In 2023, the number of Hispanic and Latino residents in California had surpassed the number of White residents, with about ***** million Hispanics compared to ***** million White residents. California’s residents California has always held a special place in the American imagination as a place where people can start a new life and increase their personal fortunes. Perhaps due partly to this, California is the most populous state in the United States, with over ** million residents, which is a significant increase from the number of residents in 1960. California is also the U.S. state with the largest population of foreign born residents. The Californian economy The Californian economy is particularly strong and continually contributes a significant amount to the gross domestic product (GDP) of the United States. Its per-capita GDP is also high, which indicates a high standard of living for its residents. Additionally, the median household income in California has more than doubled from 1990 levels.

About this dataset

Context

The dataset tabulates the California Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of California, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of California.

Key observations

Among the Hispanic population in California, regardless of the race, the largest group is of Mexican origin, with a population of 12.75 million (81.74% of the total Hispanic population).

https://i.neilsberg.com/ch/california-population-by-race-and-ethnicity.jpeg" alt="California Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the California
• Population: The population of the specific origin for Hispanic or Latino population in the California is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of California total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of California by race. It includes the distribution of the Non-Hispanic population of California across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of California across relevant racial categories.

Key observations

Of the Non-Hispanic population in California, the largest racial group is White alone with a population of 13.57 million (57.48% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the California
• Population: The population of the racial category (for Non-Hispanic) in the California is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of California total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Race & Ethnicity. You can refer the same here

In 2023, the resident population of California was ***** million. This is a slight decrease from the previous year, with ***** million people in 2022. This makes it the most populous state in the U.S. Californian demographics Along with an increase in population, California’s gross domestic product (GDP) has also been increasing, from *** trillion U.S. dollars in 2000 to **** trillion U.S. dollars in 2023. In the same time period, the per-capita personal income has almost doubled, from ****** U.S. dollars in 2000 to ****** U.S. dollars in 2022. In 2023, the majority of California’s resident population was Hispanic or Latino, although the number of white residents followed as a close second, with Asian residents making up the third-largest demographic in the state. The dark side of the Golden State While California is one of the most well-known states in the U.S., is home to Silicon Valley, and one of the states where personal income has been increasing over the past 20 years, not everyone in California is so lucky: In 2023, the poverty rate in California was about ** percent, and the state had the fifth-highest rate of homelessness in the country during that same year, with an estimated ** homeless people per 10,000 of the population.

Chart and table of population level and growth rate for the state of California from 1900 to 2024.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Los Angeles County, CA (B03002011E006037) from 2009 to 2023 about Los Angeles County, CA; Los Angeles; CA; non-hispanic; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Kern County, CA (B03002020E006029) from 2009 to 2023 about Kern County, CA; Bakersfield; latino; hispanic; CA; estimate; persons; 5-year; population; and USA.

Age-Race-Sex population estimates for all California Local Health Jurisdictions and counties. Based on combining California Department of Finance projections with Census estimates to generate County and LHJ City (Berkeley, Long Beach, and Pasadena) data.

Provides population data for calculation of rates, and to describe the demographic distribution of the population, for CDPH, other CalHHS departments, Local Health Jurisdictions, and other users

In 2023, there were over **** million White residents of Los Angeles city in California. In comparison, there were ******* Asian residents and ******* Black or African American residents amongst the Los Angeles population in that year.

Relative concentration of the Northern California region's Hispanic and/or Black, Indigenous or person of color (HSPBIPOC) population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Orange by race. It includes the distribution of the Non-Hispanic population of Orange across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Orange across relevant racial categories.

Key observations

Of the Non-Hispanic population in Orange, the largest racial group is White alone with a population of 55,627 (66.81% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Orange
• Population: The population of the racial category (for Non-Hispanic) in the Orange is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Orange total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Orange Population by Race & Ethnicity. You can refer the same here

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

U.S. Census Bureau QuickFacts statistics for Whittier city, California. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Amador County, CA (B03002021E006005) from 2009 to 2023 about Amador County, CA; latino; hispanic; CA; estimate; persons; 5-year; population; and USA.

In 2022, around 48.59 percent of New Mexico's population was of Hispanic origin, compared to the national percentage of 19.45. California, Texas, and Arizona also registered shares over 30 percent. The distribution of the U.S. population by ethnicity can be accessed here.

Population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2023 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries as of July 1, 2023. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/)released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Fields:CT20: 2020 Census tractFIP22: 2023 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2023) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP23CSA: 2020 census tract with 2023 city FIPs for incorporated cities and unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP23_AGE_0_4: 2023 population 0 to 4 years oldPOP23_AGE_5_9: 2023 population 5 to 9 years old POP23_AGE_10_14: 2023 population 10 to 14 years old POP23_AGE_15_17: 2022 population 15 to 17 years old POP23_AGE_18_19: 2023 population 18 to 19 years old POP23_AGE_20_44: 2023 population 20 to 24 years old POP23_AGE_25_29: 2023 population 25 to 29 years old POP23_AGE_30_34: 2023 population 30 to 34 years old POP23_AGE_35_44: 2023 population 35 to 44 years old POP23_AGE_45_54: 2023 population 45 to 54 years old POP23_AGE_55_64: 2023 population 55 to 64 years old POP23_AGE_65_74: 2023 population 65 to 74 years old POP23_AGE_75_84: 2023 population 75 to 84 years old POP23_AGE_85_100: 2023 population 85 years and older POP23_WHITE: 2023 Non-Hispanic White POP23_BLACK: 2023 Non-Hispanic African AmericanPOP23_AIAN: 2023 Non-Hispanic American Indian or Alaska NativePOP23_ASIAN: 2023 Non-Hispanic Asian POP23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific IslanderPOP23_HISPANIC: 2023 HispanicPOP23_MALE: 2023 Male POP23_FEMALE: 2023 Female POV23_WHITE: 2023 Non-Hispanic White below 100% Federal Poverty Level POV23_BLACK: 2023 Non-Hispanic African American below 100% Federal Poverty Level POV23_AIAN: 2023 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV23_ASIAN: 2023 Non-Hispanic Asian below 100% Federal Poverty Level POV23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV23_HISPANIC: 2023 Hispanic below 100% Federal Poverty Level POV23_TOTAL: 2023 Total population below 100% Federal Poverty Level POP23_TOTAL: 2023 Total PopulationAREA_SQMil: Area in square mile.POP23_DENSITY: 2023 Population per square mile.POV23_PERCENT: 2023 Poverty rate/percentage.How this data created?Population by age groups, ethnic groups and gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Notes:1. Population and poverty data estimated as of July 1, 2023. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundaries are as of July 1, 2023.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Modoc County, CA (B03002011E006049) from 2009 to 2023 about Modoc County, CA; CA; non-hispanic; estimate; persons; 5-year; population; and USA.

More than 39 million people and 14.2 million households span more than 163,000 square miles of Californian’s urban, suburban and rural communities. California has the fifth largest economy in the world and is the most populous state in the nation, with nation-leading diversity in race, ethnicity, language and socioeconomic conditions. These characteristics make California amazingly unique amongst all 50 states, but also present significant challenges to counting every person and every household, no matter the census year. A complete and accurate count of a state’s population in a decennial census is essential. The results of the 2020 Census will inform decisions about allocating hundreds of billions of dollars in federal funding to communities across the country for hospitals, fire departments, school lunch programs and other critical programs and services. The data collected by the United States Census Bureau (referred hereafter as U.S. Census Bureau) also determines the number of seats each state has in the U.S. House of Representatives and will be used to redraw State Assembly and Senate boundaries. California launched a comprehensive Complete Count Census 2020 Campaign (referred to hereafter as the Campaign) to support an accurate and complete count of Californians in the 2020 Census. Due to the state’s unique diversity and with insights from past censuses, the Campaign placed special emphasis on the hardest-tocount Californians and those least likely to participate in the census. The California Complete Count – Census 2020 Office (referred to hereafter as the Census Office) coordinated the State’s operations to complement work done nationally by the U.S. Census Bureau to reach those households most likely to be missed because of barriers, operational or motivational, preventing people from filling out the census. The Campaign, which began in 2017, included key phases, titled Educate, Motivate and Activate. Each of these phases were designed to make sure all Californians knew about the census, how to respond, their information was safe and their participation would help their communities for the next 10 years.

The purpose of this data package is to offer relevant demographic data for those interested to understand the health status of California population groups. This includes health indicators like newborn screenings for congenital diseases, emergency department diagnosis and visits for an asthma attack, infections among California population and surgical site infections along with demographic indicators influenced directly by the population health.

Relative concentration of the Southern California region's Asian American population. The variable ASIANALN records all individuals who select Asian as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with the Asian race alone.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as ASIANALN alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as ASIANALN alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of ASIANALN individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then ASIANALN individuals are highly concentrated locally.