This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

The Title and Salary Listing is a compilation of job titles under the jurisdiction of the Department of Civil Service.

VITAL SIGNS INDICATOR

Jobs by Industry (EC1)

FULL MEASURE NAME

Employment by place of work by industry sector

LAST UPDATED

December 2022

DESCRIPTION

Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE

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

1990-2021

CONTACT INFORMATION

vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)

Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and 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.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:

(aggregation level: 74, NAICS code: 11)

Contra Costa: 2008-2010

Marin: 1990-2006, 2008-2010, 2014-2020

Napa: 1990-2004, 2013-2021

San Francisco: 2019-2020

San Mateo: 2013

Information:

(aggregation level: 73, NAICS code: 51)

Financial Activities:

(aggregatio

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.

Louisville Metro Human Resources (HR) Department, with the support of department leaders and their agency HR Representatives, provides support to all metro agencies in personnel management, benefits administration, labor relations, training, recruitment, occupational health & safety, payroll administration and onboarding. This data is presented in a publicly available report, located HERE Please review the FAQ located at https://louisvilleky.gov/government/human-resources/salary-data-faq Field Name Field Description Cal Year Calendar year the row data is referencing Employee Name Full employee name (LAST, FIRST MIDDLE) Department Metro department of employee during the referenced calendar year job Title Job title of employee during the referenced calendar year Annual Rate Yearly compensation rate of employee (Hourly Rate X Annual Work Hours) Regular Rate Gross payments received in the referenced calendar year for hours worked Overtime Rate Amount of overtime payment(s) within the calendar year referenced Incentive Allowance Additional payments for work allowances (i.e.: uniform allowances, call out payments etc.) Other Any additional payments issued to employee not captured in the other categories YTD Total Year To Date (YTD) compensation paid

The County of Suffolk Annual Salaries File for the Year 2023 is a yearly summary of Payroll Data for all Suffolk County employees. This file contains the Employee Names and Hired Date along with their most recent Job Title and Department. In addition, the file contains the Employee’s Regular Pay Rate (Hourly, Biweekly or Annual Salary), the Year to Date Regular Earnings, Longevity Pay, Overtime Pay, and Other Payments (comprised of Holiday Pay, Night Differential Pay, Cleaning and Clothing Allowances, Taxable Legal Benefit Amounts, etc.). If an employee has been terminated or has separated from County employment, the Separation Payment Amount (if applicable), and Termination Date is also included.

Abstract

A. Objectives

To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data

Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered

Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National coverage, 17 administrative egions

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 20 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2004 BLES Survey Sampling Frame (SSF2004) is a list frame of establishments that is a partial update of the 2003 BLES Sampling Frame based on the status of establishments reported in the 2003 BLES Integrated Survey (BITS) conducted nationwide.

Reports on closures and retrenchments of establishments submitted to the Regional Offices of the Department of Labor and Employment in December 2003 and January 2004 were also considered in updating the 2004 frame.

Sampling design: The OWS is a complete enumeration of non-agricultural establishments employing 50 persons or more. The design does not consider the region as a domain to allow for more industry coverage.

Sample size: For 2004 OWS, number of establishments covered was 8,779 of which, 6,827 were eligible units.

Note: Refer to Field Operations Manual Chapter 1 Section 1.5.

Sampling deviation

While the OWS is a complete enumeration survey, not all of the fielded questionnaires are accomplished. Due to the inadequacy of the frame used, there are reports of permanent closures, nonlocation, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements of the frame and their count is not considered in the estimation. In addition to non-response of establishments because of refusals, strikes or temporary closures, there are establishments whose questionnaires contain inconsistent item responses that are not included in the processing as these have not replied to the verification queries by the time output table generation commences. Such establishments are also considered as non-respondents.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by BLES and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2006 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time Rate Workers on Full Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time Rate Workers on Full Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2006 OWS; results of the 2004 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the BLES and DOLE Regional Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in determining the correct occupational sheet that should be furnished to the respondent.

Results of the 2004 OWS (Page 12) The results of the 2004 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Note: Refer to questionnaire and List of Monitored Occupations.

Cleaning operations

Data are manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the field operations manual. The forms are again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertake the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries are returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 82.1%.

Sampling error estimates

Estimates of the sampling errors are not computed.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.

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.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

** The data set is no longer being updated.The Office of Performance Improvement strives to provide Metro Government and its partners with customized improvement support to create a world-class city.Data Dictionary:DEPT - The name of the department where employees were absentBIWEEKLY_PAY_PERIOD_END_DATE - The end date for the pay period when employees were absentPARETO_TYPE - The type of absence (vacation and holiday time is excluded)TOTAL_HOURS - The total amount of absent hours for the given type of absence, department, and pay periodTOTAL EMPLOYEE - The total number of employees that worked in the given department and pay periodTOTAL AVAL HOURS - The total number of hours scheduled for work for all employees in the given department and pay periodLOST WORKTIME RATE - The total amount of absent hours for the given type of absence, department, and pay period divided by The total number of hours scheduled for work for all employees in the given department and pay period (metric defined by the Bureau of Labor Statistics)

Data Description: This dataset lists all current City of Cincinnati employees, including full names, department, position title, full-time employee status, employee age range, employee race, and annual salary rate.

Data Creation: This data is pulled directly from the City's HR software; which centralizes all department HR actions city wide.

Data Created By: City Human Resource Information System (CHRIS)

Refresh Frequency: Daily

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/Employee-Profile/wjqv-hgc9/

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

VITAL SIGNS INDICATOR
Jobs by Industry (EC1)

FULL MEASURE NAME
Employment by place of work by industry sector

LAST UPDATED
December 2022

DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

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

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and 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.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:
(aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

Information:
(aggregation level: 73, NAICS code: 51) - Solano: 2001

Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

Unclassified:
(aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

VITAL SIGNS INDICATOR
Jobs by Industry (EC1)

FULL MEASURE NAME
Employment by place of work by industry sector

LAST UPDATED
December 2022

DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

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

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and 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.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:
(aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

Information:
(aggregation level: 73, NAICS code: 51) - Solano: 2001

Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

Unclassified:
(aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

Release Date: 2015-12-15.[NOTE: Includes firms with payroll at any time during 2012. Employment reflects the number of paid employees during the March 12 pay period. Data are based on the 2012 Economic Census, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2012 Survey of Business Owners. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for All U.S. Firms With Paid Employees by Industry, Veteran Status, and Employment Size of Firm for the U.S. and States: 2012. ..Release Schedule. . This file was released in December 2015. Included are statistics for:. . Veteran-Owned Firms (VET). Company Summary (CS)-- Includes estimates for minority- and nonminority-owned firms. . ..Key Table Information. . This data supersedes all preliminary results released on August 18, 2015, and is related to all other 2012 SBO files.. Refer to the Methodology section of the Survey of Business Owners website for additional information.. ..Universe. . The universe for the 2012 Survey of Business Owners (SBO) includes all U.S. firms operating during 2012 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. ..Geographic Coverage. . The data are shown for the United States at the national and state levels.. ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code levels.. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms With Paid Employees by Industry, Veteran Status, and Employment Size of Firm for the U.S. and States: 2012 contains data on:. . Numbers of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. . The data are shown for:. . All firms classifiable by gender, ethnicity, race, and veteran status. . Veteran Status. . Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. . . Employment size of firm during the March 12 pay period for firms with paid employees at any time during 2012. . All firms. Firms with no employees. Firms with 1 to 4 employees. Firms with 5 to 9 employees. Firms with 10 to 19 employees. Firms with 20 to 49 employees. Firms with 50 to 99 employees. Firms with 100 to 499 employees. Firms with 500 employees or more. . . . . Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Veteran Status (VET_GROUP). Employment size of firm (EMPSZFI). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSA12 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSA12.zip. ..Contact Information. . To contact the Survey of Business Owners staff:. . Visit the website at www.census.gov/programs-surveys/sbo.html.. Email general, nonsecure, and unencrypted messages to ewd.survey.of.business.owners@census.gov.. Call 301.763.3316 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Survey of Business Owners. 4600 Silver Hill Road. Washington, DC 20233. . . ...Source: U.S. Census Bureau, 2012 Survey of Business Owners.Note: The data in this file are based on the 2012 Economic Census, Survey of Business Owners (SBO). To maintain confidentiality, the Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling errors. Data users who c...

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

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