This dataset contains annual average CES data for California statewide and areas from 1990 to 2023.

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States. CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

The Current Employment Statistics (CES) program produces detailed industry estimates of employment, hours, and earnings of workers on nonfarm payrolls. CES State and Metro Area produces data for all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, and about 450 metropolitan areas and divisions. Each month, CES surveys approximately 142,000 businesses and government agencies, representing 689,000 individual worksites.

For more information and data, visit: https://www.bls.gov/sae/

Each month the Current Employment Statistics (CES) program surveys about 141,000 businesses and government agencies, representing approximately 486,000 individual worksites, in order to provide detailed industry data on employment, hours, and earnings of workers on nonfarm payrolls. More information and details about the data provided can be found at http://www.bls.gov/ces

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is 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.

The Current Employment Statistics (CES) program provides estimates of employment, hours, and earnings information on a national basis and in considerable industry detail. The Bureau of Labor Statistics collects payroll data each month from a sample of business and government establishments in all nonfarm activities.

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.

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is 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.

The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

VITAL SIGNS INDICATOR Jobs (LU2)

FULL MEASURE NAME Employment estimates by place of work

LAST UPDATED March 2020

DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/

U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/

U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/

Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017.

The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

Monthly update of data from BLS LAUS data.

The concepts and definitions underlying LAUS data come from the Current Population Survey (CPS), the household survey that is the source of the national unemployment rate. State monthly model-based estimates are controlled in "real time" to sum to national monthly employment and unemployment estimates from the CPS. These models combine current and historical data from the CPS, the Current Employment Statistics (CES) survey, and state unemployment insurance (UI) systems. Estimates for seven large areas and their respective balances of state also are model-based. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is 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.

Gross metro product per job (2013 inflation-adjusted dollars)

Graph and download economic data for Personal Taxes: State and Local Income Taxes by Occupation: All Other, Including Not Reporting (CXUSTATETAXLB1210M) from 1984 to 2023 about state & local, occupation, tax, government, personal, income, and USA.

VITAL SIGNS INDICATOR

Jobs (LU2)

FULL MEASURE NAME

Employment estimates by place of work

LAST UPDATED

October 2022

DESCRIPTION

Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE

Bureau of Labor Statistics, Quarterly Census of Employment and Wages - https://www.bls.gov/cew/downloadable-data-files.htm

1990-2021

U.S. Census Bureau: LODES Data - http://lehd.ces.census.gov/

Longitudinal Employer-Household Dynamics Program

2002-2018

METHODOLOGY NOTES (across all datasets for this indicator)

Quarterly Census of Employment and Wages (QCEW) monthly employment data represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of cities included in a sub-county is one for San Francisco and San Jose and more than one for all other sub-counties. Estimates for sub-county areas are the sums of Census block-level estimates from the U.S. Census Bureau: LEHD data.

The following incorporated cities and towns are included in each sub-county area:

North Alameda County: Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont

East Alameda County: Dublin, Livermore, Pleasanton

South Alameda County: Fremont, Hayward, Newark, San Leandro, Union City

Central Contra Costa County: Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek

East Contra Costa County: Antioch, Brentwood, Oakley, Pittsburg

West Contra Costa County: El Cerrito, Hercules, Pinole, Richmond, San Pablo

Marin County: Belvedere, Corte Madera, Fairfax, Larkspur, Mill Valley, Novato, Ross, San Anselmo, San Rafael, Sausalito, Tiburon

Napa County: American Canyon, Calistoga, Napa, St. Helena, Yountville

San Francisco County: San Francisco

North San Mateo County: Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco

Central San Mateo County: Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo

South San Mateo County: East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside, Atherton

North Santa Clara County: Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale

San Jose: San Jose

Southwest Santa Clara County: Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga

South Santa Clara County: Gilroy, Morgan Hill

East Solano County: Dixon, Fairfield, Rio Vista, Suisun City, Vacaville

South Solano County: Benicia, Vallejo

North Sonoma County: Cloverdale, Healdsburg, Windsor

South Sonoma County: Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

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.

Graph and download economic data for Personal Taxes: State and Local Income Taxes by Generation: Birth Year of 1927 or Earlier (CXUSTATETAXLB1606M) from 2016 to 2018 about birth, state & local, tax, personal, government, income, and USA.

VITAL SIGNS INDICATOR
Jobs (LU2)

FULL MEASURE NAME
Employment estimates by place of work

LAST UPDATED
October 2022

DESCRIPTION
Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021

U.S. Census Bureau: LODES Data - http://lehd.ces.census.gov/
Longitudinal Employer-Household Dynamics Program
2002-2018

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) monthly employment data represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of cities included in a sub-county is one for San Francisco and San Jose and more than one for all other sub-counties. Estimates for sub-county areas are the sums of Census block-level estimates from the U.S. Census Bureau: LEHD data.

The following incorporated cities and towns are included in each sub-county area:
- North Alameda County: Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont
- East Alameda County: Dublin, Livermore, Pleasanton
- South Alameda County: Fremont, Hayward, Newark, San Leandro, Union City
- Central Contra Costa County: Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek
- East Contra Costa County: Antioch, Brentwood, Oakley, Pittsburg
- West Contra Costa County: El Cerrito, Hercules, Pinole, Richmond, San Pablo
- Marin County: Belvedere, Corte Madera, Fairfax, Larkspur, Mill Valley, Novato, Ross, San Anselmo, San Rafael, Sausalito, Tiburon
- Napa County: American Canyon, Calistoga, Napa, St. Helena, Yountville
- San Francisco County: San Francisco
- North San Mateo County: Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco
- Central San Mateo County: Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo
- South San Mateo County: East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside, Atherton
- North Santa Clara County: Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale
- San Jose: San Jose
- Southwest Santa Clara County: Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga
- South Santa Clara County: Gilroy, Morgan Hill
- East Solano County: Dixon, Fairfield, Rio Vista, Suisun City, Vacaville
- South Solano County: Benicia, Vallejo
- North Sonoma County: Cloverdale, Healdsburg, Windsor
- South Sonoma County: Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

Graph and download economic data for Personal Taxes: State and Local Income Taxes by Generation: Birth Year from 1946 to 1964 (CXUSTATETAXLB1604M) from 2016 to 2023 about birth, state & local, tax, personal, government, income, and USA.

Data Build Appendix for “The Response of Consumer Spending to Changes in Gasoline Prices" By Michael Gelman, Yuriy Gorodnichenko, Shachar Kariv, Dmitri Koustas, Matthew D. Shapiro, Dan Silverman, and Steven Tadelis Overview We provide replication code to generate 4 figures and 6 tables in the paper. The raw (unaggregated) transactions data from the App cannot be disclosed or shared, so are not included in this repository. The deposited data includes aggregated data for replication purposes (see below). Raw data from the CEX are not included for practical purposes. The replicator should expect the CEX build to run for about 2 hours. Statement about Rights • I certify that the author(s) of the manuscript have legitimate access to and permission to use the data used in this manuscript. Summary of Availability • Some data cannot be made publicly available. Details on each Data Source Anonymous App Data This research is carried out in cooperation with a financial aggregation and bill-paying computer and smartphone application (the “app”). The raw (unaggregated) transactions data from the App cannot be disclosed or shared, so are not included in this repository. Consumer Expenditure Survey Our paper uses both the CEX Interview Survey and the CEX Diary Survey. In the directory “replication_files/Zsupplementary_data,” we provide our final builds of the CEX data from which our readers can replicate results reported in the paper. Our final build of the diary survey data is named, “diarybuild.dta” and our final build of the interview survey data is named, “CGKS_Expenditures_updated.dta.” We do not provide the raw files due to their size, however interested users can replicate our final build after downloading the raw files. We obtained the raw files from the following sources: 1980-1981 Interview Survey *.txt files from NBER http://data.nber.org/ces/1980-1981/ 1980-1981 Diary Survey (ICPSR 8235): https://doi.org/10.3886/ICPSR08235.v2 1982-1989 *.txt files from ICPSR: 1982-1983 Diary Survey (ICPSR 8599): https://doi.org/10.3886/ICPSR08599.v1 1982-1983 Interview Survey (ICPSR 8598): https://doi.org/10.3886/ICPSR08598.v1 1984 Diary Survey (ICPSR 8628): https://doi.org/10.3886/ICPSR08628.v1 1984 Interview Survey (ICPSR 8671): https://doi.org/10.3886/ICPSR08671.v2 1985 Diary Survey (ICPSR 8905): https://doi.org/10.3886/ICPSR08905.v1 1985 Interview Survey (ICPSR 8904): https://doi.org/10.3886/ICPSR08904.v2 1986 Diary Survey (ICPSR 9114): https://doi.org/10.3886/ICPSR09114.v1 1986 Interview Survey (ICPSR 9113): https://doi.org/10.3886/ICPSR09113.v2 1987 Diary Survey (ICPSR 9333): https://doi.org/10.3886/ICPSR09333.v1 1987 Interview Survey (ICPSR 9332): https://doi.org/10.3886/ICPSR09332.v2 1988 Diary Survey (ICPSR 9570): https://doi.org/10.3886/ICPSR09570.v1 1988 Interview Survey (ICPSR 9451): https://doi.org/10.3886/ICPSR09451.v2 1989 Diary Survey (ICPSR 9714): https://doi.org/10.3886/ICPSR09714.v1 1989 Interview Survey (ICPSR 9712): https://doi.org/10.3886/ICPSR09712.v1 1990-1995 *.txt files from NBER http://data.nber.org/ces/ 1996-2014 *.dta files from NBER http://data.nber.org/ces/ 2015 *.dta files from BLS https://www.bls.gov/cex/pumd_data.htm#stata For the NBER files, we store the data locally with the exact file structure at http://data.nber.org/ces/. For the ICPSR files, we download all files and preserve the original file names. We organize files in folders with the following file structure: CEX_[YEAR]/Diary and CEX_[YEAR]/Interview, for Diary and Interview data, respectively. Our build of the raw CEX Interview Survey follows Coibion, Gorodnichenko, Kueng, and Silvia (CGKS) (2012). We update the CGKS build through 2015. The CGKS build performs the following steps: sums expenditures that occur in the same month as recommended by the BLS, drops 4th and higher observations per interview, drops household with zero food expenditures