Education—from prekindergarten through postsecondary—provides a crucial avenue to economic and social mobility. High-quality preschool programs, elementary schools, and high schools boost academic achievement, college enrollment, and adult success. Schools also provide children and teens with networks of friends, peers, and mentors, helping to shape their social identity and feeling of belonging. And adults can continue to build skills and credentials throughout life, expanding their prospects for upward mobility.
Jump to predictor:
- Access to preschool
- Effective public education
- School economic diversity
- Preparation for college
- Digital access
PREDICTORS
ACCESS TO PRESCHOOL
Children who have access to preschool (particularly those who can attend high-quality preschools) are better prepared than otherwise similar children to start school ready to learn the cognitive and social skills required to succeed in an academic setting and beyond. Children without preschool experience may struggle in their early school years and ultimately attain less education.
Metric: Share of children enrolled in nursery school or preschool.
This metric measures the share of a jurisdiction’s three- to four-year-old children who are enrolled in nursery school or preschool.
Validity: Federal agencies such as the National Center for Education Statistics use household survey data to ascertain nursery and preschool enrollment.
Availability: Enrollment data are available annually from the Census Bureau’s American Community Survey (ACS) and Public Use Microdata Sample.
Frequency: New data for the metric are available annually. For subgroup analyses in less populated areas, several years of data may need to be pooled to obtain reliable estimates.
Geography: Data are available at the county and metropolitan levels.
Consistency: Information pertaining to nursery and preschool enrollment is measured the same way across all geographies in the same year and over time in the ACS.
Subgroups: The metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populated areas and for certain demographic groups, several years of data may need to be pooled to obtain reliable estimates.
Limitations: This metric can change over time if fertility patterns change or if families with young children who move out of or into the jurisdiction have very different propensities for enrolling their children in preschools than parents with young children who remain in the jurisdiction. Because ACS data do not capture the quality of preschool, enrollment figures may overstate exposure to the kinds of programs most likely to improve short-term academic outcomes and long-term outcomes such as mobility from poverty.
EFFECTIVE PUBLIC EDUCATION
Once in school, children’s cognitive and social development are supported by effective public education and quality schools. Attending lower-quality schools reduces a child’s chances of attending and succeeding at postsecondary institutions.
Metric: Average per-grade change in English Language Arts achievement between third and eighth grades.
This metric reports the average per year improvement in English Language Arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level a year. A value of 1 indicates a jurisdiction is learning at an average rate, below 1 is slower than average, and above 1 is faster than average.
Validity: State assessments are well defined and validated but vary by state. The Stanford Education Data Archive (SEDA) standardized these to be nationally comparable and comparable over time.
Availability: State assessment data are available from the SEDA.
Frequency: New data for the metric are available annually.
Geography: Data are available at the school district and county levels.
Consistency: Tests of student progress vary by state and can change over time if states modify their tests. SEDA has standardized these to be comparable over time and space.
Subgroups: SEDA has adjusted scores by race or ethnicity, income, and gender.
Limitations: Not all counties report assessments for all grades, so some estimates may be based on fewer data points. SEDA manipulated the underlying data to introduce noise to ensure confidentiality, which compromises data quality. Residential mobility into or out of a county may result in the “cohort” not being the same between third and eighth grades. The interpretation can be complicated when comparing across subgroups. For example, research suggests that annual improvement in English for Hispanic children will exceed those of non-Hispanic white children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. It is important to keep these concepts in mind when interpreting results.
SCHOOL ECONOMIC DIVERSITY
A lack of economic diversity within schools adversely affects the academic achievement of students, particularly children of color from lower-income families. Long-standing patterns of neighborhood and school segregation mean the average Black student attends a school with a much larger share of students of color and students from families experiencing poverty than the average white student. The high-poverty schools attended by Black students tend to lack the educational resources available in low-poverty schools, such as highly qualified and experienced teachers, low student-teacher ratios, college prerequisite and advanced placement courses, and extracurricular activities.
Metric: Share of students attending high-poverty schools, by student race or ethnicity.
This metric is constructed separately for each racial or ethnic group and reports the share of students attending schools in which over 40 percent of the student body receives free or reduced-price meals. Most assessments of meal programs are through the National School Lunch Program.
Validity: This metric captures the interaction of economic and racial segregation of schools and therefore reveals whether (and to what degree) students of color are more likely than white students to attend schools with large concentrations of classmates experiencing poverty. Higher concentrations of students experiencing poverty are associated with worse achievement for all the students in a school.
Availability: This metric can be constructed using information from the National Center for Education Statistics Common Core of Data through the Urban Institute’s Education Data Portal. Those data come from an annual census of schools reporting total enrollment by race across each grade. That census includes a measure of “economic disadvantage” for students based on their eligibility for free or reduced-price school meals, which is used as a proxy for poverty.
Frequency: New data for the metric are available annually.
Geography: This metric can be computed at the school district, city, and county levels. Because this metric reflects the structural conditions facing a jurisdiction’s students, changes in the metric may represent changes to those structural conditions.
Consistency: Not all states report free or reduced-price lunch. Instead, four states report the number of students directly certified. Two other states report both free and reduced-priced lunches and the number of students directly certified across schools. However, this metric overall is consistently defined and calculated for cities and counties.
Subgroups: This metric is by definition disaggregated by race or ethnicity.
Limitations: Some school districts confer eligibility for free and reduced-price school meals using community eligibility standards that can apply to clusters of schools as well as entire districts. For example, if a cluster of schools serve a set of low-income neighborhoods, and across the schools, 40 percent or more of the students qualify for free and reduced-price meals, the district can provide meals to all students at all schools in the cluster even if one of the schools wouldn’t meet the threshold on its own. Consequently, this metric may overstate student poverty exposure in those districts. Fortunately, the data sources for this metric allow us to identify the districts using this approach, and findings can be interpreted with this in mind. Changes in this metric need to be assessed with reference to changes in the area’s overall racial or ethnic composition and the poverty rate among its residents.
PREPARATION FOR COLLEGE
Being adequately prepared for college, including having a high school degree and the requisite skills to enroll in and benefit from a two- or four-year college program, indicates that individuals are prepared to build the type of skills that lead to sustained success in the labor market.
Metric: Share of 19-and 20-year-olds with a high school degree.
This metric is the share of 19- and 20-year-olds with a high school degree in a given jurisdiction relative to the total number of 19- and 20-year-olds in the jurisdiction.
Validity: Earning a high school degree is an important prerequisite for pursuing additional schooling, and although not all high school graduates are ready to enroll in college, high school completion is a well understood and widely used measure of educational attainment. Data on educational attainment are collected in a variety of federal surveys.
Availability: Data on educational attainment are available from the ACS.
Frequency: New data for the metric are available annually. For subgroup analyses in less populated areas, several years of data may need to be pooled to obtain reliable estimates.
Geography: Data are available at the county and metropolitan levels.
Consistency: Information on high school graduation is measured the same way across all geographies in the same year and over time in the ACS.
Subgroups: The metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populated areas and for certain demographic groups, several years of data may need to be pooled to obtain reliable estimates.
Limitations: Young adults moving in and out of an area can influence this metric.
DIGITAL ACCESS
Access to digital tools has a positive effect on cognitive development, educational attainment, and skill-building, all of which are strongly linked to an individual’s economic success as well as their sense of power and autonomy. Limited digital access affects students’ ability to fully participate in school, particularly as teachers assign a growing amount of homework that relies on internet access. Over 90 percent of American adults use the internet, but a digital divide exists for people with lower incomes, people of color, people residing in rural areas, people living on tribal land, and people with disabilities.
Metric: Share of households with broadband access in the home.
This is a measure of the share of households in a population that have broadband access (e.g., DSL, cable modem, or fiber) in the home.
Validity: The US Census Bureau uses a series of questions to measure aspects of digital access across the nation. Existing literature makes extensive use of these measures of digital access.
Availability: Data for this metric are publicly available nationwide through the US Census Bureau’s American Community Survey.
Frequency: Data are collected annually.
Geography: These data are available at the county, city, census tract, and block group level.
Consistency: The metric can be measured in the same way across geographies and over time, but some changes were made in 2016 to the survey questions required to construct this metric.
Structural equity and subgroups: This metric captures the racial disparities apparent in ways wealth is shared across a given population. This helps elucidate any racial or ethnic disparities in comparative housing wealth and how it has been accessed or distributed in that community, rather than focusing on an aggregate measure of total homeownership. These disparities can signal instances of racism in achieving or benefiting from homeownership.
Structural equity and subgroups: The digital divide is about the degree of inequity in digital access by demographics (including race and ethnicity) and by geography (e.g., urban versus rural). Because this metric can be disaggregated along those dimensions, it can provide insights into structural equity at the community level. The data can be disaggregated by household income and by demographics of the head of household, such as race, ethnicity, and gender.
Structural relevance: This metric measures whether individual households have broadband connections to the internet and thus reflect individual choices as well as more structural factors such as affordability and the availability of broadband services.
Limitations: Having broadband internet at home is not useful without a device from which to access it. Access to computing devices is an important component of digital access. Moreover, measuring broadband access misses other types of digital access (such as through cellular data), but existing scholarship supports a measure of broadband access as it relates to closing the digital divide. There is no universally available measure of digital access that includes both broadband access and access to computing devices. People who would like to further investigate digital access in their communities could also look at the data available through the American Community Survey on access to computing devices.
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