Summary: The composition of the population by race/ethnicity. Population projections are available for counties and regions only.

Data Source(s): U.S. Census Bureau 2010 and 2020 Census Summary File 1, 2010 and 2020 American Community Survey 5-year Summary File; California Department of Finance, Population Projections (Baseline 2020), P-3: State and County Projections Dataset; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries.

Universe: All people.

Methods: The total population and percentage population by race/ethnicity was calculated for each year and geography. County-level population projections are from the California Department of Finance. The demographic projections from the California Department of Finance follow the Office of Management and Budget 1997 guidelines on racial classification and essentially distribute the non-Hispanic other single race alone group across the other defined racial/ethnic categories. This is different from the classification used in the Decennial Census and American Community Survey, which reports data for a non-Hispanic other single race alone group. This causes a slight discrepancy between the racial classification scheme used in non-projection years (before 2030) and in projection years (2030 and later). However, in the Bay Area, the non-Hispanic other single race alone group is very small, comprising only about 0.3 percent of the overall population, so the discrepancy is largely inconsequential. 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.
• For projection years (2030 and later), the mixed/other category excludes people identifying as other race alone; that population is distributed among the other racial/ethnic groups.
• Projected data are not available sub-counties, large cities, other cities or towns, or Census Designated Places.
• No data are reported for geographies with an overall population of less than 500 in a given year, census tracts with an overall population of less than 100 are not reported in a given year.

Summary: The composition of the population by race/ethnicity, nativity, and ancestry. 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, 2000 5% sample, 2010 and 2020 American Community Survey 5-year samples.

Universe: All people. 

Methods: The total population and percentage population by race/ethnicity, nativity and ancestry was calculated for each year and geography from microdata samples using appropriate survey weights. 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 in the by ancestry breakdown if based on fewer than 50 individual (i.e., unweighted) survey respondents.
• No data are reported in breakdowns other than by ancestry if based on fewer than 100 individual (i.e., unweighted) survey respondents.
• No data are available for other cities or towns and Census Designated Places.

Summary: The share of high school graduates meeting course requirements (with a grade of C or higher) for entry into the UC/CSU system. Years reflect the latest year of each school year (e.g. 2020 reflects the 2019-2020 school year).

Data Source(s): California Department of Education, Adjusted Cohort Graduation Rate and Outcome Data (2018-2020), One-Year Graduate Counts (2018-2020), and Graduates by Race and Gender (2011-2017).

Universe: All high school graduates of public and charter high schools that were open during each year.

Methods: The number and percentage of high school graduates meeting course requirements (with grade of C or better) for entry into the UC/CSU system was calculated by race/ethnicity and by gender for each year and geography from school-level data. Schools were geocoded to determine their geographic location and assign them to each Atlas geography.

The Adjusted Cohort Graduation Rate and Outcome Data (ACGR), which is the source of college readiness data for the 2018-2020 school years, suppresses data for schools and demographic groups for which there are fewer than 11 students in the cohort. Due to this data suppression, we restricted the universe to only include schools with valid college readiness data available (i.e., those with at least 11 students in the cohort). To ensure adequate data coverage across each race/ethnicity, geography, and year breakdown, we then incorporated uncensored one-year graduation counts for the 2018-2020 school years and report data only for breakdowns in which college readiness data are present for at least 80 percent of graduates within each demographic group. 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 the mixed/other racial/ethnic group only includes data for students identifying as mixed race (and not a single other race alone, not covered by the categories delineated by the survey).
• Years reflect the latest year of each school year (e.g. 2020 reflects the 2019-2020 school year).
• No data are reported if based on fewer than 30 high school graduates.
• No data are reported for Census Designated Places due to small numbers of schools and high school graduates.

Summary: The educational attainment levels of the population age 25 or older. Except for white people, all racial groups include people of Hispanic origin who self-identify with that racial identity. Data for 2010 and 2020 represent five-year averages (e.g. 2016-2020).

Data Source(s): U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries.

Universe: All people age 25 or older.

Methods: The number and percentage of people age 25 and older by level of educational attainment was calculated by race/ethnicity and gender for each year and geography. See the methodology page for other relevant notes.

Notes:

• Except for white people, all racial groups include people of Hispanic origin who self-identify with that racial identity.
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• 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.
• No data are reported if based on fewer than 500 people age 25 or older; a lower minimum threshold for reporting of at least or 100 people age 25 or older was applied for census tract level estimates.

Summary: The share of the population ages 16 to 24 who are not working or enrolled in school. The trend, ranking, and map breakdowns show 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): U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% sample, 2010 and 2020 American Community Survey 5-year samples.

Universe: All people ages 16 through 19 for the trend, ranking, and map breakdowns; all people ages 16 through 24 for all other breakdowns.

Methods: The number and percentage of disconnected youth, among all youth ages 16 through 19 (or 16 through 24) was calculated by race/ethnicity, gender, nativity and ancestry for each year and geography. 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.
• People in the armed forces during the time of the survey are considered to be employed (i.e. not disconnected).
• No data is available for other cities or towns or Census Designated Places for the by race/ethnicity, by gender, by nativity and by ancestry breakdowns, as they are based on the IPUMS microdata.
• No data are reported if based on fewer than 100 individual (i.e. unweighted) survey respondents ages 16 through 24 for the by race/ethnicity, by gender, by nativity and by ancestry breakdowns.
• No data are reported if based on fewer than 500 people ages 16 through 19 for the trend, ranking and map breakdowns; a lower minimum threshold for reporting of at least or 100 people ages 16 through 19 was applied for census tract level estimates in the map breakdown.

Summary: The labor force participation rate, employment-to-population ratio, joblessness rate, and unemployment rate for the working age population (ages 25-64). The trend, ranking, and map breakdowns show 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): U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% sample, 2010 and 2020 American Community Survey 5-year samples.

Universe: Civilian noninstitutionalized population age 16 or older for the trend, ranking, and map breakdowns; 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.

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 available for other cities or towns or Census Designated Places for the by race/ethnicity, by gender, by nativity, and by ancestry breakdowns, as they are based on the IPUMS microdata.
• No data are reported if based on fewer than 100 individual (i.e. unweighted) survey respondents in the universe for the by race/ethnicity, by gender, by nativity and by ancestry breakdowns.
• No data are reported if based on fewer than 500 people in the universe for the trend, ranking and map breakdowns; 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 median annual earnings (in 2020 dollars) for full-time workers ages 16 years and over with earnings. With the exception of white people, all racial groups include people of Hispanic origin who self-identify with that racial identity. Data for 2000 reflect income from 1999 while data for 2010 and 2020 represent five-year averages (e.g., 2016-2020).

Data source(s): U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries.

Universe: All full-time workers age 16 years and over with earnings during the year prior to the survey.

Methods: Median annual earnings for full-time workers age 16 years and over with earnings during the year prior to the survey was calculated by race/ethnicity and gender for each year and geography. Medians at the sub-county and regional levels were derived by aggregating up from the census tract and county levels, respectively. They were specifically estimated by applying the Pareto interpolation technique to aggregated categorical data on the number of full-time workers by earnings band. All median values 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:

• With the exception of white people, all racial groups include people of Hispanic origin who self-identify with that racial identity.
• Data for 2000 reflect income from 1999 while data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• Full-time workers are defined as people who reported working at least 50 weeks and usually worked at least 35 hours per week during the year prior to the survey.
• Earnings are defined as all pre-tax wage and salary income received by employees.
• Median earnings estimates for all full-time workers (i.e., not by race/ethnicity or gender) in 2010, for all full-time workers by race/ethnicity overall (i.e., not by gender) in 2010 and 2020, and for Asian American, Pacific Islander and mixed/other workers (overall and by gender) in all years are not available in the source datasets. These values were estimated by applying the Pareto interpolation technique to categorical data on the number of full-time workers by earnings band (which is available). For geographies in which the median full-time worker fell in the top earnings bracket, a Pareto estimate of their earnings could not be derived. In such cases, it was instead estimated by taking a weighted average of the two medians by for males and females (or four medians in the case of mixed/other workers), using the number of full-time workers as weight.
• No data are reported if based on fewer than 500 full-time workers age 16 years and over with earnings during the year prior to the survey; a lower minimum threshold for reporting of at least or 100 full-time workers was applied for census tract level estimates.
• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• No data are available for other cities or towns or Census Designated Places for the by race/ethnicity, by gender, by nativity, and by ancestry breakdowns, as they are based on the IPUMS microdata.
• No data are reported if based on fewer than 100 individual (i.e. unweighted) survey respondents in the universe for the by race/ethnicity, by gender, by nativity and by ancestry breakdowns.
• No data are reported if based on fewer than 500 people in the universe for the trend, ranking and map breakdowns; a lower minimum threshold for reporting of at least or 100 people was applied for census tract level estimates in the map breakdown.

Summary: Average annual earned income for full-time wage and salary workers ages 25 to 64, and real (inflation-adjusted) earned income growth over time, by percentile. Data for 2000 reflect income from 1999 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, 2000 5% sample, 2010 and 2020 American Community Survey 5-year samples.

Universe: Full-time wage and salary workers ages 25 through 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. See the methodology page for other relevant notes.

Notes:

• Data for 2000 reflect income from 1999 while data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• Full-time workers are defined as all people who reported working at least 50 weeks and usually worked at least 35 hours per week during the year prior to the survey for all years 2010 and later. For 2000, the weeks worked threshold is 45 weeks instead of 50 weeks. The less restrictive threshold was applied in 2000 due to a change in the “weeks worked” question in the 2008 American Community Survey (ACS), which caused a dramatic rise in the share of respondents indicating that they worked at least 50 weeks during the year prior to the survey, as compared with prior years of the ACS and the long form of the decennial census. The less restrictive definition was applied in 2000 to make our data on full-time workers more comparable over time. Our analysis found that the 45-week cutoff produced a national trend in the incidence of full-time work over the 2005-2010 period that was most consistent with that found using data from the March Supplement of the Current Population Survey, which did not experience a change to the relevant survey questions. For more information on this issue, see here.
• Wage and salary workers include all people who report working during the year prior to the survey and report receiving wage and salary income but no self-employment income (e.g. income from a business, professional practice, or farm).
• The term earned income refers to all pre-tax wage and salary income received by employees.
• No data is available for other cities or towns or Census Designated Places as this indicator in entirely based on the IPUMS microdata.

Summary: Share of workers earning enough to meet their basic needs.

Data Sources: Center for Women's Welfare, University of Washington, Self-Sufficiency Standard for California (2021), selfsufficiencystandard.org/California; Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, ipums.org, 2014 and 2020 American Community Survey 5-year samples. 

Universe: Civilian noninstitutionalized full-time wage and salary workers ages 25-64. Latinx includes people of Hispanic origin of any race; all other groups exclude people of Hispanic origin.

Methods: An individual earning enough to meet their basic needs is defined as earning at least half of the Self-Sufficiency Standard for a household of two adults, one school-age child, and one preschool-age child in the county where they reside. Data represent a 2016-2020 average with Self-Sufficiency Standard values from 2021 adjusted for inflation to align with 2020 dollar values in which worker earnings are measured.

Summary: The number and rate (per 100,000 people) of civilians involved in incidents with police in which they sustained serious bodily injury or death, or in which police discharged a firearm at them. Data for 2016-2022 reflect the combined seven-year period while individual year selections only reflect data for that year.

Data Source(s): California Department of Justice, Use of Force Incident Reporting; U.S. Census Bureau, 2020 American Community Survey 5-year Summary File; U.S. Census Bureau, 2017, 2018, 2019, and 2020 American Community Survey 1-year Summary Files; U.S. Census Bureau.

Methods: The Use of Force Incident Reporting data include information on civilians and officers involved in use-of-force incidents. Data is only reported for incidents which result in serious bodily injury or death to anyone involved, or in which a firearm was discharged. Civilian-level records from incidents with police that resulted in serious bodily to the civilian, or which an officer discharged a firearm at them were selected for the years 2016 to 2022 and summed together. Records in which the civilian was not reported to have received force or in which no officer involved was reported to have used force on a civilian were excluded, as were records missing geographic or demographic information. The remaining records were aggregated by race/ethnicity, gender and age of the civilian involved, as well as by consequence of force on the civilian, for each geography. Consequence of force categories reflect the most severe consequence for the civilian in cases where there were multiple consequences, with death being the most severe consequence followed by serious bodily injury (whether a firearm was discharged or not). The third category - gun fired, no or non-serious injury - included cases in which an officer fired a gun at the civilian but it did not result in serious injury or death. For more information on how the data is collected and the what how serious injuries are defined, see here. It is important to note that due to the narrow definition of use-of-force incidents in the underlying dataset, and because 2016 was the first year of data collection and not all agencies reported (despite efforts to provide access to all law enforcement agencies in the state), it does not represent the totality of use of force incidents in California (or the Bay Area). Geographic aggregation was based on the city and county in which the incident took place. Data was aggregated for sub-county (CPUMA) geography using a city-to-CPUMA crosswalk that was created by assigning each city to the CPUMA containing the plurality of its 2010 population (from SF1 of the 2010 Census) by census block. For years 2016-2022, 2021, 2022, data on population by race/ethnicity was merged in from 2016-2020 ACS population data to derive a measure of the number of incidents per 100,000 people. For all the other years (2016, 2017, 2018, 2019, 2020), population data comes from the corresponding ACS year population data. It is important to note that the number of incidents per 100,000 people reflect the total number over the seven-year period of 2016-2022 and are not annual rates unless filtered for individual years. Individual year selections reflect data for that year only. 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.
• Use of force incidents per 100,000 people uses the sum of 2016 to 2022 civilians involved in use-of-force incidents except for individual year selections which only reflect data for that year, and averaged population data for years 2016 through 2022.
• No data is available for California as a whole, sub-counties, or Census Designated Places.