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; 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 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). 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 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; 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 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). 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 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, www.ipums.org, 1980 5% State Sample, 1990 5% Sample, 2000 5% Sample, 2010 and 2020 American Community Survey 5-year samples; 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 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. The federal poverty threshold in 2017 for a family of four with two children was about $25,000 per year (thus, 200% of the federal poverty threshold was about $50,000).

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. IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: The civilian noninstitutional 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). 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 and work efforts from the year prior, while data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.

Summary: The unemployment rate for the working-age population (25-64). 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). 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: The civilian noninstitutional population ages 25-64.

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 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: 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 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. IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: Civilian noninstitutional 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 2020 dollars (using the CPI-U from the U.S. 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. 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. 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 2020 represent 2006-2010 and 2016-2020 averages, respectively.
   

Summary: Annual household income at the 95th and 20th percentiles (in 2020 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 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. IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

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. 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:

• 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 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 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. 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, 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.
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively. 
   

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 Non-Employer 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 sole proprietorships). 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 7 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 reports data 7 racial/ethnic groups at all geographic levels and only reports 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.

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 Non-Employer 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 sole proprietorships), 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 sole proprietorships). 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 7 racial/ethnic groups reported at lower levels of geography. The 2018 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 – is based on an even smaller sample and only reports data 7 racial/ethnic groups at all geographic levels and only reports any data down to the metropolitan area level. Moreover, it only provides discrete dollar value estimates of average annual revenue per firm at the national level (only broad revenue ranges are reported at the state level and lower). 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. 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.
• Data for Asian or Pacific Islanders reflects only the Asian population (i.e. it excludes Pacific Islanders).
• 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.
• No data on revenue growth are reported if there are fewer than 30 firms in any year (2007, 2012, 2017, 2018).
• 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 2019 and 2020 ABS.

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., 2022 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 2021 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): U.S. 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 three time periods, 1990-2007, 2009-2019, and 2019-2020. These periods were selected to roughly capture economic growth before and after the Great Recession and the impact of COVID-19. GDP values were adjusted for inflation to reflect 2020 dollars (using the CPI-U from the U.S. 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). The map breakdown shows the same measures, but for the population age 16 or older. 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.

Universe: Civilian noninstitutionalized population age 16 or older for the map breakdown; civilian noninstitutionalized population ages 25 to 64 for all other breakdowns.

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. 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 from 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 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.