Data from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset from the Bureau of Labor Statistics's State and Metro Area Employment, Hours, & Earnings data contains non-farm, non-seasonally adjusted employment data for Salinas from January 1999 to August 2024.This dataset is part of the Bureau of Labor Statistic's Current Employment Statistics (CES) program. The CES program is a monthly survey conducted by the Bureau of Labor Statistics. It is a federal and state cooperative program that provides employment, hours, and earnings estimates for states and metropolitan areas based on payroll records of business establishments.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: May 2025 (preliminary values at the county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: July 18th, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and County NationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova. As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

This RESTful API provides Australian Bureau of Statistics (ABS) labour force data such as employment statistics by region, sex, age groups, and labour utilisation using original, seasonally adjusted and trend markers since 1978.\r \r It connects to an existing ABS API and improves the usability of the information queried from ABS by transforming the SDMX formatted data into a JSON format. This allows developers to consume ABS data easily by using a standard format without requiring time-consuming reformatting and transformation of the data received.\r \r Version 1.0.0\r \r An API key will be issued if you wish to explore and understand the way this API operates.\r \r Access for this API is available via request through developer.vic.gov.au.

VITAL SIGNS INDICATOR Income (EC4)

FULL MEASURE NAME Household income by place of residence

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B19013 (2006-2017; place of residence) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

Historical resident Labor Force and Employment, not seasonally adjusted

Index of Washington state and labor market areas, 1990-2022

Source: Employment Security Department/DATA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

These datasets contain information on child labor and forced labor worldwide from ILAB’s three flagship reports: Findings on the Worst Forms of Child Labor; List of Goods Produced by Child Labor or Forced Labor; and List of Products Produced by Forced or Indentured Child Labor. There are 14 tables containing data from the 2015-2019 reporting cycles and 11 tables from the 2014 reporting cycle. ILAB plans to update the structure of the API. This information is also available in ILAB’s app, Sweat & Toil: Child Labor, Forced Labor, and Human Trafficking Around the World. For more information, see ILAB’s International Child Labor and Forced Labor Reports page. https://www.dol.gov/agencies/ilab/resources/reports/child-labor/findings https://developer.dol.gov/others/sweat-and-toil/

Washington State, metropolitan statistical areas (MSA) and nonmetropolitan areas (NMA), 2020

OEWS is a program of the U.S. Department of Labor, Bureau of Labor Statistics (BLS). This federal-state cooperative program produces employment and wage estimates for nearly 867 occupations. The occupational employment and wage estimates are based on data collected from the OEWS survey. The survey includes employment counts, occupations and wages from more than 4,200 Washington state employers. Data from six survey panels are combined to create a sample size of more than 26,400 employers. Blanks in the data columns indicate suppressed data.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

VITAL SIGNS INDICATOR Income (EC5)

FULL MEASURE NAME Worker income by workplace (earnings)

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B08521 (2006-2017; place of employment) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION Vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

The ATUS-CPS dataset contains information about each household member of all individuals selected to participate in ATUS. The information on the ATUS-CPS dataset was collected 2 to 5 months before the ATUS interview.

For the data dictionary and survey methodology, visit: http://www.bls.gov/tus/atusintcodebk14.pdf

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset is derived from the Quarterly Census of Employment & Wages (QCEW) from the U.S. Bureau of Labor Statistics for Fulton County, the Atlanta MSA and the U.S. The dataset includes QCEW data aggregated down to the NAICS Sector (e.g. NAICS 23 Construction, NAICS 31-33 Manufacturing). Information on each quarter, geographic area and industry include total employment, total number of establishments and average wages.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

VITAL SIGNS INDICATOR Income (EC4)

FULL MEASURE NAME Household income by place of residence

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B19013 (2006-2017; place of residence) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

VITAL SIGNS INDICATOR Rent Payments (EC8)

FULL MEASURE NAME Median rent payment

LAST UPDATED August 2019

DESCRIPTION Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE U.S. Census Bureau: Decennial Census 1970-2000 https://nhgis.org Note: Count 1 and Count 2; Form STF1; Form SF3a

U.S. Census Bureau: American Community Survey 2005-2017 http://api.census.gov Note: Form B25058; 1-YR

Bureau of Labor Statistics: Consumer Price Index 1970-2017 http://www.bls.gov/data/ Note: All Urban Consumers Data Table (by metro)

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Rent data reflects median rent payments rather than list rents (refer to measure definition above). Larger geographies (metro and county) rely upon ACS 1-year data, while smaller geographies rely upon ACS 5-year rolling average data. 1970 Census data for median rent payments has been imputed by ABAG staff as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B24021 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This indicator includes the estimated total lost wages related to opioid use and the associated lost tax revenue for the state. Estimated lost wages are due to absenteeism, reduced productivity, hospitalization, unemployment and labor force exits due to opioid use. This value is the product of the estimated number of individuals with drug use disorder, the average weekly wages for county of residence—as reported by the Bureau of Labor Statistics—multiplied by 52 to represent annual wages, and a 17% reduction in productivity.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

These datasets match information on child labor and forced labor worldwide from ILAB’s three flagship reports (Findings on the Worst Forms of Child Labor; List of Goods Produced by Child Labor or Forced Labor; and List of Products Produced by Forced or Indentured Child Labor) with U.S. import trade data, including Harmonized Tariff Schedule Codes, to empower users to advance efforts in supply chain transparency as well as strategic sourcing priorities. There are 3 tables combining data from ILAB’s essential reporting with U.S. import trade data.

This dataset contains the consumer price index (CPI) over time for all consumer items for the Atlanta Metropolitan Statistical Area (MSA) and for the largest U.S. metro areas combined. The 2-month change and year-over-year change in CPI is also included.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset contains the consumer price index (CPI) over time for the U.S. and various geographic areas. The CPI is given for all goods and services combined as well for individual classes such as energy, housing, transportation and food. The 2-month change and year-over-year change in CPI is also included.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)