This table contains data on the percent of the population in the labor force who are unemployed (unemployment rate), for California, its regions, counties, county divisions, cities/towns, and census tracts. Data is from the Local Area Unemployment Statistics (LAUS), Bureau of Labor Statistics (BLS), and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Unemployment is associated with higher rates of self-reported poor health, long-term illnesses, higher incidence of risky health behaviors (alcoholism, smoking), and increased mortality. Various explanations have been proposed for the link between poor health and unemployment; for example, economic deprivation that results in reduced access to essential goods and services. Another explanation is that unemployment causes the loss of latent functions (social contact, social status, time structure and personal identity) which can result in stigma, isolation and loss of self-worth. More information about the data table and a data dictionary can be found in the About/Attachments section.

An area of employment is a geographical area within which most of the workers reside and work, and in which establishments can find the bulk of the labour force needed to fill the jobs offered. The division into employment areas is a partition of the territory adapted to local labour market studies. It serves as a reference for the dissemination of localised unemployment rates and job estimates.Zoning also defines territories relevant to local diagnostics and can guide the delimitation of territories for the implementation of territorial policies initiated by public authorities or local actors. This zoning is defined for both metropolitan France and the French overseas departments. The updated breakdown is based on the commuting flows of the observed workers. An area of employment is a geographical area within which most of the workers reside and work, and in which establishments can find the bulk of the labour force needed to fill the jobs offered. The division into employment areas is a partition of the territory adapted to local labour market studies. It serves as a reference for the dissemination of localised unemployment rates and job estimates. Zoning also defines territories relevant to local diagnostics and can guide the delimitation of territories for the implementation of territorial policies initiated by public authorities or local actors. This zoning is defined for both metropolitan France and the French overseas departments.

The updated breakdown is based on the commuting flows of the observed workers.

Quarterly Employment and Wage Data (Timeseries)

Detailed Insights into Local Industries

By State of New York [source]

About this dataset

The Quarterly Census of Employment and Wages (QCEW) program is the most comprehensive labor market data source out there, collecting vital information on employment and wage trends across New York State. It provides a virtual census of 97 percent of the state's nonfarm employees and employers who are covered by the Unemployment Insurance (UI) Law, making it incredibly precise in measuring total wages, establishments, unemployment insurance reports, as well as crucial geographical labor information by state region and county.

At its core, this program seeks to give users a precise quantitative view comparative data that takes into account differences in employee coverage regulatory policy across bureaus or federal laws. All this while taking into consideration factors like agricultural workers, private households employments students or unpaid family workers that are excluded from UI considerations but still count towards Current Employment Statistics totals. This dataset offers an eye-opening look into employment dynamics in New York State; one that you won't find anywhere else! Before using any found data though make sure to review and read through the Terms of Service license requirements first!

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How to use the dataset

The New York Quarterly Employment and Wage Data is an invaluable resource for researchers, students, professionals and policymakers. The data offers a wealth of information on employment status and wages in New York State across all industries. This guide will provide you with a step-by-step introduction to using this dataset.

Research Ideas

• Analyzing small business trends over time to understand hiring trends in the localization and industry level.
• Creating predictive models to forecast future employment levels and wage demands for New York State's departments, businesses, and regions in the upcoming quarters or fiscal years.
• Tracking changes in average wages and employment by industry, region or area type over time to identify potential labor shortages or job losses due to automation or other factors that could lead to policy recommendations at a state level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: quarterly-census-of-employment-and-wages-quarterly-data-beginning-2000-1.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------| | Area Type | The type of area the data is for (String) | | Year | The year the data is for (Integer) | | Quarter | The quarter the data is for (Integer) | | NAICS | The North American Industry Classification System code (Integer) | | NAICS Title | The title of the NAICS code (String) | | Establishments | The number of establishments in the area (Integer) | | Month 1 Employment | The number of employees in the first month (Integer) | | Month 2 Employment | The number of employees in the second month (Integer) | | Month 3 Employment | The number of employees in the third month (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

Regional unemployment rates used by the Employment Insurance program, by effective date, current month.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by immigrant status and age group, last 5 years.

This app 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: March 2021 (preliminary values at the state and 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. Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData 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 2019 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.To better understand the different labor force statistics included in this map, see the diagram below from BLS:Esri's U.S. Updated Demographic Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Data Note: The median household income value divides the distribution of household income into two equal parts. Pareto interpolation is used if the median falls in an income interval other than the first or last. For the lowest interval, <$10,000, linear interpolation is used. If the median falls in the upper income interval of $500,000+, it is represented by the value of $500,001. Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

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.

This is an extensive record of the unemployment status of the Nevada labor force from 1976 to 2023, which includes detailed information on seasonal adjustments, total employed individuals, unemployed individuals, and the resulting unemployment rate. The record provides a comprehensive understanding of the employment trends in Nevada over the years, highlighting fluctuations in the labor market and significant events that have impacted employment rates. This information can be used by economists, policymakers, and businesses to make informed decisions and develop strategies to improve job creation and the overall economic health of Nevada.

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels. Disclaimer: For information regarding future updates or preliminary/final data releases, please refer to the Bureau of Labor Statistics Release Calendar: https://www.bls.gov/cew/release-calendar.htm

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by province, territory and economic region, last 5 years.

The Local Area Unemployment Statistics (LAUS) program produces monthly employment, unemployment, and labor force data for Census regions and divisions, States, counties, metropolitan areas, and many cities, by place of residence. The LAUS program 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). A major source of labor force data estimates, the Current Population Survey (CPS) includes a sample of over 1,600 Connecticut households each month regarding the labor force status of their occupants

Further information from the CT Department of Labor is available here: https://www1.ctdol.state.ct.us/lmi/LAUS/default.asp

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.

2004 to 2021 Virginia Employment Status of the Civilian Non-Institutional Population by Sex, by Race, Hispanic or Latino ethnicity, and detailed by Age, by Year. Annual averages, numbers in thousands.

U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, Expanded State Employment Status Demographic Data Data accessed from the Bureau of Labor Statistics website (https://www.bls.gov/lau/ex14tables.htm)

Statewide data on the demographic and economic characteristics of the labor force are published on an annual-average basis from the Current Population Survey (CPS), the sample survey of households used to calculate the U.S. unemployment rate (https://www.bls.gov/cps/home.htm). For each state and the District of Columbia, employment status data are tabulated for 67 sex, race, Hispanic or Latino ethnicity, marital status, and detailed age categories and evaluated against a minimum base, calculated to reflect an expected maximum coefficient of variation (CV) of 50 percent, to determine reliability for publication.

The CPS sample was redesigned in 2014–15 to reflect the distribution of the population as of the 2010 Census. At the same time, BLS developed improved techniques for calculating minimum bases. These changes resulted in generally higher minimum bases of unemployment, leading to the publication of fewer state-demographic groups beginning in 2015. The most notable impact was on the detailed age categories, particularly the teenage and age 65 and older groups. In an effort to extend coverage, BLS introduced a version of the expanded state employment status demographic table with intermediate age categories, collapsing the seven categories historically included down to three. Ages 16–19 and 20–24 were combined into a 16–24 year-old category, ages 25–34, 35–44, and 45–54 were combined into a 25–54 year-old category, and ages 55–64 and 65 and older were combined into a 55-years-and-older category. These intermediate age data are tabulated for the total population, as well as the four race and ethnicity groups, and then are evaluated against the unemployment minimum bases. The more detailed age categories continue to be available in the main version of the expanded table, where the minimum base was met.

Additional information on the uses and limitations of statewide data from the CPS can be found in the document Notes on Using Current Population Survey (https://www.bls.gov/lau/notescps.htm) Subnational Data and in Appendix B of the bulletin Geographic Profile of Employment and Unemployment (https://www.bls.gov/opub/geographic-profile/home.htm).

This dataset uses Claimant Count to monitor unemployment in Leicester and Upper-Tier-Local-Authority (UTLA) comparators as defined by the ONS as well as UTLAs in the East Midlands, and England core cities.Claimant Count is the number of people claiming Universal Credit or Jobseekers' Allowance principally for the reason of being unemployed.Claimant Count is a useful proxy for unemployment because it is the most comprehensive unemployment-related dataset published at geographies smaller than the local authority level. While there is significant overlap, it is not the same as the national measure for unemployment, which is based on estimates from the Labour Force Survey and Annual Population Survey.Claimant Count is best used for understanding short term changes in the labour market and the relative position of small areas.Rates are calculated using ONS mid-year or census-based population estimates for the 16-64 year old population as a denominator.A dashboard has also been produced summarising this data into a single page. Click here to view: DashboardFurther information: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/methodologies/aguidetolabourmarketstatistics#introduction

Deduplicated aggregated job data by area and occupation with salary data. Data is available for USA and territories by FIPS area codes, ONET occupation code, NAIC industry code, education group, and experience group, with USD salary information by month, quarter, and year time frames. Data includes aggregations for both active jobs in given time frame and those first posted in given time frame.

1B+ job posts 42k+ job sources 5M+ unique employers 4.8M new jobs per month 18 years of data

At iQuery, we provide unparalleled insight into the labor market through our proprietary aggregated jobs data. By combining historical data with real-time job postings, our platform captures employment trends as they unfold—offering powerful predictive capabilities for workforce analysis, planning, and development.

Our specialized team of developers processes and refines 7 to 10 million job listings daily, collected from a wide array of online sources including public and private job boards, government portals, healthcare systems, and various other employment websites. We ensure data freshness by validating and de-duplicating postings and conducting daily checks to confirm their active status—making our datasets among the most accurate and current in the industry.

Our dedicated Data Services Team enhances the dataset by assigning standardized taxonomy codes for occupation (ONET), employer industry (NAICS), location (FIPS), education level, and experience requirements. We also offer crosswalk capabilities between Classification of Instructional Programs (CIP) codes and federal taxonomies, enabling education providers to align curricula with real-time and projected workforce needs. Our proprietary taxonomy extends to skills, tools, and certifications, further enriching each job posting for granular labor market analysis.

With a comprehensive blend of real-time and historical data, our platform supports economic development by tracking workforce dynamics across occupations, industries, and skill sets. Our in-house economist and analysts generate reliable labor market forecasts—including unemployment trends—often outperforming traditional forecasting models.

Our data empowers a wide range of stakeholders:

Governments can assess local labor supply to attract employers and inform policy. Researchers can uncover trends and model future workforce shifts. Employers can locate optimal labor pools for hiring needs. Colleges and universities can tailor programs and credentials to match employer demand in specific regions. iQuery delivers a data-rich foundation for workforce planning, policy development, and educational alignment—driving smarter decisions in a dynamic labor market.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by industry and by economic activity status. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Industry (current)

Classifies people aged 16 years and over who were in employment between 15 March and 21 March 2021 by the Standard Industrial Classification (SIC) code that represents their current industry or business.

The SIC code is assigned based on the information provided about a firm or organisation’s main activity.

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

Quarterly estimates for young people (aged 16 to 24 years) who are not in education, employment or training (NEET) in the UK. These are official statistics in development.

An area of employment is a geographical area within which most of the workers reside and work, and in which establishments can find the bulk of the labour force needed to fill the jobs offered. The division into employment areas is a partition of the territory adapted to local labour market studies. It serves as a reference for the dissemination of localised unemployment rates and job estimates. Zoning also defines territories relevant to local diagnostics and can guide the delimitation of territories for the implementation of territorial policies initiated by public authorities or local actors. This zoning is defined for both metropolitan France and the French overseas departments. The updated breakdown is based on the commuting flows of the observed workers.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by economic activity status. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and parcels nationally.

Over 60M parcels reflecting over 330M permits over the past 20 years.

This comprehensive dataset contains building permits issued in the United States, providing valuable insights into residential and commercial construction activities. With over millions of records covering millions of homes, this dataset offers a vast opportunity for analysis and business growth.

Includes permits from various states across the US

Covers residential and commercial construction activities

Insights:

Residential vs. Commercial: Analyze the distribution of permits by type (residential, commercial) to understand local market trends.

Construction Activity: Track permit issuance over time to identify patterns and fluctuations in construction activity.

Geographic Patterns: Examine the concentration of permits by state, county, or city to reveal regional development opportunities.

Potential Applications:

Contractors and Builders: Utilize this dataset to identify potential projects, estimate job values, and stay up-to-date on permit requirements.

Local Governments: Analyze building permit data to inform land-use planning, zoning regulations, and infrastructure development.

Investors and Developers: Explore the types of construction projects being undertaken in specific areas, enabling informed investment decisions.

Value Propositions:

Understand Current Home Condition: Gain insights into the current state of homes by analyzing building permit data, allowing you to:

Identify areas with high concentrations of permits

Determine the scope and type of work being performed

Infer the potential for improved home values

Lender Lead Generation: Use this dataset to identify potential refinance candidates based on improved homes, enabling lenders to:

Target homeowners who have invested in their properties

Offer tailored financial solutions to capitalize on increased property value

Contractor Lead Generation:

Solar installers can target neighbors of solar customers, increasing the chances of successful referrals and upselling opportunities.

Pool cleaners can target new pools, identifying potential customers for maintenance and cleaning services.

Roofing contractors can target homes with recent roofing permits, offering replacement or repair services to homeowners.

Home Service Providers:

Handyman services can target homes with permit records, offering a range of maintenance and repair services.

Appliance installers can target new kitchens and bathrooms, identifying potential customers for appliance installation and integration.

Real Estate Professionals:

Realtors can analyze permit data to understand local market trends, adjusting their sales strategies to capitalize on areas with high construction activity.

Property managers can identify potential investment opportunities, using permit data to evaluate the feasibility of investment projects.

Data Analysis Ideas:

Trend Analysis: Identify trends in permit issuance by type (residential, commercial), project size, or location to forecast future demand.

Geospatial Analysis: Visualize permit data on a map to analyze the concentration of construction activity and identify areas with high growth potential.

Correlation Analysis: Examine the relationship between permit issuance and local economic indicators (e.g., GDP, unemployment rates) to understand the impact of construction on the local economy.

Business Use Cases:

Market Research: Analyze permit data to inform business decisions about market trends, competition, and growth opportunities.

Risk Assessment: Identify areas with high concentrations of permits and potential risks (e.g., building code non-compliance) to adjust business strategies accordingly.

Investment Analysis: Use permit data to evaluate the feasibility of investment projects in specific regions or markets.

Data Visualization Ideas:

Interactive Maps: Create interactive maps to visualize permit concentration by location, type, and project size.

Permit Issuance Charts: Plot permit issuance over time to illustrate trends and fluctuations in construction activity.

Bar Charts by Category: Display the distribution of permits by category (e.g., residential, commercial) to highlight market trends.

Additional Ideas:

Combine with other datasets: Integrate building permit data with other sources (e.g., crime statistics, weather patterns) to gain a more comprehensive understanding of local conditions.

Analyze by demographic factors: Examine how permit issuance varies across different demographics (e.g., age, income level) to understand market preferences and behaviors.

Develop predictive models: Create statistical models to forecast future permit issuance based on historical trends and external factors.

Project and Permit...

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by economic activity status, by sex, and by age. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

Sex

This is the sex recorded by the person completing the census. The options were “Female” and “Male”.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.