With the number of coronavirus infections surging, school districts across the United States are grappling with decisions about whether and how to re-open this fall. For online learning to be effective, students need access to computers and high-speed internet access, but a new analysis and interactive dashboard by PRB show sharp digital and economic divides among school-age children across states and between racial and ethnic groups.
In 2018, roughly 10% of U.S. children ages 5 to 17 did not have a computer—desktop, laptop, or tablet—at home, and 23% did not have home access to paid high-speed internet.1 Fully one-fourth of all school-age children were lacking either a computer or high-speed internet. Children without computers or high-speed internet at home were already at an educational disadvantage before the coronavirus pandemic due to the growing need for students to access resources and submit assignments online. Many relied on computers and internet access at school or a local library to complete their work. As the pandemic prompted libraries to close and schools across the country shut down and moved to online instruction, this digital divide has become even more critical.
The Digital Divide Is Wider for Children of Color
A racial and ethnic digital divide also persists. Half of all American Indian/Alaska Native children lack either computers or paid high-speed internet access (or both) at home (see Table 1). More than one-third of Black and Latinx children lack computers or high-speed internet at home, compared with only one-fifth of non-Hispanic white children and one in seven Asian/Native Hawaiian and Other Pacific Islander (NHOPI) children.
Economic barriers contribute to the digital divide between racial and ethnic groups. Poverty rates range from 10% among non-Hispanic white children ages 5 to 17 to 31% among Black children. American Indian/Alaska Native and Latinx children also have poverty rates far above the national average of 17%.
Black, American Indian/Alaska Native, and Latinx school-age children are two to three times more likely to live in households receiving Supplemental Nutrition Assistance Program (SNAP) benefits than white or Asian/Native Hawaiian and Other Pacific Islander children. Nearly 40% of Black and 35% of American Indian/Alaska Native school-age children live in households receiving SNAP benefits. Children whose households receive SNAP benefits are automatically eligible to receive free meals at school—which provide an essential source of daily nutrition for many of these children. With schools shut down, children of color whose families live in poverty and receive SNAP benefits are at much greater risk of going hungry and not receiving the nutrition they need during the pandemic.
Table. Children of Color More Likely to Lack Computers and High-Speed Internet at Home
Economic and Digital Divides of Children Ages 5 to 17 by Race and Ethnicity, 2018
Predicted Minutes of Mother’s Time in Activities, by Marital Status
Notes: Computers include desktops, laptops, or tablets. All racial groups are non-Hispanic, and Hispanics can be of any race. NHOPI is Native Hawaiian and Other Pacific Islander.
Source: PRB analysis of data from the U.S. Census Bureau’s 2018 American Community Survey Microdata Sample.
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Nearly half (47%) of school-age children who live in poverty and 43% of those who receive SNAP benefits lack access to either computers or high-speed internet, compared with only 21% of children who do not receive SNAP benefits and whose family incomes are above the poverty line.
Within every racial and ethnic group, the share of school-age children without access to computers or high-speed internet is much higher for those who receive SNAP benefits and those who live in poverty (see Figure 1). While half of all American Indian/Alaska Native children lack access to computers and high-speed internet, this share jumps to 61% for those receiving SNAP benefits and nearly 70% among those living in poverty. Among non-Hispanic white children, the share without access to computers and high-speed internet nearly doubles from 20% to 39% for those receiving SNAP benefits and from 20% to 41% for those in poverty. Lower levels of economic well-being are widening racial and ethnic gaps in access to computers and high-speed internet.
FIGURE 1.Low Levels of Economic Well-Being Contribute to the Digital Divide
Percent of Children Ages 5 to 17 in Different Racial/Ethnic Groups Lacking Access to Computers or High-Speed Internet by Poverty Status and Receipt of SNAP Benefits, 2018
Notes: All racial groups are non-Hispanic, and Hispanics can be of any race. AIAN is American Indian/Alaska Native. NHOPI is Native Hawaiian and Other Pacific Islander. Without access means without access to either a computer, high-speed internet, or both at home.
Source: PRB analysis of data from the U.S. Census Bureau’s 2018 American Community Survey Microdata Sample.
Economic and Digital Divides Vary Across States
States vary widely in their shares of school-age children without access to computers and high-speed internet, from a low of 13% in New Hampshire to a high of 46% in Mississippi. In eight states—Alabama, Arkansas, Louisiana, Mississippi, New Mexico, Oklahoma, Tennessee, and Texas—more than 30% of children lack either or both computers and internet access, but this count rises to 31 states (including the District of Columbia) for minority children. More than half of school-age minority children in Mississippi, Arkansas, and South Dakota lack access to computers and high-speed internet at home.2
The economic divide is also present in every state. Twelve states (including the District of Columbia) have more than 20% of children ages 5 to 17 living in poverty, and 24 states (including the District of Columbia) have more than 20% of school-age children living in households receiving SNAP benefits. Concentration of poverty and SNAP receipt among school-age minority children is much higher than among non-minority children and is widespread across states. There are only 10 states where the share of minority children living in poverty drops below 20% and only three states (Utah, Vermont, and Wyoming) where the share in households receiving SNAP benefits falls below 20%. Conversely, nine states (Alabama, Arkansas, Louisiana, Mississippi, Ohio, South Carolina, South Dakota, Tennessee, and West Virginia) have one-third or more of minority school-age children living in poverty, and 22 states have more than one-third living in households receiving SNAP benefits.
Reading and Math Proficiency Are Linked to Economic and Digital Divides
Economic and digital divides among school-age children are linked to differences in reading and mathematics proficiency levels across states and between racial and ethnic groups. Proficiency in reading by the end of third grade is an important marker of overall educational development but, beginning in fourth grade, it is also essential for learning other subjects and keeping up academically.3 Children who reach fourth grade without being able to read proficiently are more likely to drop out of high school—reducing their earnings potential and chances for success.4 Similarly, proficiency in mathematics fundamentals makes college attendance and completion more likely, which increases earnings potential.5
In 2019, a shocking two-thirds of all fourth graders in the United States scored below the proficient level in reading, as did two-thirds of eighth graders in math. However, these shares are much higher among children in the racial and ethnic groups with the highest levels of poverty and receipt of SNAP benefits, and the least access to computers and high-speed internet (see Figure 2).
Figure 2. Two-Thirds of U.S. Students Score Below Proficient Level in Reading and Mathematics
Reading and Math Proficiency of Children by Race and Ethnicity, 2019
Notes: All racial groups are non-Hispanic. NHOPI is Native Hawaiian and Other Pacific Islander.
Source: PRB analysis of data from the the U.S. Department of Education’s 2019 National Assessment of Education Progress (NAEP) database.
Among Black and American Indian/Alaska Native students, at least 80% of fourth graders scored below the proficient level in reading, and 85% or more of eighth graders scored below the proficient level in math. More than three-quarters of Latinx fourth graders scored below the proficient level in reading and math in 2019. With higher levels of economic well-being and access to computers and high-speed internet, the shares of non-Hispanic white and Asian/Native Hawaiian and Other Pacific Islander children scoring below the proficient level in reading and math are much lower. These gaps in basic reading and math skills make it hard to envision how today’s children can become tomorrow’s productive workers in a globally competitive economy. In outlining his education policy in 2009, President Barack Obama argued that “The relative decline of American education is untenable for our economy, it’s unsustainable for our democracy, and it’s unacceptable for our children—and we cannot afford to let it continue.”6
States also vary widely in reading and math proficiency levels. For example, the share of fourth graders who scored below the proficient level in reading ranged from a low of 55% in Massachusetts to a high of 76% in New Mexico, while at least seven out of 10 children scored below proficient in reading in eight states. The share of eighth graders who scored below proficient in math ranged from a low of 53% in Massachusetts to a high of 79% in New Mexico, while at least seven out of 10 eighth graders scored below the proficient level in math in 17 states in 2019.
The low levels of proficiency in reading and math among children of color are even more concerning given the fact that minorities make up a growing share of the school-age population. The share of school-age children who are members of a racial or ethnic minority ranges from a low of 7% in Vermont to a high of 80% in the District of Columbia. Among minority students, only two states—Hawaii and Vermont—had fewer than seven out of 10 fourth graders who scored below the proficient level in reading. However, no states had fewer than seven out of 10 minority eighth graders who scored below proficient in math. For example, nearly half (48%) of school-age children in Louisiana belong to a racial or ethnic minority group, and 84% of minority fourth graders scored below proficient in reading while 88% of minority eighth graders scored below proficient in math. Taken together, roughly 40% of all fourth and eighth graders in Louisiana had already fallen behind academically even before the coronavirus pandemic hit and schools closed.
As schools shut down in spring 2020, some districts like Los Angeles Unified tried to address the digital divide by distributing laptops to all students who needed them. In addition, some districts provided internet access to students without it by distributing hot spots or data plans. However, these solutions were not economically feasible in many districts serving low-income communities of color such as Prince George’s County Public Schools in Maryland. With 10 of the 15 largest school districts already deciding to begin the new school year online as of early August, reducing the digital divide and providing free and reduced-price meals for children who need them has become a daunting challenge across the country.
Unemployment rates remain at record highs, and with the supplemental $600 unemployment payments ending and Congress unable to agree on a new stimulus package, poverty levels and demand for SNAP benefits are both likely to rise this fall. A growing economic divide may further exacerbate the digital divide among school-age children, putting even more students at risk of falling further behind.
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|
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/.