Summary: Race/ethnicity and gender composition of elected officials holding the municipal offices of mayor or councilmember, or county offices of supervisor or district attorney, compared with the general population. Data for counties includes only county offices, while data for all other geographies includes both county and municipal offices. Pacific Islander elected officials’ data only available starting in 2023, no elected officials prior to this year.

Data Source(s): GovBuddy.com; PolicyLink and USC ERI data collection; U.S. Census Bureau, 2017, 2018, 2020 American Community Survey 5-year Summary Files.

Universe: The universe of the racial/ethnic and gender composition of elected officials includes city and county elected officials holding municipal offices of mayor or councilmember (for all Atlas geographies except counties), and county offices of supervisor or district attorney. The universe of the racial/ethnic and gender composition of the total population includes all people.

Methods: An initial list of current elected officials in California was obtained in 2018 from GovBuddy.com. It reflected elected officials that were in office during May 2018, the month during which data was collected by GovBuddy.com. The list was reduced to only municipalities and counties within the nine-county Bay Area region, and to only municipal offices of mayor or councilmember and county offices of supervisor or district attorney. Municipal offices vice mayors and mayor pro-tems were also included as they are considered councilmembers as well. Using the May 2018 list of elected officials provided by GovBuddy, we identified the race and gender of the elected officials via web-based research. We then sent the information to the elected officials via email and mail, providing them with multiple opportunities to correct the data. We then updated the dataset using a July 2019 GovBuddy list following the same approach. Subsequent updates were made by identifying all changes to our list of elected officials after each election, identifying the race and gender of new electeds via web-based research, and reaching out to them by mail and e-mail to provide an opportunity to make any corrections. Data on the racial/ethnic and gender composition of the total population in each Atlas geography was merged in from the 2017, 2018, and 2020 American Community Survey 5-year Summary File, which reflects a 2013-2017, 2014-2018, and 2016-2020 averages, respectively (see below). Data for the ranking and map breakdowns show the percentage point difference between the share of elected officials that identify with a particular race/ethnicity and the share of the total population that identify with that race/ethnicity in each Atlas geography, calculated by subtracting the share of the total population from the share of elected officials. Thus, for example, if 10 percent of the elected officials attributed to a city were Black but 30 percent the city's total population was Black, the percentage point difference shown for the Black population would be -20. Both municipal and county elected officials are attributed to the Atlas geographies of large cities and other cities or towns because both levels of elected officials are accountable to their residents; for Census Designated Places, which are not municipalities and thus do not have mayors or councilmembers, only county elected officials are attributed. For counties, only county elected officials are attributed because the elected officials in all the municipalities within each county are not accountable to the entire county. For the five- and nine-county Bay Area regions, both county and municipal offices are attributed because the regional boundaries are apolitical and including all elected officials for which data was collected gives a broad sense of the degree to which there is equal representation by race/ethnicity and gender for the region overall. 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 counties includes only county offices, while data for all other geographies includes both county and municipal offices.
• Data for 2019 reflects electeds holding office as of May 2019, with the race/ethnicity and gender composition of the overall population from the 2017 5-year ACS summary file (reflecting a 2013-2017 average).
• Data for 2021 reflects electeds holding office as of January 2021, with the race/ethnicity and gender composition of the overall population from the 2018 5-year ACS summary file (reflecting a 2014-2018 average).
• Data for 2023 reflects electeds holding office as of February 2023, with the race/ethnicity and gender composition of the overall population from the 2020 5-year ACS summary file (reflecting a 2016-2020 average).
• No data are available for the state of California overall and sub-counties.
• Data for 2018 and 2020 not included in this indicator. 

Summary: Potential voters (citizens age 18+), people registered to vote, and voting in the presidential and midterm elections of 2012, 2014, 2016, 2018, 2020, and 2022. Racial/ethnic detail is only available for Latinos and Asian Americans, imputed based on surnames.
Data source(s): Statewide Database (SWDB); US Census Bureau, Citizen Voting Age Population (CVAP) Special Tabulation from the 2012, 2014, 2016, 2018, 2019, and 2021 American Community Survey 5-Year Summary Files.

Universe: Varies depending on breakdown. All breakdowns are restricted to US citizens age 18 or older.

Methods: Statement of Vote (SOV) data files were downloaded from the SWDB for the 2012, 2014, 2016, 2018, 2020, and 2022 general elections at the SR precinct level. The SR precinct is a consolidated precinct geographic unit constructed for statistical merging purposes by SWDB. Geographic crosswalks between SR precincts and census blocks for each election were also downloaded from the SWDB, and were used to crosswalk the registration and voting data from the SOV files to the 2010 or 2020 census block geographies depending on the election year. Data from 2010 to 2020 were provided in 2010 census block boundaries while 2022 data was structured by 2020 boundaries. The registration and voting data were then aggregated up to the  census block group level and data on the size of the Citizen Voting Age Population (CVAP), or US citizens age 18 or older, was merged in. CVAP data are available as special tabulations of the 2012, 2014, 2016, 2018, 2019, and 2021 American Community Survey (ACS) 5-Year Summary Files. Registration, voting, and CVAP data was then aggregated up from the census block group geography to each Atlas geography. 

To aggregate the data to the census-defined place (large cities, smaller cities or towns and Census Designated Places) and sub-county (CPUMA) geographies, geographic crosswalks were used that assigned each block group to the census-defined place or CPUMA that contained its internal point, which is a point defined by a particular latitude and longitude that approximates the geographic center of the geography (in this case, the block group). Total registration counts in each year were divided by total CVAP in the corresponding year of the ACS summary file (e.g. total registration in 2012 was divided by total CVAP from the 2012 5-year ACS) for each Atlas geography to derive a measure of registration as a share of CVAP; total votes were divided by total registration in each year to derive a measure of total votes as a share of those registered, and total votes were also divided by total CVAP in the corresponding year of the ACS summary file to derive a measure of total votes as a share of CVAP. It is important to note that while data on registration and voting reflect the actual election years indicated in each chart, data on citizens age 18 or older (CVAP) reflects a five-year average (e.g. data for 2020 represents a 2016-2020 average). While the temporal alignment of the data is imperfect, the degree to which the CVAP data lags the voting and registration data is consistent across election years.

In some instances, rates of registration and voting rates that were derived came out to higher than 100 percent. This could be due partly to the temporal mismatch between the SWDB and CVAP data, partly due to inconsistencies in how data by race/ethnicity were collected between datasets, and/or partly due simply to inconsistencies in the underlying SWDB data itself. To avoid reporting impossible rates, the following edits were made to the data: in cases where the total vote was higher than total registration, total registration was set equal to total votes; subsequently, if total CVAP was found to be less than total registration, total CVAP was set equal to total registration. Overall, these adjustments lead to an average difference in total CVAP from the original values of only 1.6 percent across all years and Atlas geographies. See the methodology page for other relevant notes.

Notes:

• Racial/ethnic identification of data on registration and voting is only available for Latino and Asian American citizens, and is imputed based on surnames.
• For data CVAP, Latinos include people of Hispanic origin of any race and Asian Americans exclude people of Hispanic origin.
• Data on registration and voting reflect the year indicated in the chart while data on citizens age 18 or older (CVAP) reflects a five-year average (e.g. data for 2020 represents a 2016-2020 average).
• In a small number of cases where the total vote was higher than total registration, total registration was set equal to total votes; subsequently, if total CVAP was found to be less than total registration, total CVAP was set equal to total registration.
• 2020 and 2022 general election data are supplemented with the 2019 and 2021 5-year ACS data respectively due to data availability.

Summary: A household is considered to be linguistically isolated when no member age 14 years or older speaks only English or speaks English at least “very well.” 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 households.

Methods: The number and percentage of linguistically isolated (also referred to as limited English speaking) households was calculated by language spoken at home (if a language other than English was spoken) and ancestry for each year and geography. A household is considered to be linguistically isolated when no member, age 14 years or older, speaks only English or speaks English at least “very well.” The data for “all languages” included all households (even in which only English is spoken) while the data for “all other languages” is restricted to households that speak a language other than English that is not already included on the list provided. See the methodology page for other relevant notes.

Notes:

• Ancestry is based on that of the householder.
• Language spoken at home is determined by the first member of the household that speaks a language other than English, following a particular priority order defined by the Census. For more information, see here.
• Data for 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 ancestry breakdown, as it is based on the IPUMS microdata.
• No data are reported if based on fewer than 100 individual (i.e. unweighted) households for by ancestry breakdowns.
• No data are reported if based on fewer than 100 households for the trend, by language, ranking and map breakdowns.

Summary: Actual and projected aggregate disposable income for renters, and average disposable income/percent gain in disposable income per rent-burdened household, under a scenario in which no households are rent burdened (paying more than 30 percent of household income on gross rent). Disposable income is defined as household income minus gross rent. Data for 2020 represents a 2016-2020 average. All dollar values are in 2020 dollars.

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% sample, and 2010 and 2020 American Community Survey 5-year samples.

Universe: All renter-occupied households.

Methods: Aggregate disposable income, defined as household income minus gross rent, and average disposable income per rent burdened household was calculated by race/ethnicity and poverty level based on the actual survey data sources for each year and geography. Rent burden was defined as spending more than 30 percent of household income on gross rent, which includes contract rent and utilities. Aggregate disposable income and average disposable income per rent burdened household were then recalculated under a hypothetical scenario in which no households were rent burdened (i.e. in which gross rent was capped at 30 percent of household income for all households). Gains in aggregate (total) disposable income and in average disposable income per (formerly) rent burdened household under the hypothetical scenario of no rent burden were then derived. Negative values for average percent gain in disposable income were set to missing. Values for 2000 and 2010 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.
• Demographic characteristics are based on those of the householder.
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• All dollar values are in 2020 dollars.
• No data is available for other cities or towns or Census Designated Places as this indicator in entirely based on the IPUMS microdata.
• No data are reported if based on fewer than 100 individual (i.e. unweighted) renter-occupied households.