Summary: The educational attainment levels of the working-age population (ages 25-64). Data for 2010 and 2020 represent five-year averages (e.g. 2016-2020).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1990 and 2000 5% samples, 2010 and 2020 American Community Survey 5-year samples; U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: All people ages 25 through 64.

Methods: The number and percentage of people ages 25-64 by level of educational attainment was calculated by race/ethnicity, gender, nativity, and ancestry for each year and geography. For the map breakdown, census tract level data from the 2020 5-year American Community survey was re-estimated into 2010 census tract boundaries using a 2020 to 2010 census tract geographic crosswalk developed using 2020 block level population data from the 2020 Census Redistricting Data along with a block level geographic crosswalk (2020 to 2010 blocks) from NHGIS. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• The high school diploma category of education includes those with an actual high school diploma as well as high school equivalency or a General Educational Development (GED) certificate.
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• No data are reported if based on fewer than 500 people in the universe for the map breakdown; a lower minimum threshold for reporting of at least or 100 people was applied for census tract level estimates in the map breakdown.

Summary: The share of the population ages 16 to 24 who are not working or enrolled in school. The map breakdown shows the share of the population ages 16 to 19 who are not working or enrolled in school. Data for 2010 and 2020 represent five-year averages (e.g., 2016-2020).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1990 and 2000 5% samples, 2010 and 2020 American Community Survey 5-year samples; U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: All people ages 16 through 24 (16 through 19 for the map breakdown).

Methods: The number and percentage of disconnected youth, among all youth ages 16 through 24 was calculated by race/ethnicity, gender, nativity, and ancestry for each year and geography. For the map breakdown, which provides data down to the census tract level, the universe reflects all people ages 16 through 19 (rather than 16 through 24) due to data limitations. Census tract level data from the 2020 5-year American Community survey was re-estimated into 2010 census tract boundaries using a 2020 to 2010 census tract geographic crosswalk developed using 2020 block level population data from the 2020 Census Redistricting Data along with a block level geographic crosswalk (2020 to 2010 blocks) from NHGIS. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• People in the armed forces during the time of the survey are considered to be employed (i.e., not disconnected). 
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.

Summary: The percentage of students attending public elementary and secondary schools by school poverty level. The year indicated is the latest of a given school year (e.g. 2020 refers to the 2019-2020 school year). School poverty levels are defined by the share of students eligible for free or reduced-price lunch (FRPL) and include: "Low" (less than 25% FRPL), "Mid-low" (25-50% FRPL), "Mid-high" (50-75% FRPL), and "High" (greater than 75% FRPL). It is important to note that the Healthy, Hunger-Free Kids Act of 2010 changed eligibility requirements and this can impact comparability of the school poverty data over time.

Data Source(s): National Center for Education Statistics, Common Core of Data, Public Elementary/Secondary School Universe Survey.

Universe: All students attending public schools.

Methods: The share of students eligible for free or reduced-price lunch (FRPL) was calculated at the school level for all public elementary and secondary schools. Schools were then classified into four groups – school poverty level categories – based on this share (low, mid-low, mid-high, and high), and the number and shares of students by school poverty level category were aggregated to the various Atlas geographies for each racial/ethnic group. For the vast majority of schools, the total student count is consistent with the sum of the counts by race/ethnicity. For a small number of schools, however, it is slightly higher given that the latter excludes any students belonging to an unknown or non-CCD race category. For this reason, data for all racial/ethnic groups combined (the "All" category) reported in the Atlas is based on the sum of student counts by race/ethnicity.

It is important to note that the measure of school poverty used, the share of students eligible for FRPL, is not always reported and is subject to some degree of error at the school level. The reasons for this include the fact that the count of students deemed FRPL-eligible may be taken at a different time than the total student count, and in some states, a single school may administer the free lunch program for a group of schools (in which case its count and share of FRPL-eligible students would be overstated). However, it is likely that any bias caused by these inconsistencies in reporting at the school level are largely mitigated once the data is aggregated across the many schools in a given Atlas geography. It is also important to note that the Healthy, Hunger-Free Kids Act of 2010 changed eligibility requirements and this can impact comparability of the school poverty data over time. In particular, the Act introduced the Community Eligibility Option (CEO), available in 11 states (including the District of Columbia) by the 2013-14 school year and in all states in the 2014-15 school year, which allows more children to be eligible for FRPL. 

Given the prevalence of missing data for some schools and changes to eligibility requirements in recent years, we took precautions to avoid reporting data that are inaccurate or misleading. First, we do not report school poverty information if ten percent or more of the relevant student population attends schools that do not report valid (non-missing) FRPL eligibility data. Second, after making an initial calculation of the overall share of students eligible for FRPL based on available data for the 2009-10 through 2019-20 school years, we examined changes in this measure over time for all 731 Atlas geographies and noted any dramatic year-to-year changes. School poverty data for some Atlas geographies in certain years were set to missing based on this examination. See the methodology page for other relevant notes.

Notes: 

• Disaggregated data for Asian Americans and Pacific Islanders is only available for 2010 and 2020.
• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• Data are not reported if 10 percent or more of the relevant student population attend schools that do not report valid (non-missing) FRPL eligibility data.
• The year indicated is the latest of a given school year (e.g., 2020 refers to the 2019-2020 school year).

Summary: Estimated life expectancy at birth based on abridged life tables constructed from mortality data by race/ethnicity and gender. The "years above average" measure in the ranking and map breakdowns reports the difference in life expectancy between a given racial/ethnic group and the overall population in a given geography, and therefore appears to be zero/missing when the race/ethnicity filter is set to "all." Data for each year represent an average over the previous five years (e.g., 2020 is a 2016-2020 average).

Data Source(s): Centers for Disease Control and Prevention, CDC WONDER, wonder.cdc.gov.

Universe: All people.

Methods: Life expectancy at birth by race/ethnicity for each year and geography was estimated using information on mortality and mid-year population estimates from the WONDER database using an abridged life tables approach. A life table is a table that includes the number of deaths, total population, probability of dying, and remaining life expectancy by single year of age for a given year or time period. Abridged life tables are similar, but present the information for age groups rather than by single year of age. Remaining life expectancy for each age group is largely a function of the probability of dying for people in their own age group and in older age groups. Due to non-disclosure of death counts for some age group cells by race/ethnicity, geography, and year, death rates were at times substituted from higher levels of geographic aggregation. Given this, measures were taken to avoid reporting unreliable estimates – that is, estimates with too many substitutions. Specifically, we only report estimates for which at least 90 percent of the total number of deaths for a population are from age groups that had disclosed death counts in the underlying data and did not require substitution of death probabilities from higher levels of geography. We also only report estimates based on at least 100 total deaths (for all age groups combined). See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• No data is available for the Mixed/other population or for all people of color combined.
• No data is reported unless at least 90 percent of the total number of deaths for a population are from age groups that had disclosed death counts in the underlying data and did not require substitution of death probabilities from higher levels of geography.
• No data is reported unless based on at least 100 total deaths (for all age groups combined).

Summary: Estimated average automation risk associated with a worker’s occupation. Automation risk is a measure that attempts to capture the percentage of tasks associated with an occupation that can be automated, and thus reflects the likelihood of computerization of the underlying tasks for a given occupation.

Data source(s): Lightcast (formerly Burning Glass Technologies); Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, http://www.ipums.org/, 2019 American Community Survey 5-year sample.

Universe: The employed civilian noninstitutionalized population age 16 or older.

Methods: The American Community Survey microdata was filtered to employed civilian noninstitutionalized people age 16 or older. Nationwide data from Lightcast on an index of automation risk, or “automation factor,” along with occupational entry-level education requirements — both expressed at the SOC level — were matched. The number of employed civilian noninstitutionalized people age 16 or older and the estimated automation factor were then aggregated by race/ethnicity, gender, nativity, education requirements, and industry, for all states, regions, and the US overall. The automaton factor was estimated as a weighted average of the SOC-level factors, using the number of employed civilian noninstitutionalized people age 16 or older as weight.

The underlying automation factor is defined for an occupation as the percentage of tasks that can be computerized (i.e., automated). Is it drawn from a 2013 paper, The Future of Employment: How Susceptible Are Jobs to Computerisation, by Carl Benedikt Frey and Michael A. Osborne. The data reported on the number of jobs at risk of automation are calculated based on the assumption that the percentage of tasks within an occupation that can be automated approximates the percentage of jobs that are at risk of automation.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• No data are available for cities or counties.

Summary: Percentage of workers with a job that is future ready. Future-ready jobs are those that provide stable, family-supporting incomes for workers and strong prospects for employers and communities.

Data source(s): Lightcast (formerly Burning Glass Technologies); Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, http://www.ipums.org/, 2019 American Community Survey 5-year sample.

Universe: The employed civilian noninstitutionalized population age 16 or older.

Methods: We define future readiness based on three criteria: (1) stable or growing employment, (2) resilient to automation, and (3) paying a living wage. A job is considered future-ready if it meets all three criteria. A job is considered not future-ready if it lacks one or a combination of the three criteria. 

Whether a job is stable or growing is determined using Lightcast’s job growth data. Automation risk is defined as having a probability of computerization (or “automation factor”) lower than 50 percent, given the full array of tasks that comprise a particular job. The automation factor is based on automation risk associated with each occupation from a 2013 paper, The Future of Employment: How Susceptible Are Jobs to Computerisation, by Carl Benedikt Frey and Michael A. Osborne. Lastly, the living wage criteria is a function of the average wage of an occupation and the cost of living in each geographic area. Jobs are determined to pay a living wage when the average wage is sufficient to sustain a family comprised of two working adults and two children.

The projection of future-ready jobs is extracted from a proprietary dataset from Lightcast based on its job counts data. Lightcast uses the four most recent Quarterly Census of Employment and Wages (QCEW) datasets from the Bureau of Labor Statistics to calculate current employment. Then, job counts of future years are projected based on past trends. Various adjustments are made to the job growth projection using additional data sources including the National Industry-Occupation Employment Matrix (NIOEM) and state-level projection data. See here for a detailed explanation of Lightcast’s projection methodology. Estimated job growth by race/ethnicity and gender assumes that current staffing patterns of detailed occupations remain constant going forward.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin. 
• No data are available for cities or counties.