The resources in this dataset contain demographic information for the Oklahoma state government workforce. The resources present data from the current fiscal year along with demographic trends over time. The data can be used for workforce planning purposes.

Between 2023 and 2027, the majority of companies worldwide expect ***** changes to their workforce strategies. Those surveyed stated that ** percent would invest in learning and training on the job. ** percent said that they would accelerate the automation of processes, and ** percent said they would reduce the current workforce significantly.

The Asia-Pacific region shows significant variations in labor force participation rates (LFPR) among the population aged 15 to 64 years, with North Korea’s LFPR estimated at ** percent and Afghanistan’s at about ** percent in 2024. This stark contrast highlights the diverse economic and social landscapes across the region, influencing workforce engagement. Regional trends and forecasts APAC’s rapidly aging population is putting growing pressure on the labor market, with projections showing a declining labor force participation rate across the region between 2023 and 2050. East Asia, where demographic changes are most pronounced, is expected to see a significant decline in LFPR among those aged 15 to 54 years, while participation among those over 54 years is projected to rise notably during this period. In contrast, South Asia is the only sub-region anticipated to record a modest increase in participation rates for the 25-54 years age group, highlighting a regional divergence in labor force trends Youth engagement in the labor force The labor force participation rates among youth populations vary greatly across Asia-Pacific countries, reflecting diverse economic conditions, education systems, and social factors. For example, North Korea and Australia boast high youth labor force participation rates of more than ** percent for those aged 15 to 24 years, while South Korea's rate for the same age group is considerably lower at around ** percent. In Australia, strong labor market access for students and abundant part-time work opportunities could enable high youth engagement alongside education. Meanwhile, South Korea's strong societal focus on academic achievement and the pursuit of higher education qualifications often leads to prolonged periods of education, which delays young people's entry into the workforce. Moreover, many APAC countries have high NEET (not in education, employment, or training) rates, particularly those in South Asia, underscoring challenges such as skills mismatches and limited job opportunities.

The report contains thirteen (13) performance metrics for City's workforce development programs. Each metric can be breakdown by three demographic types (gender, race/ethnicity, and age group) and the program target population (e.g., youth and young adults, NYCHA communities) as well.

This report is a key output of an integrated data system that collects, integrates, and generates disaggregated data by Mayor's Office for Economic Opportunity (NYC Opportunity). Currently, the report is generated by the integrated database incorporating data from 18 workforce development programs managed by 5 City agencies.

There has been no single "workforce development system" in the City of New York. Instead, many discrete public agencies directly manage or fund local partners to deliver a range of different services, sometimes tailored to specific populations. As a result, program data have historically been fragmented as well, making it challenging to develop insights based on a comprehensive picture. To overcome it, NYC Opportunity collects data from 5 City agencies and builds the integrated database, and it begins to build a complete picture of how participants move through the system onto a career pathway.

Each row represents a count of unique individuals for a specific performance metric, program target population, a specific demographic group, and a specific period. For example, if the Metric Value is 2000 with Clients Served (Metric Name), NYCHA Communities (Program Target Population), Asian (Subgroup), and 2019 (Period), you can say that "In 2019, 2,000 Asian individuals participated programs targeting NYCHA communities.

Please refer to the Workforce Data Portal for further data guidance (https://workforcedata.nyc.gov/en/data-guidance), and interactive visualizations for this report (https://workforcedata.nyc.gov/en/common-metrics).

The dataset contains estimates for the number of healthcare professionals in 15 different healthcare categories (e.g., Registered Nurse, Dentist, License Clinical Social Worker, etc.) based on completion of license renewal by Race/Ethnicity. There are two timeframes: all current licenses and recent licenses (since 2017). California population estimates are also included to provide a marker for each Race/Ethnicity. Each healthcare professional category can be compared across Race/Ethnicity groups and compared to statewide population estimates, so Race/Ethnicity shortages can be identified for each healthcare professional category. For instance, a notable difference between healthcare professional category and statewide population would indicate either underrepresentation or overrepresentation for that Race/Ethnicity, depending on the direction of the difference.

This data asset was created in response to House Report 117-401, which stated, "The Committee directs the USAID Administrator, in consultation with the Director of the Office of Personnel Management and the Director of the Office of Management and Budget, to submit a report to the appropriate congressional committees, not later than 180 days after enactment of this Act, on USAID's workforce data that includes disaggregated demographic data and other information regarding the diversity of the workforce of USAID. Such report shall include the following data to the maximum extent practicable and permissible by law: 1) demographic data of USAID workforce disaggregated by grade or grade-equivalent; 2) assessment of agency compliance with the Equal Employment Opportunity Commission Management Directive 715; and 3) data on the overall number of individuals who are part of the workforce, including all U.S. Direct Hires, personnel under personal services contracts, and Locally Employed staff at USAID. The report shall also be published on a publicly available website of USAID in a searchable database format." This data asset fulfills the final part of this requirement, to publish the data in a searchable database format. The data are compiled from USAID's 2021 MD-715 report, available at https://www.usaid.gov/who-we-are/organization/independent-offices/office-civil-rights/md-715-reports. The original data source is the system National Finance Center Insight owned by the Treasury Department. This dataset reports demographic data for the USAID workforce for fiscal year 2021.

Nigeria's labor force continues to grow, with over 75.5 million people estimated to be economically active in 2023. This marks a significant increase from the previous year's figure of 73.3 million. The country's workforce has been steadily expanding over the past decade, reflecting the nation's demographic changes and economic development. Urban concentration and gender distribution The labor force in Nigeria is predominantly concentrated in urban areas, with approximately 51.3 million workers in cities compared to 37.6 million in rural communities. Interestingly, the gender distribution of the workforce shows a slight advantage for women, with about 45.4 million female workers compared to 43.6 million male workers. This gender balance in the labor force suggests progress in women's participation in the Nigerian economy. Age demographics and education levels The Nigerian workforce is relatively young, with the largest group being those aged 25 to 34 years, comprising around 23 million people. The second-largest group consists of individuals aged 35 to 44 years, numbering nearly 20.4 million. Education levels vary among workers, with a significant portion having completed secondary school. However, unemployment rates differ based on educational attainment, often with vocational or commercial training graduates experiencing the lowest unemployment rates. Notably, the State of Abia faces the highest unemployment rate at nearly 19 percent, while Lagos state boasts the lowest at 5.5 percent, highlighting regional disparities in job opportunities across the country.

Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 25 to 54 Years for United States (LFWA25TTUSM647N) from Jan 1955 to Jun 2025 about 25 to 54 years, working-age, population, and USA.

Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by age group and gender. Data are presented for 12 months earlier, previous month and current month, as well as year-over-year and month-to-month level change and percentage change. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

Demographic trends in the BSSR workforce by gender, age, and citizenship.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

According to the U.S. Bureau of Labor Statistics, the labor force participation rate is an important measure of the health of the labor market, which represents the relative amount of labor resources available for the production of goods and services.

Changes in overall labor force participation reflect demographic, policy, and employer changes, whereas gaps in labor force participation between different segments of the working-age population reveal barriers to participation.

Black, Indigenous, and people of color participate in the labor market at lower rates than white people. These inequities reflect policies and practices, such as employment discrimination, racial segregation, and mass incarceration, among other factors.

The District's Response

Investing in targeted programs that provide pathways to higher wages and jobs, such as the Advanced Technical Centers (ATC), the DC Infrastructure Academy, and Career MAP, which aim to tackle the systemic barriers that keep people out of the labor force.

Administering federal and local safety net programs such as TANF For District Families, SNAP, unemployment insurance, and Medicaid that provide temporary cash and health benefits to address economic hardship.

Partners with the Department of Employment Services in building youth from the ground up through its various programs and services, including mentorship, counseling justice system services, job training development, and employment.

In 2023, China's labor force amounted to approximately 772.2 million people. The labor force in China indicated a general decreasing trend in recent years. As both the size of the population in working age and the share of the population participating in the labor market are declining, this downward trend will most likely persist in the foreseeable future. A country’s labor force is defined as the total number of employable people and incorporates both the employed and the unemployed population. Population challenges for China One of the reasons for the shrinking labor force is the Chinese one-child policy, which had been in effect for nearly 40 years, until it was revoked in 2016. The controversial policy was intended to improve people’s living standards and optimize resource distribution through controlling the size of China’s expanding population. Nonetheless, the policy also led to negative impacts on the labor market, pension system and other societal aspects. Today, China is becoming an aging society. The increase of elderly people and the lack of young people will become a big challenge for China in this century. Employment in China Despite the slowing down of economic growth, China’s unemployment rate has sustained a relatively low rate. Complete production chains and a well-educated labor force make China’s labor market one of the most attractive in the world. Working conditions and salaries in China have also improved significantly over the past years. Due to China’s leading position in terms of talent in the technology industry, the country is now attracting investment from some of the world’s leading companies in the high-tech sector.

This Indicator measures the percent of the unemployed population (ages 16 and up) in Oakland by race/ethnicity who did not participate in the City of Oakland’s Workforce Development program between 7/1/2016 and 6/30/2017. The percent that did participate for each race/ethnicity is calculated by dividing number of participants of that race/ethnicity by the number of unemployed people in the labor force in Oakland of that race/ethnicity. Percent that did not participate is 100% minus the percent that did participate. NOTE: Participation is not the most meaningful metric, but was the data available. In the future, we hope to replace this with a measurement of exit outcomes for participants by race/ethnicity (i.e., did participants successfully find jobs?).

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

This table contains quarterly and yearly figures on labour participation in the Netherlands. The population of 15 to 74 years of age (excluding the institutionalized population) is divided into the employed labour force, the unemployed labour force and those not in the labour force. The employed labour force is subdivided on the basis of the professional status, and the average working hours. A division by sex, age and level of education is available.

Data available from: 2013

Status of the figures: The figures in this table are final.

Changes as of July 30, 2025: The figures for the 2nd quarter 2025 have been added.

Changes as of November 14, 2024: The figures for 3rd quarter 2024 are added. Figures have been added on labor participation based on whether or not the state pension age has been reached.

Changes as of August 17, 2022: None, this is a new table. This table has been compiled on the basis of the Labor Force Survey (LFS). Due to changes in the research design and the questionnaire of the LFS, the figures for 2021 are not automatically comparable with the figures up to and including 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Arbeidsdeelname, kerncijfers seizoengecorrigeerd (see section 4), in which the outcomes for the period 2013-2020 have been recalculated to align with the outcomes from 2021. When further detailing the outcomes according to job and personal characteristics, there may nevertheless be differences from 2020 to 2021 as a result of the new method.

When will new figures be released? New figures will be published in October 2025.

Abstract

The survey concentrates on the situation from the point of view of the economic activity of the population, i.e. the fact of being employed, unemployed or economically inactive in the reference week.

The primary objective of the LFS is to obtain data on the size and structure of the labor force. The results of the study will be used primarily to: - Determining the balance of the workforce from three basic categories of the population: employed, unemployed and inactive, - Analysis of changes in economic activity in different groups of socio-professional and cross-territorial - Analysis of the situation on the labor market, including the assessment of the scale and extent of changes in the unemployment rate in spatial terms, - Socio-demographic characteristics of the unemployed, - Analysis of the structure of employment by socio-demographic characteristics and professional.

Geographic coverage

National coverage

Analysis unit

• Individual aged 15 and over

Universe

The LFS covers persons aged 15 and over who are the members of sampled households. The survey does not cover the members of households who stayed abroad above 2 months. Neither does it cover the population living in the collective households, such as lodging-houses for employees, student hostels, boarding-schools, army barracks, houses for the poor and the old, etc.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Labour Force Survey is the probability sample survey. Its results are generalized on the general population.

Since the fourth quarter of 1999, the observation of the reference week in the middle month of a quarter has been replaced by the continuous observation method, which means that in each of the 13 weeks of a quarter 1/13th part of the quarterly sample of dwellings is surveyed. It allows presenting the situation on the labour market during a whole quarter.

The selection of quarterly samples follows principles of the so-called rotation scheme according to which there are scheduled for the interview in a given quarter: two elementary samples2 surveyed in the previous quarter, one elementary sample introduced into the survey for the first time and one elementary sample that was not surveyed in the previous quarter and had been introduced into the survey exactly one year before. As the result of this rotation system each elementary sample is used according to the 2-(2)-2 principle: two quarters in the survey, two quarters break, again two quarters in the survey and then out.

The selection of the sample to the LFS survey is carried out according to the principles of the two-stage sampling. The first-stage sampling units are census units called census clusters (in urban areas) and census enumerations districts (in rural areas) stratified by voivodships and classes of localities. The second-stage sampling units are dwellings. The selections of the first-stage sampling units and dwellings is carried out on the basis of the National Official Register Territorial Division of the Country, including, i.a. the list of territorial statistical units and the address register of dwellings aggregated in accordance with particular census clusters and census enumeration districts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Labour Force Survey is carried out with the use of two questionnaires: • ZG Household File - assigned to each household recorded in the surveyed dwelling (ZG Files comprise household register during the entire survey cycle);

• ZD questionnaire, completed each quarter for every person aged 15 years and more present in the household or absent from the household; the ZD questionnaire includes questions concerning the respondent's current economic activity (i.e. the fact of performing work, being unemployed or economically inactive), occupational history, the methods of job search, as well as additional questions concerning, i.e. education and training.

Demographic change in Europe. Topics: most pressing demographic challenges in the own country; most important threats to the EU’s economic prosperity and competitiveness; attitude towards the following statements about the current demographic trends in the EU: contribute to labour shortages, contribute to skills mismatches, put the EU´s long-term economic prosperity and competitiveness at risk, undermine long-term sustainability of public finances, intensify differences between and within EU member states, affect personal prospects and future possibilities; preferred level of action to manage demographic change: EU level, member state level, both levels, measures to manage demographic change should not be a political priority; attitude towards the following statement: managing demographic change requires close cooperation between all relevant levels of government; most effective actions to address the consequences of a shrinking workforce in the own country: facilitate the combination of paid work and private life, facilitate longer working lives, reform pensions systems, facilitate labour mobility and migration to attract talent from abroad, address youth unemployment, support regions affected by depopulation, other; preferred governmental actions in the own country to enable the current and future generations to lead an active life in old age: support lifelong education and training, adjust workplace conditions to the needs of older persons, allow people to continue working past the official retirement age if they want to, make sure pensions are high enough, provide high-quality and affordable health care services, provide high-quality and affordable long-term care services, provide adequate and affordable housing, other; attitude towards the following statement: digital technologies, robotics and artificial intelligence can help address the consequences of a shrinking and ageing population, including possible labour shortages. Demography: age; sex; nationality; financial difficulties; age at end of education; occupation; professional position; type of community; household composition and household size; own a mobile phone and fixed (landline) phone. Additionally coded was: respondent ID; country; type of phone line; region; nation group; weighting factor. Demographischer Wandel in Europa. Themen: dringlichste demographische Herausforderungen im eigenen Land; wichtigste Bedrohungen für den wirtschaftlichen Wohlstand und die Wettbewerbsfähigkeit der EU; Einstellung zu den folgenden Aussagen über aktuelle demographische Trends in der EU: tragen zum Arbeitskräftemangel bei, tragen zum Qualifikationsungleichgewicht bei, sind eine Gefahr für den langfristigen wirtschaftlichen Wohlstand und die Wettbewerbsfähigkeit der EU, unterminieren die langfristige Nachhaltigkeit öffentlicher Finanzen, verstärken die Unterschiede zwischen und innerhalb der EU-Mitgliedstaaten, haben Auswirkungen auf persönliche Aussichten und künftige Möglichkeiten; präferierte Handlungsebene beim Umgang mit dem demographischen Wandel: EU-Ebene, Ebene der Mitgliedstaaten, beide Ebenen, Maßnahmen zum Umgang mit dem demographischen Wandel sollten keine politische Priorität haben; Einstellung zu der folgenden Aussage: Umgang mit demographischem Wandel verlangt enge Zusammenarbeit aller relevanten Regierungsebenen; effektivste Maßnahmen beim Umgang mit den Folgen einer schrumpfenden Erwerbsbevölkerung im eigenen Land: Erleichterung der Vereinbarkeit von bezahlter Arbeit und Privatleben, Erleichterung von verlängerten Erwerbsbiografien, Reform der Rentensysteme, Erleichterung der Mobilität und Migration von Arbeitskräften zur Rekrutierung von Fachkräften aus dem Ausland, Bekämpfung der Jugendarbeitslosigkeit, Unterstützung von von Entvölkerung betroffenen Regionen, andere; präferierte Regierungsmaßnahmen im eigenen Land zur Förderung eines aktiven Lebens im Alter für heutige und künftige Generationen: Förderung von lebenslanger Bildung und Ausbildung, Anpassung der Arbeitsplatzbedingungen an die Bedürfnisse älterer Menschen, auf Wunsch Ermöglichen von Arbeit über das offizielle Rentenalter hinaus, Sicherstellung einer ausreichenden Rentenhöhe, Bereitstellung qualitativ hochwertiger und erschwinglicher Gesundheitsversorgung, Bereitstellung qualitativ hochwertiger und erschwinglicher pflegerischer Langzeitversorgung, Bereitstellung angemessener und bezahlbarer Wohnmöglichkeiten, andere; Einstellung zu der folgenden Aussage: Digitale Technologien, Robotik und künstliche Intelligenz können beim Umgang mit den Folgen einer schrumpfenden und alternden Bevölkerung (inkl. Arbeitskräftemangel) hilfreich sein. Demographie: Alter; Geschlecht; Staatsangehörigkeit; finanzielle Schwierigkeiten; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße; Besitz eines Mobiltelefons; Festnetztelefon im Haushalt. Zusätzlich verkodet wurde: Befragten-ID; Land; Interviewmodus (Mobiltelefon oder Festnetz); Region; Nationengruppe; Gewichtungsfaktor.

This dataset presents the total number of economically active individuals aged 15 years and above in the State of Qatar, disaggregated by nationality (Qatari and Non-Qatari) and gender (males and females) from 2018 to 2023. It includes annual totals for each subgroup and overall aggregates for males, females, and the entire population. This data is essential for analyzing labor force participation trends and informing workforce planning and policy.

Abstract

The survey concentrates on the situation from the point of view of the economic activity of the population, i.e. the fact of being employed, unemployed or economically inactive in the reference week.

The primary objective of the LFS is to obtain data on the size and structure of the labor force. The results of the study will be used primarily to: - Determining the balance of the workforce from three basic categories of the population: employed, unemployed and inactive, - Analysis of changes in economic activity in different groups of socio-professional and cross-territorial - Analysis of the situation on the labor market, including the assessment of the scale and extent of changes in the unemployment rate in spatial terms, - Socio-demographic characteristics of the unemployed, - Analysis of the structure of employment by socio-demographic characteristics and professional.

Geographic coverage

National coverage

Analysis unit

• Individual aged 15 and over

Universe

The LFS covers persons aged 15 and over who are the members of sampled households. The survey does not cover the members of households who stayed abroad above 2 months. Neither does it cover the population living in the collective households, such as lodging-houses for employees, student hostels, boarding-schools, army barracks, houses for the poor and the old, etc.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Labour Force Survey is the probability sample survey. Its results are generalized on the general population.

Since the fourth quarter of 1999, the observation of the reference week in the middle month of a quarter has been replaced by the continuous observation method, which means that in each of the 13 weeks of a quarter 1/13th part of the quarterly sample of dwellings is surveyed. It allows presenting the situation on the labour market during a whole quarter.

The selection of quarterly samples follows principles of the so-called rotation scheme according to which there are scheduled for the interview in a given quarter: two elementary samples2 surveyed in the previous quarter, one elementary sample introduced into the survey for the first time and one elementary sample that was not surveyed in the previous quarter and had been introduced into the survey exactly one year before. As the result of this rotation system each elementary sample is used according to the 2-(2)-2 principle: two quarters in the survey, two quarters break, again two quarters in the survey and then out.

The selection of the sample to the LFS survey is carried out according to the principles of the two-stage sampling. The first-stage sampling units are census units called census clusters (in urban areas) and census enumerations districts (in rural areas) stratified by voivodships and classes of localities. The second-stage sampling units are dwellings. The selections of the first-stage sampling units and dwellings is carried out on the basis of the National Official Register Territorial Division of the Country, including, i.a. the list of territorial statistical units and the address register of dwellings aggregated in accordance with particular census clusters and census enumeration districts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Labour Force Survey is carried out with the use of two questionnaires: • ZG Household File - assigned to each household recorded in the surveyed dwelling (ZG Files comprise household register during the entire survey cycle);

• ZD questionnaire, completed each quarter for every person aged 15 years and more present in the household or absent from the household; the ZD questionnaire includes questions concerning the respondent's current economic activity (i.e. the fact of performing work, being unemployed or economically inactive), occupational history, the methods of job search, as well as additional questions concerning, i.e. education and training.

Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The "Sysmiss" label in the Statistics section indicates the number of non-responding records for each variable, and the "Valid" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. The Labour Force Survey provides estimates of employment and unemployment which are among the most timely and important measures of performance of the Canadian economy. With the release of the survey results only 13 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Human Resources Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.