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

Unemployment Rate in Los Angeles County, CA was 5.40% in May 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 August of 2025.

Graph and download economic data for Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) (LOSA106URN) from Jan 1990 to Jun 2025 about Los Angeles, CA, unemployment, rate, and USA.

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

This dataset contains unemployment rates for the U.S. (1948 - Present) and California (1976 - Present). The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces. This rate is also defined as the U-3 measure of labor underutilization.

As of February 2023, there were ******* unemployed people in the Los Angeles metro area. Within the provided time period, the peak for unemployment was during January 2021, when there were ******* people who were unemployed.

Unemployment Rate in Los Angeles-Long Beach-Anaheim, CA (MSA) was 5.30% in May of 2025, 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 July of 2025.

Graph and download economic data for Unemployment Rate in Orange County, CA (LAUCN060590000000003A) from 1990 to 2024 about Orange County, CA; Los Angeles; CA; unemployment; rate; and USA.

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.

Graph and download economic data for Unemployment Rate in Orange County, CA (CAORAN7URN) from Jan 1990 to Jun 2025 about Orange County, CA; Los Angeles; CA; unemployment; rate; and USA.

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

Graph and download economic data for Unemployed Persons in Los Angeles County, CA (LAUCN060370000000004) from Jan 1990 to Jun 2025 about Los Angeles County, CA; Los Angeles; persons; CA; household survey; unemployment; and USA.

Graph and download economic data for Unemployed Persons in Orange County, CA (LAUCN060590000000004A) from 1990 to 2024 about Orange County, CA; Los Angeles; CA; household survey; unemployment; persons; and USA.

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

Graph and download economic data for Total Unemployed, Plus All Marginally Attached Workers, Plus Total Employed Part Time for Economic Reasons, as a Percent of the Civilian Labor Force Plus All Marginally Attached Workers for California (U6UNEM6CA) from Q4 2003 to Q3 2024 about marginally attached, part-time, labor underutilization, workers, civilian, labor force, 16 years +, labor, CA, household survey, unemployment, rate, and USA.

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.

Graph and download economic data for Unemployed Persons in Los Angeles-Long Beach-Anaheim, CA (MSA) (LAUMT063108000000004) from Jan 1990 to Jun 2025 about Los Angeles, persons, CA, household survey, unemployment, and USA.

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.

These data were prepared in conjunction with a project using Bureau of Labor Statistics data (not provided with this collection) and the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program data to examine the relationship between unemployment and violent crime. Three separate time-series data files were created as part of this project: a national time series (Part 1), a state time series (Part 2), and a time series of data for 12 selected cities: Baltimore, Buffalo, Chicago, Columbus, Detroit, Houston, Los Angeles, Newark, New York City, Paterson (New Jersey), and Philadelphia (Part 3). Each data file was constructed to include 82 monthly time series: 26 series containing the number of Part I (crime index) offenses known to police (excluding arson) by weapon used, 26 series of the number of offenses cleared by arrest or other exceptional means by weapon used in the offense, 26 series of the number of offenses cleared by arrest or other exceptional means for persons under 18 years of age by weapon used in the offense, a population estimate series, and three date indicator series. For the national and state data, agencies from the 50 states and Washington, DC, were included in the aggregated data file if they reported at least one month of information during the year. In addition, agencies that did not report their own data (and thus had no monthly observations on crime or arrests) were included to make the aggregated population estimate as close to Census estimates as possible. For the city time series, law enforcement agencies with jurisdiction over the 12 central cities were identified and the monthly data were extracted from each UCR annual file for each of the 12 agencies. The national time-series file contains 82 time series, the state file contains 4,083 time series, and the city file contains 963 time series, each with 228 monthly observations per time series. The unit of analysis is the month of observation. Monthly crime and clearance totals are provided for homicide, negligent manslaughter, total rape, forcible rape, attempted forcible rape, total robbery, firearm robbery, knife/cutting instrument robbery, other dangerous weapon robbery, strong-arm robbery, total assault, firearm assault, knife/cutting instrument assault, other dangerous weapon assault, simple nonaggravated assault, assaults with hands/fists/feet, total burglary, burglary with forcible entry, unlawful entry-no force, attempted forcible entry, larceny-theft, motor vehicle theft, auto theft, truck and bus theft, other vehicle theft, and grand total of all actual offenses.