Graph and download economic data for Unemployment Rate in Los Angeles County, CA (CALOSA7URN) from Jan 1990 to Apr 2025 about Los Angeles County, CA; Los Angeles; CA; unemployment; rate; and USA.

Unemployment Rate in Los Angeles County, CA was 5.10% in April of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Los Angeles County, CA reached a record high of 18.90 in May of 2020 and a record low of 4.10 in April of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Los Angeles County, CA - last updated from the United States Federal Reserve on June of 2025.

In 2023, the unemployment rate in California was 4.8 percent. This is an increase from the previous year, when the unemployment rate was 4.3 percent, and is down from a high of 12.5 percent in 2010.

The monthly unemployment rate for the United States can be accessed here.

Unemployment Rate in Los Angeles County, CA was 5.80% in January of 2024, according to the United States Federal Reserve. Historically, Unemployment Rate in Los Angeles County, CA reached a record high of 12.60 in January of 2010 and a record low of 4.40 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Los Angeles County, CA - last updated from the United States Federal Reserve on July of 2025.

California - Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) was 5.10% in March of 2025, according to the United States Federal Reserve. Historically, California - Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) reached a record high of 17.90 in May of 2020 and a record low of 3.70 in April of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for California - Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) - last updated from the United States Federal Reserve on May of 2025.

As of February 2023, the unemployment rate in the Los Angeles metropolitan area was 5.3 percent compared to 4.9 percent in the previous month. Within this time period, the unemployment rate was highest in January 2021 at 11 percent, falling to a low of 4.5 percent by May 2022. During this month there were 265,249 unemployed people unemployed in the Los Angeles metro area.

Chile Unemployment: Los Ángeles data was reported at 7.660 Person th in Apr 2019. This records a decrease from the previous number of 7.952 Person th for Mar 2019. Chile Unemployment: Los Ángeles data is updated monthly, averaging 4.793 Person th from Mar 2010 (Median) to Apr 2019, with 110 observations. The data reached an all-time high of 8.448 Person th in Oct 2014 and a record low of 2.295 Person th in Nov 2016. Chile Unemployment: Los Ángeles data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G021: Unemployment: NENE.

Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) was 5.60% in December of 2024, according to the United States Federal Reserve. Historically, Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) reached a record high of 18.40 in May of 2020 and a record low of 4.00 in June of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) - last updated from the United States Federal Reserve on June of 2025.

In the December 2024 ranking of the unemployment rates in the United States' larger metropolitan areas, the Minneapolis-St. Paul-Bloomington, Minnesota metro area had the lowest rate, at 2.5 percent. In the same period, the unemployment rate was highest in the Las Vegas-Henderson-Paradise, Nevada metro area at 5.9 percent.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. table name indicator name Universe timeframe source race notes source url

below_fpl_perc below 100% federal poverty level percent (%) Population for whom poverty status is determined 2016-2020 American Community Survey - S1703 Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S1703

below_200fpl_perc below 200% federal poverty level percent (%) Total population 2021 Population and Poverty Estimates of Los Angeles County Tract-City Splits by Age, Sex and Race-Ethnicity for July 1, 2021, Los Angeles, CA, April 2022 All races are Non-Hispanic LA County eGIS-Demography

median_income Median income (household) Households 2016-2020 American Community Survey - S1903 All races are Non-Hispanic; Race is that of householder https://data.census.gov/cedsci/table?q=S1903&g=0500000US06037

percapita_income Mean Per Capita Income Total population 2016-2020 American Community Survey - S1902 Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S1902

college_degree_any College degree AA, BA, or Higher % Population 25 years and over 2021 American Community Survey - B15002B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?q=b15002b&g=0500000US06037

graduate_professional_degree Graduate or professional degree % Population 25 years and over 2021 American Community Survey - B15002B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?q=b15002b&g=0500000US06037

unemployment_rate Unemployment Rate Population 16 years and over 2016-2020 American Community Survey - S2301 Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?q=S2301%3A%20EMPLOYMENT%20STATUS&g=0500000US06037&tid=ACSST5Y2020.S2301

below_300fpl_food_insecure Percent of Households with Incomes <300% Federal Poverty Level That Are Food Insecure Percent of Households with Incomes <300% Federal Poverty Level 2018 Los Angeles County Health Survey

https://publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

below_185fpl_snap Percent of Adults (Ages 18 Years and Older) with Household Incomes <185% Federal Poverty Level Who Are Currently Receiving Supplemental Nutrition Assistance Program (SNAP), Also Known as Calfresh Adults (Ages 18 Years and Older) with Household Incomes <185% Federal Poverty Level Los Angeles County Health Survey 20182018 https://publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

B24010 Sex by Occupation for the Civilian Employed Population 16 Years and Over Civilian employed population 16 years and over

This poll is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked if they felt that things in the United States were going in the right direction and whether they approved of how Bush was handling the presidency, the economy, race relations, education, and the environment. Respondents also offered approval ratings of Congress and their own Congressional representatives, rated the condition of the economy, and indicated whether they were better off financially than in 1989 when George Bush became president. In addition, respondents gave their impressions of Bush, Bill Clinton, Ross Perot, Dan Quayle, and television character Murphy Brown. They were also asked whether Vice President Quayle would be qualified to take over as president if something happened to Bush, and whether after four years of Bush a new president was needed that could set the country in a new direction. Concerning the 1992 presidential election, those surveyed rated their chances of voting, indicated for whom they would vote if the election were held the day of the interview, and commented on whether they supported a candidate because they liked him or because they didn't like the other candidates. Perot supporters were asked whether they would vote for Bush or Clinton if Perot did not run, and whether they would switch their support from Perot to one of the two major-party candidates in November. All respondents were asked if they thought the candidates were qualified, whether there was a candidate for whom they would definitely not vote under any circumstances, and whether they would be better off financially under Bush, Clinton, or Perot. Those surveyed were also asked which candidate would do the best job of dealing with a variety of problems including race relations, unemployment, foreign affairs, the economy, the environment, health care, and protecting the Social Security system. Respondents indicated the applicability of various characteristics to each of the candidates including strong leadership, vision for the future, trustworthiness in a crisis, understanding the needs of average Americans, honesty, the right temperament to serve as president, and high moral standards. In addition, those surveyed indicated whether the views of Bush, Clinton, and Perot were too liberal, too conservative, or just about right, whether they had a good idea of where the three candidates planned to lead the nation in the next four years, and whether they would be more or less likely to support a presidential candidate who had engaged in extramarital affairs, had never run for public office, or had come from a wealthy, privileged background. Other topics included assessments of the Republican and Democratic parties, re-electing representatives in Congress, the role of the federal government, and the Los Angeles riots of 1992. Background information on respondents includes political alignment, voter registration status, most recent presidential vote choice, education, age, religion, social class, area of residence, marital status, household composition, labor union membership, employment status, Hispanic origin, household income, and sex.

The LA Promise Zone is a collective impact project involving leaders from government, local institutions, non-profits and community organizations that targets resources to create jobs, boost public safety, improve public education and stimulate better housing opportunities for our residents and neighborhoods.The Promise Zone is located within Central Los Angeles and includes the neighborhoods of Hollywood, East Hollywood, Koreatown, Pico Union and Westlake. The Zone is home to approximately 165,000 residents, of whom 35% live in poverty. Nearly one-quarter of Promise Zone households earn less than $15,000 each year, and educational attainment for adults is weak with 35% of the population 25 years of age and older having obtained less than a high school diploma. The Promise Zone also has alarming high school dropout rates, high unemployment, and a shortage of affordable housing. Large shares of recent immigrant populations hailing from Latin America, Asia and Eastern Europe live there.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Abstract (en): Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 15 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Hispanic origin are included. In addition to providing these core data, the October survey also contains a special supplement on school enrollment for all persons surveyed aged 3 years old or older. This supplement includes the following items: current grade attending at public or private school, whether attending college full- or part-time at a two- or four-year institution, year last attended a regular school, and year graduated from high school. All persons in the civilian noninstitutional population of the United States living in households. The probability sample selected to represent the universe consists of approximately 57,000 households. The sample was located in 729 primary sampling units comprising 1,297 counties and independent cities with coverage in every state, the District of Columbia, and the sub-state areas of New York City and the Los Angeles-Long Beach metropolitan area. The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

The Justice Equity Need Index (JENI), by Advancement Project California, offers a means to map out the disparate burden that criminalization and a detention-first justice model place on specific communities. The index includes the following indicators:System Involvement: The system-involved population by ZIP Code results in direct needs for justice equity, as measured by adult and youth probation. Indicators: Adult Probation (per 1,000 people); Youth Probation (per 1,000 people) Inequity Drivers: Root inequities across communities that contribute to racial and economic disparities as seen in incarceration and policing. Indicators: Black, Latinx, AIAN, and NHPI Percentages of Population (average percentile); Unemployment Rate (%); Population aged 25+ without a High School Diploma (%); Population below 200% of the Federal Poverty Level (%); Violent Crime Rate (per 1,000 people) Criminalization Risk: Conditions where the criminal justice system has historically taken a detention-first, prevention-last approach. Indicators: Mental Health Hospitalizations (per 1,000 people); Substance Use-Related Hospitalizations (per 1,000 people); Homelessness Rate (per 1,000 people) Learn more at https://www.catalystcalifornia.org/campaign-tools/maps-and-data/justice-equity-need-index.Supervisorial Districts, SPAs, and CSAs determined by ZIP Code centroid.

Data are aggregated from census tract to Countywide Statistical Area (CSA).Link to full report, State of Black LA.For more information about the purpose of this data, please contact CEO-ARDI.For more information about the configuration of this data, please contact ISD-Enterprise GIS. Field Descriptions:

Field

Description

Source

Source Year

csa

Countywide Statistical Area

eGIS

2022

sd

Supervisorial District

eGIS

2021

med_income_total

Average median household income for all residents

US Census ACS 5-year table S1903

2020

med_income_black

Average median household income for Black residents

US Census ACS 5-year table S1903

2020

homeownership_total

Homeownership rate for all residents

US Census ACS 5-year table B25003

2020

homeownership_black

Homeownership rate for Black residents

US Census ACS 5-year table B25003B

2020

eviction_filings_per100_renters

Eviction filings per 100 renter households

The Eviction Lab

2002-2018 (yearly average of available years)

life_expectancy

Average life expectancy

CDC

2015

black_pop

Black population (alone or in combination)

US Census ACS 5-year table DP05

2020

black_pct

% Black population (alone or in combination)

US Census ACS 5-year table DP05

2020

nh_black_pop

Non-Hispanic Black alone population

US Census ACS 5-year table DP05

2020

nh_black_pct

% Non-Hispanic Black alone population

US Census ACS 5-year table DP05

2020

college_grad

Population of residents age 25+ with bachelor degree or higher

US Census ACS 5-year table DP02

2020

college_grad_pct

% of all residents age 25+ with bachelor degree or higher

US Census ACS 5-year table DP02

2020

college_grad_black

Population of Black residents age 25+ with bachelor degree or higher

US Census ACS 5-year table S1501

2020

college_grad_black_pct

% of Black residents age 25+ with bachelor degree or higher

US Census ACS 5-year table S1501

2020

unemployment

Unemployment Rate

US Census ACS 5-year table S2301

2020

unemployment_black

Black (Alone) Unemployment Rate

US Census ACS 5-year table S2301

2020

total_pop

Total population

US Census ACS 5-year table DP05

2020

Shape

CSA Geometry

eGIS

2022

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Armed Forces data are not shown for the population 65 years and over..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2023, the GDP of the Chicago-Naperville-Elgin metropolitan area amounted to ****** billion chained 2017 U.S. dollars. The GDP of the United States since 1990 can be accessed here. Economic growth and unemployment in Chicago Economic growth in Chicago, measured by the growth in Gross Domestic Product (GDP), was significant in the years between 2001 and 2022. This growth occurred in a period of growth for cities nationally as seen by growth of other major American cities such as Los Angeles and San Francisco. In contrast to Chicago’s growth, San Francisco’s growth rate demonstrated the effect of a new and booming industry. The influence of technology and internet companies saw San Francisco grow nearly ** percent in comparison to the ** percent growth in GDP achieved by Chicago. As a result, Chicago-Naperville-Elgin ranked third in Gross Metropolitan Product of the United States, by metropolitan area in 2022. The drop in GDP output in 2020 can be attributed to the COVID-19 pandemic.