. But longstanding vulnerabilities suggest that some groups in the Appalachian Region risked greater hardship related to the pandemic, including older adults with disabilities, households without internet access, and residents of the Region’s most rural counties.

The Appalachian Region: A Data Overview From the 2016-2020 American Community Survey, a new PRB report for the Appalachian Regional Commission, provides a comprehensive picture of social and economic conditions in Appalachia before and during the first 10 months of the COVID-19 pandemic. As more data from the pandemic and post-pandemic periods become available in the coming years, this report can serve as a benchmark of comparison for future analysis.

“Although the report data do not measure the pandemic’s social and economic impact beyond 2020, they do allow Appalachian program planners and policymakers to pinpoint areas and population subgroups most at risk and enable them to better target assistance,” said Kelvin Pollard, senior demographer at PRB, who coauthored the report with Linda A. Jacobsen, PRB senior fellow.

Drawing from the latest American Community Survey and U.S. Census Bureau Population Estimates, the report contains more than 300,000 data points comparing Appalachia’s regional, subregional, state, and county levels with the rest of the nation.

Appalachia’s Economy Was Improving Before COVID-19

During the 2016-2020 period (which includes the four years leading up to the pandemic), Appalachia was improving across several measures. Data suggest that much of the Region had finally recovered from the 2007-2009 recession, though this recovery was slower than in most of the nation.

• Median household income increased nearly 10% between 2011-2015 and 2016-2020, with 83 of 423 Appalachian counties throughout the Region experiencing increases of at least 15%.
• Appalachia’s overall poverty rate (14.7%) decreased 2.4 percentage points between 2011-2015 and 2016-2020.
• Labor force participation (73.8%)—though 4 percentage points lower than the national average—increased by 1.1 percentage points between 2011-2015 and 2016-2020, surpassing the national increase of 0.8 percentage points.

Another bright spot is that Appalachia’s residents are slightly less likely to be without health insurance at nearly all ages than other U.S. residents; young adults ages 26 to 34 are the only exception.

Pandemic May Be Compounding Disadvantages Related to Poverty, Aging, Disability, and Lagging Internet Access

Despite positive trends, the report revealed vulnerabilities that may have been exacerbated by the COVID-19 pandemic’s health, social, and economic impacts.

• Regional poverty rates have declined overall, but rates have stayed the same or increased in 85 Appalachian counties.
• Fewer Appalachian households had a broadband subscription compared with households elsewhere in the nation (80.7% compared with 85.2%). In 26 Appalachian counties, the prevalence of subscriptions was less than 65%. This digital divide, even within the Region itself, impacts residents’ access to remote work, online learning, telehealth, and more.
• The percentage of Appalachian households receiving payments from the federal Supplemental Nutrition Assistance Program (SNAP) (formerly known as Food Stamps) was higher (more than 13%) compared to non-Appalachian households (more than 11%), with households in Central Appalachia reaching almost 21%. For households with children under age 18, Appalachia’s SNAP participation rate is higher than the national rate (21% v. 18%).
• The proportion of working-age adults (ages 25 to 64) with a bachelor’s degree was 15.8% in Central Appalachia, 18.2% in rural Appalachian counties, and 26.9% Region-wide, compared with 34.3% nationally.
• Nearly three-fourths (73.8%) of Appalachia’s working-age adults were in the labor force compared with 78.2% nationwide. Only 60.5% were in the labor force in Central Appalachia. Counties with higher labor force participation rates also tend to have higher levels of educational attainment.
• The share of Appalachia’s residents ages 65 and older was just over 19% in 2020, more than 2 percentage points above the national average. Additionally, the share of Appalachians ages 65 and older with a disability was more than 3 percentage points higher than the national rate.

“With persons ages 65 and older particularly vulnerable to COVID-19 complications, communities with the largest share of older adults have similarly been at risk of higher illness and death rates because of the pandemic,” Pollard points out.

Rural Appalachia More Disadvantaged Than the Rest of the Rural United States

The report’s data show that not only are Appalachia’s rural areas more vulnerable than its urban areas, but the Region’s 107 rural counties also face greater disadvantages than 841 similarly designated rural counties in the rest of the country.

• Population decline was much faster in rural Appalachia between 2010 and 2020 than in rural counties in the rest of the country—3% versus 0.6%.
• Educational attainment among adults ages 25 to 64 in rural Appalachia lagged about 4 percentage points behind that in rural counties outside the Region in 2016-2020, both in terms of high school and college completion.
• At $42,403, median household income in rural Appalachian counties was about $9,500 lower than median income in rural counties outside the Region. In rural Appalachia, 20% of residents live in poverty compared with 15.4% in the rest of the rural United States.
• Housing stock also differed: Mobile homes made up nearly 20% of residences in rural Appalachia compared with just under 12% in the rest of the rural United States.
• As was true with Appalachia as a whole, labor force participation in the Region’s rural counties was lower than in rural counties outside the Region (65% v. 74%).
• Workers in rural Appalachia were also much more likely to work outside their county of residence (32% v. 20%) and have commutes of 30 minutes or more (31% v. 22%).
• The digital divide was wider in rural Appalachian counties than elsewhere in the rural United States. More than one-fifth of rural Appalachian households (22%) had no internet access—4 percentage points higher than in other rural counties. And the share with at least 1 computer device in the household (83%) and the share with a broadband subscription (74%) were both more than 4 percentage points lower in rural Appalachia. With most schools closed throughout much of 2020 due to the COVID-19 pandemic, this rural digital divide likely made online education even more challenging for children in Appalachia’s rural counties.
• Disability rates were higher in rural Appalachia than in other rural areas of the country (20% v. 16%). Indeed, they were higher among all age groups, with at least a 5-percentage point gap among residents ages 35 to 64 and ages 65 and older.
• SNAP participation rates in rural Appalachia were also higher than in rural areas outside the Region (17% v. 13%). More than one-fourth of households with children in rural Appalachia (26%) received SNAP benefits compared with slightly more than one-fifth of households with children in the rest of the rural United States (21%).

“The report indicates that conditions were already more challenging in rural Appalachia up through the first 10 months of the pandemic than in rural areas outside the Region. These data also provide an important baseline for future assessments of the differential impact of the pandemic on rural Appalachia compared with rural areas in the rest of the country,” says Jacobsen.

The Appalachian Region encompasses 206,000 square miles along the Appalachian Mountains from southern New York to northern Mississippi, including portions of 12 states and all of West Virginia.

The Appalachian Regional Commission report uses data from the 2016-2020 American Community Survey and the Census Bureau’s vintage 2020 population estimates—the most recent data available for the characteristics studied. It includes detailed tables and county-level maps covering state- and county-level data on population, age, race and ethnicity, housing occupancy and tenure, housing type, education, computer ownership and internet access, labor force participation, employment and unemployment, transportation and commuting, income and poverty, health insurance coverage, disability status, migration patterns, and veteran status. It also includes a detailed comparison of characteristics in rural Appalachian counties with those outside the Region.

About the Appalachian Regional Commission

The Appalachian Regional Commission is an economic development agency of the federal government and 13 state governments focusing on 423 counties across the Appalachian Region. ARC’s mission is to innovate, partner, and invest to build community capacity and strengthen economic growth in Appalachia to help the Region achieve socioeconomic parity with the nation.

The life expectancy of Californians has decreased by about three years as a result of the COVID-19 pandemic, according to a study by National Bureau of Economic Research (NBER)-affiliated researchers and colleagues published in the Journal of the American Medical Association.¹

The research shows that life expectancies for Hispanic, Black, and Asian Californians decreased more than for white Californians (see Figure 1). Hispanic populations in California lost 5.7 years of life expectancy between 2019 and 2021, while Black populations lost 3.8 years, Asian populations lost 3.0 years, and white populations lost 1.9 years, according to the study led by Hannes Schwandt, a Northwestern University professor and NBER affiliate.

FIGURE 1. Latino, Black, and Asian Californians Saw Significant Drops in Life Expectancy During the COVID-19 Pandemic

Change in Life Expectancy (in Years) in California by Race/Ethnicity, 2019-2021

Note: Black, Asian, and white categories are non-Hispanic.

Source: Hannes Schwandt et al., “Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021,” JAMA 328, no. 4 (2022): 360-66, doi:10.1001/jama.2022.10952.

 “In California, Hispanic individuals have historically lived longer than white individuals, but the pandemic upended that, as the life expectancy for Hispanic Californians decreased by about six years, three times as high as the decline for white Californians,” said co-author Jonathan Kowarski, a University of California, Los Angeles doctoral student in economics.

The study also found that life expectancy for those living in the lowest-income census tracts fell by nearly five years between 2019 and 2021 (from 75.9 to 71.1 years) compared with less than one year for those living in the highest-income census tracts (from 87.4 to 86.6 years) (see Figure 2). The gap in life expectancies between the two groups grew, from a difference of about 11.5 years before the pandemic to more than 15 years in 2021. During this time, income also became more tightly correlated with life expectancy than it had been previously.

FIGURE 2. Low-Income Neighborhoods in California Saw Significant Drops in Life Expectancy During the COVID-19 Pandemic

Change in Life Expectancy Since 2019 (in Years) in California Census Tracts With Lowest and Highest Median Household Incomes

Source: Schwandt et al., “Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021,” JAMA 328, no. 4 (2022): 360-66.

“We’ve had indications that the pandemic affected economically disadvantaged people more strongly, but we never really had numbers on actual life expectancy loss across the income spectrum,” said Schwandt. “I am shocked by how big the differences were and the degree of inequality that they reflected.”

In their analysis of 1.9 million deaths in California between 2015 and 2021, the research team calculated that life expectancy for Californians fell from 81.4 years in 2019 to 79.2 years in 2020, and down to 78.4 years in 2021. This study demonstrates that the reduction in life expectancy continued from 2020 into 2021, despite the availability of vaccines for much of 2021.

Life expectancy is not the average life span of individuals in a society, but a hypothetical measure based solely on the mortality rates observed in a given year. It estimates how long a cohort of newborns could expect to live if it experienced the mortality rates of that specific year throughout their entire lifetimes.

In the current study, life expectancy captures how much life was lost collectively within a population during the pandemic years, and it illustrates the dramatic differences in the pandemic’s impact across communities of different socioeconomic status.

“Our results highlight the disproportionate burden the pandemic placed on low-income people and people of color,” said study co-author Janet Currie, a Princeton University professor and NBER affiliate.

The study is based on an analysis of restricted death data obtained from the California Comprehensive Death Files maintained by the California Department of Health.

“Our findings are another troubling sign of how the pandemic’s impact was not felt evenly across all communities,” said study co-author Till von Wachter, a UCLA professor and NBER affiliate. “Policymakers can use these findings to craft a more equitable response now and also to inform how we plan for future public health crises.”

This article is based on pieces written by Sean Coffey of the California Policy Lab and Max Witynski of Northwestern University.

References

¹ Hannes Schwandt et al., “Changes in the Relationship Between Income and Life Expectancy Before and During the COVID-19 Pandemic, California, 2015-2021,” Journal of the American Medical Association 328 no. 4 (2022): 360-66, doi:10.1001/jama.2022.10952.