Historical dataset of population level and growth rate for the Angeles City, Philippines metro area from 1950 to 2025.

About this dataset

Context

This list ranks the 88 cities in the Los Angeles County, CA by Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Asian Population: This column displays the rank of cities in the Los Angeles County, CA by their Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Asian Population: The Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Los Angeles County Hispanic Asian Population: This tells us how much of the entire Los Angeles County, CA Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Graph and download economic data for Resident Population in Los Angeles County, CA (CALOSA7POP) from 1970 to 2024 about Los Angeles County, CA; Los Angeles; residents; CA; population; and USA.

Tabular data of 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 2010 census tracts split by 2015 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. 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 2010 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 Field:CT10: 2010 Census tractFIP15: 2015 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2015) CT10FIP15: 2010 census tract with 2015 city FIPs for incorporated cities and unincorporated areas. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP15_AGE_0_4: 2015 population 0 to 4 years oldPOP15_AGE_5_9: 2015 population 5 to 9 years old POP15_AGE_10_14: 2015 population 10 to 14 years old POP15_AGE_15_17: 2015 population 15 to 17 years old POP15_AGE_18_19: 2015 population 18 to 19 years old POP15_AGE_20_44: 2015 population 20 to 24 years old POP15_AGE_25_29: 2015 population 25 to 29 years old POP15_AGE_30_34: 2015 population 30 to 34 years old POP15_AGE_35_44: 2015 population 35 to 44 years old POP15_AGE_45_54: 2015 population 45 to 54 years old POP15_AGE_55_64: 2015 population 55 to 64 years old POP15_AGE_65_74: 2015 population 65 to 74 years old POP15_AGE_75_84: 2015 population 75 to 84 years old POP15_AGE_85_100: 2015 population 85 years and older POP15_WHITE: 2015 Non-Hispanic White POP15_BLACK: 2015 Non-Hispanic African AmericanPOP15_AIAN: 2015 Non-Hispanic American Indian or Alaska NativePOP15_ASIAN: 2015 Non-Hispanic Asian POP15_HNPI: 2015 Non-Hispanic Hawaiian Native or Pacific IslanderPOP15_HISPANIC: 2015 HispanicPOP15_MALE: 2015 Male POP15_FEMALE: 2015 Female POV15_WHITE: 2015 Non-Hispanic White below 100% Federal Poverty Level POV15_BLACK: 2015 Non-Hispanic African American below 100% Federal Poverty Level POV15_AIAN: 2015 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV15_ASIAN: 2015 Non-Hispanic Asian below 100% Federal Poverty Level POV15_HNPI: 2015 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV15_HISPANIC: 2015 Hispanic below 100% Federal Poverty Level POV15_TOTAL: 2015 Total population below 100% Federal Poverty Level POP15_TOTAL: 2015 Total PopulationAREA_SQMIL: Area in square milePOP15_DENSITY: Population per square mile.POV15_PERCENT: Poverty rate/percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by 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 2010 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. Note:1. Population and poverty data estimated as of July 1, 2015. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

About this dataset

Context

This list ranks the 88 cities in the Los Angeles County, CA by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Los Angeles County, CA by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Los Angeles County Black Population: This tells us how much of the entire Los Angeles County, CA Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Tabular data of 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 2021 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. 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 Field:CT20: 2020 Census tractFIP21: 2021 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2021) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP21CSA: 2020 census tract with 2021 city FIPs for incorporated cities, 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.POP21_AGE_0_4: 2021 population 0 to 4 years oldPOP21_AGE_5_9: 2021 population 5 to 9 years old POP21_AGE_10_14: 2021 population 10 to 14 years old POP21_AGE_15_17: 2021 population 15 to 17 years old POP21_AGE_18_19: 2021 population 18 to 19 years old POP21_AGE_20_44: 2021 population 20 to 24 years old POP21_AGE_25_29: 2021 population 25 to 29 years old POP21_AGE_30_34: 2021 population 30 to 34 years old POP21_AGE_35_44: 2021 population 35 to 44 years old POP21_AGE_45_54: 2021 population 45 to 54 years old POP21_AGE_55_64: 2021 population 55 to 64 years old POP21_AGE_65_74: 2021 population 65 to 74 years old POP21_AGE_75_84: 2021 population 75 to 84 years old POP21_AGE_85_100: 2021 population 85 years and older POP21_WHITE: 2021 Non-Hispanic White POP21_BLACK: 2021 Non-Hispanic African AmericanPOP21_AIAN: 2021 Non-Hispanic American Indian or Alaska NativePOP21_ASIAN: 2021 Non-Hispanic Asian POP21_HNPI: 2021 Non-Hispanic Hawaiian Native or Pacific IslanderPOP21_HISPANIC: 2021 HispanicPOP21_MALE: 2021 Male POP21_FEMALE: 2021 Female POV21_WHITE: 2021 Non-Hispanic White below 100% Federal Poverty Level POV21_BLACK: 2021 Non-Hispanic African American below 100% Federal Poverty Level POV21_AIAN: 2021 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV21_ASIAN: 2021 Non-Hispanic Asian below 100% Federal Poverty Level POV21_HNPI: 2021 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV21_HISPANIC: 2021 Hispanic below 100% Federal Poverty Level POV21_TOTAL: 2021 Total population below 100% Federal Poverty Level POP21_TOTAL: 2021 Total PopulationAREA_SQMIL: Area in square milePOP21_DENSITY: Population per square mile.POV21_PERCENT: Poverty percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by 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. Note:1. Population and poverty data estimated as of July 1, 2021. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

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.

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

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

As of October 2024, approximately ****** Japanese residents lived in Los Angeles, continuing the downward trend. Los Angeles had the largest Japanese population of any city outside Japan. In the same year, the United States was by far the country with the highest number of Japanese residents.

Abstract

The Labor Force Survey is a nationwide survey of households conducted regularly to gather data on the demographic and socio-economic characteristics of the population. It is primarily geared towards the estimation of the levels of employment in the country.

The Labor Force Survey aims to provide a quantitative framework for the preparation of plans and formulation of policies affecting the labor market.

Geographic coverage

National coverage, the sample design has been drawn in such a way that accurate lower level classification would be possible. The 73 provinces, 14 cities of the Philippines are covered.

Analysis unit

• Person/ individual

Universe

The survey covered all persons 10 years old and over. Persons who reside in institutions are not covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Labor Force Survey adopts that of the Integrated Survey of Households (ISH), which uses a stratified two-stage sampling design. It is prepared by the NEDA Technical Committee on Survey Design and first implemented in 1984. It is the same sampling design used in the ISH modules starting in 1986.

The urban and rural areas of each province are the principal domains of the survey. In addition, the urban and rural areas of cities with a population of 150,000 or more as of 1980 are also made domains of the survey. These cities are the four cities in Metro Manila (Manila, Quezon City, Pasay and Caloocan); and the cities of Angeles, Olongapo,, Bacolod, Iloilo, Cebu, Zamboanga, Butuan, Cagayan de Oro, Davao, and Iligan.

The rest of Metro Manila, i.e., Pasig, Makati and the 11 other municipalities, are treated as three separate domains. In the case of Makati, six exclusive villages are identified and samples are selected using a different scheme. These villages are Forbes Park, Bel-Air, Dasmarinas, San Lorenzo, Urdaneta and Magallanes.

Sampling Units and Sampling Frame The primary sampling units (PSUs) under the sample design are the barangays and the households within each sample barangay comprise the secondary sampling units (SSUs). The frame from which the sample barangays are drawn is obtained from the 1980 Census of Population and Housing (CPH). Hence, all the approximately 40,000 barangays covered in the 1980 CPH are part of the primary sampling frame. The sampling frame for the SSUs, that is, the households, is prepared by listing all households in each of the selected sample barangays. The listing operation is conducted regularly in the sample barangays to update the secondary sampling frame from where the sample households are selected.

Sample Size and Sampling Fraction The size of the sample is envisioned to meet the demand for fairly adequate statistics at the domain level. Taking this need into account and considering cost constraints as well, the decision reached is for a national sample of about 20,000 households. In general, the sample design results in self-weighting samples within domains, with a uniform sampling fraction of 1:400 for urban and 1:600 for rural areas. However, special areas are assigned different sampling fractions so as to obtain "adequate" samples for each. Special areas refer to the urban and rural areas of a province or large city which are small relative to their counterparts.

Selection of Samples For the purpose of selecting PSUs, the barangay in each domain are arranged by population size (as of the 1980 Census of Population) in descending order and then grouped into strata of approximately equal sizes. Four independent PSUs are drawn with probability proportional to size with complete replacement.

Secondary sampling units are selected systematiclally with a random start.

Sampling deviation

Replacement of non-responding or transferred sample households is allowed although it is still possible to have non-response cases due to critical peace and order situation or inaccessibility of the selected sample households. If there are unenumerated barangays or sample households, non-response adjustments are utilized.

Mode of data collection

Face-to-face [f2f]

Research instrument

The items of information presented in the April 1991 Quarterly Labor Force Survey questionnaire were derived from a structured questionnaire covering the demographic and economic characteristics of individuals. The demographic characteristics include age, sex, relationship to household head, marital status, and highest grade completed. The economic characteristics include employment status, occupation, industry, nomal working hours, total hours worked, class of worker, etc.

Cleaning operations

Data processing involves two stages: manual processing and machine processing. Manual processing refers to the manual editing and coding of questionnaires. This was done prior to machine processing which entailed code validation, consistency checks as well as tabulation.

Enumeration is a very complex operation and may happen that accomplished questionnaires may have some omissions and implausible or inconsistent entries. Editing is meant to correct these errors.

For purposes of operational convenience, field editing was done. The interviewers were required to review the entries at the end of each interview. Blank items, which were applicable to the respondents, were verified and filled out. Before being transmitted to the regional office, all questionnaires were edited in the field offices.

Coding, the transformation of information from the questionnaire to machine readable form, was likewise done in the field offices.

Machine processing involved all operations that were done with the use of a computer and/or its accessories, that is, from data encoding to tabulation. Coded data are usually in such media as tapes and diskettes. Machine editing is preferred to ensure correctness of encoded information. Except for sample completeness check and verification of geographic identification which are the responsibility of the subject matter division, some imputations and corrections of entries are done mechanically.

Response rate

The response rate for April 1991 LFS was 99.86 percent. The non-response rate of 0.14 percent was due to crticial peace and order situation or inaccessibility of the selected sample or sample households.

Sampling error estimates

Standard Error (SE) and Coefficient of Variation (CV) for the selected variables of the Labor Force Survey (LFS) for April 1991 survey round was computed using the statistical package IMPS. The selected variables referred to include the employment, unemployment and labor force population levels and rates.

A sampling error is usually measured in terms of the standard error for a particular statistic. A standard error is a measure of dispersion of an estimate from the expected value.

The SE can be used to calculate confidence intervals within which the true value for the population can be estimated, while the CV is a measure of relative variability that is commonly used to assess the precision of survey estimates.

The CV is defined as the ratio of the standard error and the estimate. An estimate with CV value of less than 10 percent is considered precise.

In 2023, the real GDP of the Los Angeles metro area amount to around 1.08 trillion U.S. dollars, and increase after 2021. The overall quarterly GDP growth in the United States can be found here. Gross domestic product of Los AngelesWith a population of over 12.8 million inhabitants in 2023, Los Angeles is the second-largest city in America, following only New York. The Los Angeles metro area also ranked second among U.S. metro areas in terms of gross metropolitan product, second again only to New York City metro area, which came in with a GMP of 1.99 trillion U.S. dollars to Los Angeles’ 1.13 trillion U.S. dollars in the fiscal year of 2021. Chicago metro area ranked third with GMP of 757.2 billion U.S. dollars. Additional detailed statistics about GDP in the United States is available here. Despite Los Angeles’ high GDP, L.A. did not do as well as some cities in terms of median household income. Los Angeles ranked 9th with a median household income of 76,135 U.S. dollars annually in 2022. This was slightly higher than the median household income of the United States in 2022, which came in at 74,580 U.S. dollars annually. Located in Southern California, Los Angeles is home to Hollywood, the famous epicenter of the U.S. film and television industries. The United States is one of the leading film markets worldwide, producing 449 films in 2022, many of them produced by Hollywood-based studios. In 2018, movie ticket sales in North America generated over 11.89 billion U.S. dollars in box office revenue. Famous Hollywood actresses earn millions annually, with the best paid, Sofia Vergara, earning 43 million U.S. dollars in 2020. Second on the list was Angelina Jolie with earnings of 35.5 million U.S. dollars.

About this dataset

Context

The dataset tabulates the Port Angeles median household income by race. The dataset can be utilized to understand the racial distribution of Port Angeles income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Port Angeles, WA median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Port Angeles, WA (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Port Angeles median household income by race. You can refer the same here