Summary: The percentage of full-time wage and salary workers ages of different age groups earnings at least $15 per hour (in 2020 dollars). Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while 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, 1980 5% State Sample, 1990 5% Sample, 2000 5% Sample, 2010 and 2020 American Community Survey 5-year samples.

Universe: All civilian noninstitutionalized full-time wage and salary workers of a given age group.

Methods: The share of workers earning at least $15/hour was calculated by race/ethnicity, education, gender, nativity, and ancestry for each year and geography. Before calculating the share of workers earning at least $15/hours, earnings for each year were adjusted for inflation to reflect 2020 dollars (using the CPI-U from the U.S. Bureau of Labor Statistics). 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.
• Data for 1980 through 2000 are based on surveys in those years but reflect income and work efforts from the year prior, while data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.

Summary: The median hourly wage for full-time wage and salary workers of different age groups (in 2020 dollars). Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while 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, 1980 5% State Sample, 1990 5% Sample, 2000 5% Sample, 2010 and 2020 American Community Survey 5-year samples.

Universe: All civilian noninstitutionalized full-time wage and salary workers of a given age group.

Methods: The median hourly wage was calculated by race/ethnicity, education, gender, nativity, and ancestry for each year and geography. Values were then adjusted for inflation to reflect 2020 dollars (using the CPI-U from the U.S. Bureau of Labor Statistics). 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.
• Data for 1980 through 2000 are based on surveys in those years but reflect income and work efforts from the year prior, while data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.

Summary: The percentage of the population living below the indicated federal poverty threshold based on their family income, size, and composition. The federal poverty threshold in 2020 for a family of four with two children was about $26,200 per year (thus, 200% of the federal poverty threshold was about $52,400). Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while 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, http://www.ipums.org/, 2000 5% Sample, 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. IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: All people for whom poverty status is determined (excludes group quarters).

Methods: The percentage of people below the federal poverty level (and 150 and 200 percent of the federal poverty level) was calculated by race/ethnicity, age, gender, nativity, and ancestry for each year and geography. The dollar value of the federal poverty level varies by family size and composition. 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.
• Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• No data are reported if based on fewer than 100 individuals (i.e., unweighted) survey respondents in the universe for the by race/ethnicity/age, composition by race/ethnicity, trend, by gender, by nativity, by ancestry, and ranking breakdowns.
• 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 percentage of all workers ages 25-64 who are "working poor," defined as both (1) working full-time and (2) having a family income below the indicated federal poverty threshold based on family size and composition. For instance, the federal poverty threshold in 2022 for a family of four with two children was about $29,700 (thus, 200% of the federal poverty threshold was $59,400).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1980 5% State Sample, 1990 5% Sample, 2000 5% Sample, 2010 and 2022 American Community Survey 5-year samples.

Universe: The civilian noninstitutionalized population ages 25-64 not living in group quarters who worked at all during the year prior to the survey.

Methods: The percent working poor was calculated by race/ethnicity, gender, nativity, and ancestry for each year and geography. The dollar value of the federal poverty level varies by family size and composition. Calculations were made using three different definitions of "poor" based on ratios of family income to the federal poverty level (below 100, 150, and 200 percent). 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.
• Data for 1980 through 2000 are based on surveys in those years but reflect income and work efforts from the year prior, while data for 2010 and 2022 represent 2006-2010 and 2018-2022 averages, respectively.

Summary: The unemployment rate for the working-age population (25-64). The map breakdown shows the unemployment rate for the population age 16 or older. Data for 2010 and 2022 represent five-year averages (e.g., 2018-2022).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1980, 1990, 2000 5% samples, 2010 and 2022 American Community Survey 5-year samples.

Universe: Civilian noninstitutionalized population ages 25 to 64.

Methods: The unemployment rate was calculated by race/ethnicity, education, gender, nativity, and ancestry for each year and geography. The unemployment rate is the number of people who are out of work divided by the number who are in the labor force, defined as working or actively seeking employment (over the last four weeks). 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.
• Data from 2010 and 2022 represent 2006-2010 and 2018-2022  averages, respectively.
• No data are reported if based on fewer than 100 individual (i.e., unweighted) survey respondents.

Summary: Average annual earned income for full-time wage and salary workers ages 25-64, and real (inflation-adjusted) earned income growth over time, by percentile. Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while data for 2010 and 2022 represent five-year averages (e.g., 2018-2022).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1980 5% State Sample, 1990 5% Sample, 2000 5% Sample, 2010 and 2022 American Community Survey 5-year samples.

Universe: Civilian noninstitutionalized full-time wage and salary workers ages 25-64.

Methods: Average annual earned income percentiles were estimated for full-time wage and salary workers ages 25 through 64 in each year and geography. Values were then adjusted for inflation to reflect 2022 dollars (using the CPI-U from the US Bureau of Labor Statistics) before growth rates over time were calculated. Income percentiles are the point in the income distribution below which a given percent of workers fall. For example, if the 20th percentile income value is $23,000, that means that 20 percent of workers earn less than that amount. See the methodology page for other relevant notes.

Notes:

• Data for 1980 through 2000 are based on surveys in those years but reflect income and work efforts from the year prior, while data for 2010 and 2022 represent 2006-2010 and 2018-2022 averages, respectively.

Summary: Annual household income at the 95th and 20th percentiles (in 2022 dollars), and the ratio of the 95th to the 20th percentile (the 95/20 ratio). A household income percentile is a level of income below which a given percentage of households fall. For example, 95 percent of households earn below the 95th percentile and 20 percent of households earn below the 20th percentile. The 95/20 ratio is a useful measure of income inequality, with a higher ratio indicating greater inequality. Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while data for 2010 and 2022 represent five-year averages (e.g., 2018-2022).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1980 5% State Sample, 1990 5% Sample, 2000 5% Sample, 2010 and 2022 American Community Survey 5-year samples.

Universe: All households.

Methods: Household income at the 95th and 20th percentiles were estimated for all households in each year and geography, and the ratio of the 95th to the 20th percentile was calculated. See the methodology page for other relevant notes.

Notes:

• Data for 1980 through 2000 are based on surveys in those years but reflect income from the year prior, while data for 2010 and 2022 represent 2006-2010 and 2018-2022 averages, respectively.

Summary: The percentage of households that are owner-occupied. 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 5% Sample, 2000 5% Sample, 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 households.

Methods: The rate of homeownership was calculated by race/ethnicity, gender, nativity, ancestry, and poverty status 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.
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively. 
• No data are reported if based on fewer than 100 individual (i.e., unweighted) survey respondents in the universe for the by race/ethnicity, trend, by gender, by nativity, by ancestry, and ranking breakdowns.
• 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 number of firms per 100 persons in the labor force ages 16 or older and growth in the number of firms. Firms are classified by race/ethnicity and gender based on the self-identification of the majority owner. With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.

Data Source(s): U.S. Census Bureau, 2007 and 2012 Survey of Business Owners, 2017, 2018, 2019, and 2020 Annual Business Survey, 2017 and 2018 Nonemployer Statistics by Demographics series, 2009, 2014, 2017, 2018, 2019, and 2020 American Community Survey 5-year Summary Files.

Universe: Firms include all nonfarm businesses filing Internal Revenue Service tax forms as individual proprietorships, partnerships, or any type of corporation, and with receipts of $1,000 or more.

Methods: Data on the number of firms with paid employees, industry, and race/ethnicity and gender of the proprietor was collected from the 2007 and 2012 Survey of Business Owners (SBO), the 2017, 2018, 2019, and 2020 Annual Business Survey (ABS), and the 2017 and 2018 Nonemployer Statistics by Demographics series (NES-D) for all Atlas geographies. To be consistent across breakdowns and cuts by race/ethnicity and gender, firm counts for all breakdowns were restricted to firms classifiable by race, gender, and veteran status. A single firm may be tabulated in more than one racial/ethnic group category. This can result because the sole owner was reported to be of more than one race, the majority owner was reported to be of more than one race, or a majority combination of owners was reported to be of more than one race. The denominator used to calculate the number of firms per 100 persons in the labor force age 16 or older by race/ethnicity and gender was merged in from the 2009 American Community Survey (ACS) 5-year summary file for the 2007 SBO data and the 2014 ACS 5-year summary file for the 2012 SBO data. These years of the ACS summary file were chosen because the central year of each five-year pool aligns with the year of the SBO data (e.g., the central year of the 2014 5-year ACS, which covers years 2010-2014 is 2012).

Beginning in 2017, the SBO was discontinued and replaced with the ABS (for data on firms with paid employees) and the NES-D (for data on nonemployer businesses). One advantage of the shift to the ABS and NES-D is that the data are released annually and are thus more current. One major disadvantage, however, is that the ABS is based on a smaller sample of firms, particularly in years that do not align with the Economic Census (those ending with a two or a seven), and does not report data for many smaller geographies and more detailed groups defined by race/ethnicity and gender. While the approach behind the NES-D is innovative in that it draws on a wealth of individual-level information from administrative records along with Census data to assign demographic characteristics, it still provides far less detailed demographic information than was available in the SBO and less detail in terms of geography as well.

For example, while the SBO reports data for over 20 racial/ethnic groups for the nation, states, CBSAs, counties, and places, the 2017 ABS only reports such detailed data at the national and state levels with only seven racial/ethnic groups reported at lower levels of geography. The 2018, 2019, and 2020 ABS (the most recent data available at the time of the last update of the business ownership indicator) – and presumably all subsequent years of the ABS until the next Economic Census in 2022 – are based on an even smaller sample and only report data for seven racial/ethnic groups at all geographic levels and only report any data down to the metropolitan area level. There are similar limitations with the NES-D, which was only available for 2017 and 2018 at the time of the last update of the business ownership indicator.

And while the timelier release schedule for the ABS and NES-D is a good thing, it did lead us to draw data for the denominator (the number of people in the labor force age 16 or older) from a relatively older vintage of the ACS summary file for 2017 (and later years) compared with earlier years of the indicator; we shifted to combining the ABS data with ACS 5-year summary file data from the corresponding year (e.g., 2017 ABS with the 2017 ACS 5-year summary file). This shift ensures that the ACS data needed for the denominator will be available at the time the new ABS data are released with the downside being that the central year of the ACS sample is two years older than the ABS data. See the methodology page for other relevant notes.

Notes: 

• With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.

• Estimates for small geographies and/or demographic groups are often not reported because the data does not meet ABS/SBO/NES-D publication standards.

• No data is available for the mixed/other racial group since it is not identified in the ABS and NES-D data.

• No data on the number of firms per 100 workers (i.e., persons in the labor force age 16 or older) are reported if the calculated rate came out to more than 100 or if there are fewer than 1,000 workers in the denominator.

• Total firm counts for all breakdowns are restricted to firms classifiable by race, gender, and veteran status.

• No data is available for cities or counties in 2018, 2019, and 2020. No data is available for nonemployer businesses in 2019 and 2020, nor for cities or counties in 2017.

Summary: The average annual receipts per firm (in 2018 dollars). Firms are classified by race/ethnicity and gender based on the self-identification of the majority owner. With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity. 

Data Source(s): U.S. Census Bureau, 2007 and 2012 Survey of Business Owners, 2017 and 2018 Annual Business Survey, 2017 and 2018 Nonemployer Statistics by Demographics series.

Universe: Firms include all nonfarm businesses filing Internal Revenue Service tax forms as individual proprietorships, partnerships, or any type of corporation, and with receipts of $1,000 or more.

Methods: Data on aggregate revenues and the number of firms by firm type (firms with paid employees and nonemployer firms), industry, and race/ethnicity and gender of the proprietor was collected from the 2007 and 2012 Survey of Business Owners (SBO), the 2017 and 2018 Annual Business Survey (ABS) and the 2017 and 2018 Nonemployer Statistics by Demographics series (NES-D) for all Atlas geographies. Average annual revenues per firm was calculated by dividing aggregate revenues by the number of firms, and values for 2007 and 2012 were adjusted for inflation to reflect 2018 dollars (using the CPI-U from the U.S. Bureau of Labor Statistics). To be consistent across breakdowns and cuts by race/ethnicity and gender, revenues and firm counts for all breakdowns were restricted to firms classifiable by race, gender, and veteran status. A single firm may be tabulated in more than one racial/ethnic group category. This can result because the sole owner was reported to be of more than one race, the majority owner was reported to be of more than one race, or a majority combination of owners was reported to be of more than one race.

Beginning in 2017, the SBO was discontinued and replaced with the ABS (for data on firms with paid employees) and the NES-D (for data on nonemployer firms). One advantage of the shift to the ABS and NES-D is that the data are released annually and are thus more current. One major disadvantage, however, is that the ABS is based on a smaller sample of firms, particularly in years that do not align with the Economic Census (those ending with a two or a seven), and does not report data for many smaller geographies and more detailed groups defined by race/ethnicity and gender. While the approach behind the NES-D is innovative in that it draws on a wealth of individual-level information from administrative records along with Census data to assign demographic characteristics, it still provides far less detailed demographic information than was available in the SBO and less detail in terms of geography as well.

For example, while the SBO reports data for over 20 racial/ethnic groups for the nation, states, CBSAs, counties, and places, the 2017 ABS only reports such detailed data at the national and state levels with only seven racial/ethnic groups reported at lower levels of geography. The 2018, 2019, and 2020 ABS (the most recent data available at the time of the last update of the business revenue indicator) – and presumably all subsequent years of the ABS until the next Economic Census in 2022 – are based on an even smaller sample and only report data for seven racial/ethnic groups at all geographic levels and only report any data down to the metropolitan area level. Moreover, for years 2019 and later, revenue estimates expressed in discrete dollar values are only provided at the national level; for all other geographies (state level and lower), only broad revenue ranges are reported. There are similar limitations with the NES-D, which was only available for 2018 at the time of the last update of the business revenue indicator. For this reason, we currently only provide data through 2018 for the indicator. See the methodology page for other relevant notes.

Notes: 

• With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.
• Estimates for small geographies and/or demographic groups are often not reported because the data does not meet ABS/SBO/NES-D publication standards.
• No data is available for the mixed/other racial group since it is not identified in the ABS and NES-D data.
• Revenues per firm for all breakdowns are restricted to firms classifiable by race, gender, and veteran status.
• No data is available for 2019 and 2020 due to lack of sufficient information on firm revenues in the ABS and no data available for the NES-D at the time of the last update.

Summary: The net percentage change in jobs and earnings per worker by wage level category. Industries were grouped into three categories (low, middle, and high) by average annual earnings per worker in 1990, and measures of growth in jobs and earnings per worker were calculated for each category over time. Earnings growth is adjusted for inflation.

Data Source(s): U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW); Woods & Poole Economics, Inc., 2024 Complete Economic and Demographic Data Source.

Universe: Private-sector jobs covered by state unemployment insurance laws (about 95 percent of all U.S. private-sector jobs). 

Methods: Using 1990 as the base year, broad private-sector industries (at the two-digit NAICS level) were classified into three wage categories: low-, medium-, and high-wage industries. An industry’s wage category was based on its average annual wage, and each of the three categories contained approximately one-third of all private two-digit NAICS industries in each Atlas geography. The 1990 industry wage-category classification was applied across all the years in the dataset, so that the industries within each category remained the same over time. The percentage change in the number of jobs and in average earnings per worker were then calculated. Earnings values were adjusted for inflation to 2022 dollars (using the CPI-U from the U.S. Bureau of Labor Statistics) prior to calculating earnings growth. See the methodology page for other relevant notes.

Notes:

• Earnings growth is in real terms (adjusted for inflation).
• No data is available for cities.

Summary: Compound annual growth rate of jobs and gross domestic product (GDP) over the indicated period. GDP measures the dollar value of all goods and services produced in the region, and its growth rate is adjusted for inflation.

Data Source(s): US Bureau of Economic Analysis, Gross Domestic Product by State, Gross Domestic Product by Metropolitan Area, CA30: regional economic profile.

Universe: All public- and private-sector jobs.

Methods: Compound annual growth rates in the number of jobs and GDP was calculated over four time periods, 1990-2007, 2009-2019, 2019-2020, and 2021-2022. These periods were selected to roughly capture economic growth before and after the Great Recession, the short-term impact of COVID-19, and the recovery since. GDP values were adjusted for inflation (using the CPI-U from the US Bureau of Labor Statistics) before growth rates over time were calculated. See the methodology page for other relevant notes.

Notes:

• GDP growth is in real terms (adjusted for inflation).
• No data is available for cities.
   

Summary: The labor force participation rate, employment-to-population ratio, joblessness rate, and unemployment rate for the working age population (ages 25-64). Data for 2010 and 2022 represent five-year averages (e.g., 2018-2022).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% Sample, 2010 and 2022 American Community Survey 5-year samples.

Universe: Civilian noninstitutionalized population ages 25 to 64.

Methods: The labor force participation rate, employment-to-population ratio, joblessness rate, and unemployment rate were calculated by race, nativity, ancestry, and gender for each year and geography. The labor force includes those who are employed or unemployed, and the labor force participation rate is their share of the civilian noninstitutionalized population. The employment-to-population ratio is the employed divided by the civilian noninstitutionalized population. The unemployed includes those not working but actively seeking work, and the unemployment rate is their share of the civilian noninstitutionalized labor force. The joblessness rate is the unemployed divided by the civilian noninstitutionalized population. 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.
• Data from 2010 and 2022 represent 2006-2010 and 2018-2022 averages, respectively.

Summary: Estimated job growth over 10 years (2019-2029) by detailed occupation, occupational group, and future readiness. Estimated job growth by race/ethnicity and gender assumes that current staffing patterns remain constant in the future. 

Data source(s): Lightcast (formerly Burning Glass Technologies).

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

Methods: Jobs (or detailed occupations) are defined based on the six-character IPUMS USA variable OCCSOC. Occupational groups are defined based on the first two digits of the OCCSOC variable.  
The job growth data is 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, and are thus determined by the modeled growth rates of detailed occupations within each geographic area. In other words, while the growth rate of a detailed occupation within a geographic area is the same for all demographic groups, the number of jobs added differs and is proportional to each group’s share of workers in that occupation in the base year (2019), and the growth rates of occupational groups will vary by demographic group based on a different mix of detailed occupations held by a given demographic group within each occupational group. While in an equitable economy, we would not expect much or any occupational segregation by race/ethnicity or gender, the data presented for the job growth indicator shows what we can expect if current inequities in the labor market persist into the future.

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 of two working adults and two children based on data from the MIT Living Wage Calculator accessed in early 2022.

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.