VITAL SIGNS INDICATOR Jobs by Industry (EC1)
FULL MEASURE NAME Employment by place of work by industry sector
LAST UPDATED July 2019
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: Current Employment Statistics 1990-2017 http://data.bls.gov
CONTACT INFORMATION vitalsigns.info@bayareametro.gov
METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment by place of work and by industry. Industries are classified by their North American Industry Classification System (NAICS) code. Vital Signs aggregates employment into 11 industry sectors: Farm, Mining, Logging and Construction, Manufacturing, Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Educational and Health Services, Leisure and Hospitality, Government, and Other. EDD counts all public-sector jobs under Government, including public transportation, public schools, and public hospitals. The Other category includes service jobs such as auto repair and hair salons and organizations such as churches and social advocacy groups. Employment in the technology sector are classified under three categories: Professional and Business Services, Information, and Manufacturing. The latter category includes electronic and computer manufacturing. For further details of typical firms found in each sector, refer to the 2012 NAICS Manual (http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2012).
The Bureau of Labor Statistics (BLS) provides industry estimates for non-Bay Area metro areas. Their main industry employment estimates, the Current Employment Survey and Quarterly Census of Employment and Wages, do not provide annual estimates of farm employment. To be consistent, the metro comparison evaluates nonfarm employment for all metro areas, including the Bay Area. Industry shares are thus slightly different for the Bay Area between the historical trend and metro comparison sections.
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 the nation’s employment in that same sector. Because BLS does not provide national farm estimates, note that there is no LQ for regional farm employment. 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.
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
This dataset uses seasonally adjusted data from the US Bureau of Labor Statistics to present information on Maryland's labor force participation rate, employment rate, and unemployment rate.
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for All Employees, Government (USGOVT) from Jan 1939 to Jun 2025 about establishment survey, government, employment, and USA.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Employment by industry and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.
VITAL SIGNS INDICATOR Home Prices (EC7)
FULL MEASURE NAME Home Prices
LAST UPDATED August 2019
DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.
DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/
Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov
CONTACT INFORMATION vitalsigns.info@bayareametro.gov
METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.
For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/
Inflation-adjusted data are presented to illustrate how home prices 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 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)
Local authorities have a duty to track young people’s activity to identify those not participating and support them to do so.
The data shows the number and proportion of 16- and 17-year-olds recorded as in education or training in each local authority area and an estimate of the proportion and number of 16- and 17-year-olds who are recorded as NEET or whose activity is ‘not known’.
Until 2018 this information was made available for December, March and June quarters but following an internal review this was revised and from 2018 became an annual release for March only.
Caution should be taken when comparing historic figures due to a change in the methodology from 2016 where NEET and not known were reported as the headline statistic. Previously an adjustment was made to the NEET figure to try and estimate the proportion of not known activity that was likely to be a result of the young person being NEET (see notes that accompany each publication for further information on the methodological change).
Also prior to 2016 local authorities were required to also track 18 year olds but this requirement was lifted and only those aged 16 and 17 are included in the 2016 and 2017 figures.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Annual sickness absence rates of workers in the UK labour market, including number of work days lost, by country and region, sex and age group, and employment type. These are official statistics in development.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
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.
This data release describes:
It also looks specifically at the jobs deemed most important across 5 critical technologies:
Alongside this release, a jobs and skills dashboard has been developed. This allows further analysis of the data, presents the data in an easy to navigate format and provides further data on skills shortages.
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VITAL SIGNS INDICATOR Jobs by Industry (EC1)
FULL MEASURE NAME Employment by place of work by industry sector
LAST UPDATED July 2019
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: Current Employment Statistics 1990-2017 http://data.bls.gov
CONTACT INFORMATION vitalsigns.info@bayareametro.gov
METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment by place of work and by industry. Industries are classified by their North American Industry Classification System (NAICS) code. Vital Signs aggregates employment into 11 industry sectors: Farm, Mining, Logging and Construction, Manufacturing, Trade, Transportation and Utilities, Information, Financial Activities, Professional and Business Services, Educational and Health Services, Leisure and Hospitality, Government, and Other. EDD counts all public-sector jobs under Government, including public transportation, public schools, and public hospitals. The Other category includes service jobs such as auto repair and hair salons and organizations such as churches and social advocacy groups. Employment in the technology sector are classified under three categories: Professional and Business Services, Information, and Manufacturing. The latter category includes electronic and computer manufacturing. For further details of typical firms found in each sector, refer to the 2012 NAICS Manual (http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2012).
The Bureau of Labor Statistics (BLS) provides industry estimates for non-Bay Area metro areas. Their main industry employment estimates, the Current Employment Survey and Quarterly Census of Employment and Wages, do not provide annual estimates of farm employment. To be consistent, the metro comparison evaluates nonfarm employment for all metro areas, including the Bay Area. Industry shares are thus slightly different for the Bay Area between the historical trend and metro comparison sections.
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 the nation’s employment in that same sector. Because BLS does not provide national farm estimates, note that there is no LQ for regional farm employment. 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.