California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

US Census American Community Survey Custom Tabulation (ST542) by Census Tract. Language spoken at home for population 5 years and over by ability to speak English, summarized by census tract for 114 languages spoken across LA County, 5-year estimates 2019-2023.See also source data tables:Census Tracts: Language Spoken at Home LA County Census TractsLA County: Language Spoken at Home LA County Headings:GEOIDGeography identificationCT20Census tract (2020)NameCensus tract nameCSACountywide Statistical Area (city or community)SPAService Planning AreaSDSupervisorial Districttotal_popPopulation over 5 years old in census tract (universe)total_limited_engPopulation that speaks English less than "very well"total_limited_eng_pctPercent of population that speaks English less than "very well"

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail.The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts.The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate.More information about these data is available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review FAQs.Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data.Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR).1. Population Density: 2020 Population per square mile,2. Poverty Rate: Percentage of population under 100% FPL,3. Median Household income: Based on countywide median HH income of $71,538.4. Highschool Education Attainment: Percentage of 18 years and older population without high school graduation.5. English Speaking Ability: Percentage of 18 years and older population with less or none English speaking ability. 6. Household without Internet Access: Percentage of HH without internet access.7. Non-Hispanic White Population: Percentage of Non-Hispanic White population.8. Non-Hispanic African-American Population: Percentage of Non-Hispanic African-American population.9. Non-Hispanic Asian Population: Percentage of Non-Hispanic Asian population.10. Hispanic Population: Percentage of Hispanic population.

This statistic shows the 25 largest counties in the United States in 2022, by population. In 2022, about 9.72 million people were estimated to be living in Los Angeles County, California.

Additional information on urbanization in the United States

Urbanization is defined as the process by which cities grow or by which societies become more urban. Rural to urban migration in the United States, and around the world, is often undertaken in the search for employment or to enjoy greater access to services such as healthcare. The largest cities in the United States are steadily growing. Given their size, incremental increases yield considerable numerical gains as seen by New York increasing by 69,777 people in 2011, the most of any city. However in terms of percentage growth, smaller cities outside the main centers are growing the fastest, such as Georgetown city and Leander city in Texas.

Urbanization has increased slowly in the United States, rising from 80.77 percent of the population living in urban areas in 2010 to 82.66 percent in 2020. In 2018, the United States ranked 14th in a ranking of countries based on their degree of urbanization. Unlike fully urbanized countries such as Singapore and Hong Kong, the United States maintains a sizeable agricultural industry. Although technological developments have reduced demands for rural labor, labor in the industry and supporting services are still required.

About this dataset

Context

The dataset tabulates the population of Los Angeles County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Los Angeles County. The dataset can be utilized to understand the population distribution of Los Angeles County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Los Angeles County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Los Angeles County.

Key observations

Largest age group (population): Male # 30-34 years (407,113) | Female # 30-34 years (391,234). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Los Angeles County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Los Angeles County is shown in the following column.
• Population (Female): The female population in the Los Angeles County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Los Angeles County for each age group.

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 Los Angeles County Population by Gender. You can refer the same here

Population with limited English proficiency is defined as persons over the age of 5 years that speak English less than very well.Individuals with limited English proficiency can face significant language barriers, which can make it difficult for them to navigate various social systems, such as educational institutions, or access essential services, such as health insurance, healthcare, or food assistance programs.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

Includes median household income in the past twelve months (in 2022 inflation-adjusted dollars). Geography-specific median household income are calculated as the population-weighted averages of the median household incomes within their respective 2020 census tracts. Median household income is defined as the amount that divides the household income distribution of a population into two equal groups; half of the population has a household income above that amount, whereas the other half has a household income below that amount. Household income is an important driver of life expectancy and other health outcomes, as individuals with higher household incomes, on average, experience better health and live longer than individuals with lower household incomes. This is largely due to increased access to opportunities, resources, and healthier living conditions that higher income individuals experience compared to lower income individuals.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Adults ages 18+ who walked for transportation or leisure for at least 150 minutes in the past week. Years covered are 2011 to 2012 by zip code. Data taken from the California Health Interview Survey Neighborhood Edition (AskCHIS NE) (http://askchisne.ucla.edu/), downloaded January 2016."Field" = "Definition""ZIPCODE" = postal zip code in LA County "Zip_code" = postal zip code in LA County "PAdPhysAct" = fraction of projected 18 and older population walking more than or equal to 150 minutes per week residing in Zip Code"PAdPhysAct2" = percentage of projected 18 and older population walking more than or equal to 150 minutes per week residing in Zip Code"NAdPhysAct" = number of projected 18 and older population walking more than or equal to 150 minutes per week residing in Zip Code"Pop_18olde" = projected 18 and older population total residing in Zip CodeHealth estimates available in AskCHIS NE (Neighborhood Edition) are model-based small area estimates (SAEs).SAEs are not direct estimates (estimates produced directly from survey data, such as those provided through AskCHIS).CHIS data and analytic results are used extensively in California in policy development, service planning and research, and is recognized and valued nationally as a model population-based health surveyFAQ: 1. Which cycle of CHIS does AskCHIS Neighborhood Edition provide estimates for?All health estimates in this version of AskCHIS Neighborhood Edition are based on data from the 2011- 2012 California Health Interview Survey. Socio-demographic indicators come from the 2008-2012 American Community Survey (ACS) 5-year summary tables. 2. Why do your population estimates differ from other sources like ACS? The population estimates in AskCHIS NE represent the CHIS 2011-2012 population sample, which excludes Californians living in group quarters (such as prisons, nursing homes, and dormitories). 3. Why isn't there data available for all ZIP codes / cities in Los Angeles?While AskCHIS NE has data on all ZCTAs (Zip Code Tabulation Areas), two factors may influence our ability to display the estimates:A small population (under 15,000): currently, the application only shows estimates for geographic entities with populations above 15,000. If your ZCTA has a population below this threshold, the easiest way to obtain data is to combine it with a neighboring ZCTA and obtain a pooled estimate. A high coefficient of variation: high coefficients of variation denote statistical instability.

OverviewThese are the Homeless Counts for 2020 as provided by the Los Angeles Homeless Services Authority (LAHSA), and the cities of Glendale, Pasadena, and Long Beach. The majority of this data comes from LAHSA using tract-level counts; the cities of Glendale, Pasadena, and Long Beach did not have tract-level counts available. The purpose of this layer is to depict homeless density at a community scale. Please read the note from LAHSA below regarding the tract level counts. In this layer LAHSA's tract-level population count was rounded to the nearest whole number, and density was determined per square mile of each community. It should be noted that not all of the sub-populations captured from LAHSA (eg. people living in vans, unaccompanied minors, etc.) are not captured here; only sheltered, unsheltered, and total population. Data generated on 12/2/20.Countywide Statistical AreasLos Angeles County's 'Countywide Statistical Areas' layer was used to classify the city / community names. Since this is tract-level data there are several times where a tract is in more than one city/community. Whatever the majority of the coverage of a tract is, that is the community that got coded. The boundaries of these communities follow aggregated tract boundaries and will therefore often deviate from the 'Countywide Statistical Area' boundaries.Note from LAHSALAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels. Due to rounding, the census tract-level data may not add up to the total for Los Angeles City Council District, Supervisorial District, Service Planning Area, or the Los Angeles Continuum of Care.The Los Angeles Continuum of Care does not include the Cities of Long Beach, Glendale, and Pasadena and will not equal the countywide Homeless Count Total.Street Count Data include persons found outside, including persons found living in cars, vans, campers/RVs, tents, and makeshift shelters. A conversion factor list can be found at https://www.lahsa.org/homeless-count/Please visit https://www.lahsa.org/homeless-count/home to view and download data.Last updated 07/16/2020

In terms of population size, the sex ratio in the United States favors females, although the gender gap is remaining stable. In 2010, there were around 5.17 million more women, with the difference projected to decrease to around 3 million by 2027.

Gender ratios by U.S. state In the United States, the resident population was estimated to be around 331.89 million in 2021. The gender distribution of the nation has remained steady for several years, with women accounting for approximately 51.1 percent of the population since 2013. Females outnumbered males in the majority of states across the country in 2020, and there were eleven states where the gender ratio favored men.

Metro areas by population National differences between male and female populations can also be analyzed by metropolitan areas. In general, a metropolitan area is a region with a main city at its center and adjacent communities that are all connected by social and economic factors. The largest metro areas in the U.S. are New York, Los Angeles, and Chicago. In 2019, there were more women than men in all three of those areas, but Jackson, Missouri was the metro area with the highest share of female population.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). This indicator is based on self-report and includes adults who had at least one drink of any alcoholic beverage (such as beer, wine, or liquor) in the past month.In the US, alcohol use is legal for those ages 21 years and older and should be avoided or used in moderation (defined as consuming two or less drinks per day for men or one or less drinks per day for women). Excessive alcohol use includes binge drinking, heavy drinking, any underage alcohol use, and any alcohol use by pregnant persons. Alcohol use is associated with numerous health, safety, and social problems, including chronic diseases, unintentional injuries, interpersonal violence, fetal alcohol spectrum disorders, alcohol use disorders, and weakened interpersonal relationships and ability to function at work, school, or home. In general, people with higher socioeconomic status (SES) report drinking more frequently and more heavily than those with lower SES; however, people with lower SES are on average more negatively affected by alcohol-related harms. It is important for cities and communities to build strategies that create environments that reduce excessive alcohol use and prevent underage drinking.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

How many incorporated places are registered in the U.S.?

There were 19,502 incorporated places registered in the United States as of July 31, 2019. 16,410 had a population under 10,000 while, in contrast, only 10 cities had a population of one million or more.

Small-town America

Suffice it to say, almost nothing is more idealized in the American imagination than small-town America. When asked where they would prefer to live, 30 percent of Americans reported that they would prefer to live in a small town. Americans tend to prefer small-town living due to a perceived slower pace of life, close-knit communities, and a more affordable cost of living when compared to large cities.

An increasing population

Despite a preference for small-town life, metropolitan areas in the U.S. still see high population figures, with the New York, Los Angeles, and Chicago metro areas being the most populous in the country. Metro and state populations are projected to increase by 2040, so while some may move to small towns to escape city living, those small towns may become more crowded in the upcoming decades.

The 2016 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

The records in this file allow users to map the parts of Urban Areas that overlap a particular county.

After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.

The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

National and subnational mid-year population estimates for England and Wales by administrative area, age and sex (including components of population change, median age and population density).

description: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org; abstract: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org

Life expectancy at birth is the average number of years a group of infants would live if they were to experience, throughout their lives, the age-specific death rates prevailing during a specified period. Life expectancy at birth estimates were calculated using abridged period life tables according to the Chiang method. Estimates are based on provisional data and subject to change. Unstable estimates are excluded and are defined as having confidence intervals greater than 6 years, i.e., +/-3.0 years. The average life expectancy of a population is one of the most basic and important measures of the health of a community. Life expectancy is heavily driven by the social determinants of health, including social, economic, and environmental conditions, with Black and low-income individuals experiencing much lower life expectancies compared to White and more affluent individuals.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Los Angeles County. The dataset can be utilized to gain insights into gender-based income distribution within the Los Angeles County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Los Angeles County, among individuals aged 15 years and older with income, there were 3.48 million men and 3.32 million women in the workforce. Among them, 1.94 million men were engaged in full-time, year-round employment, while 1.46 million women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.61% fell within the income range of under $24,999, while 10.60% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 29.65% of men in full-time roles earned incomes exceeding $100,000, while 23.88% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Los Angeles County median household income by race. You can refer the same here