The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical _location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

The Current Population Survey (CPS) is a sample survey of the population 16 years of age and over. The survey is conducted each month by the U.S. Census Bureau for the Bureau of Labor Statistics and provides comprehensive data on the labor force, the employed, and the unemployed, classified by such characteristics as age, sex, race, family relationship, marital status, occupation, and industry attachment. The information is collected by trained interviewers from a sample of about 60,000 households located in 754 sample areas. These areas are chosen to represent all counties and independent cities in the United States, with coverage in 50 States and the District of Columbia. The data collected are based on the activity or status reported for the calendar week including the 12th of the month. Union data are available for all workers, members of unions and represented by unions, with data available by age, race, Hispanic or Latino ethnicity, sex, occupation, industry, state, and full- or part-time status. Median weekly earnings data are also available for members of unions, represented by unions and non-union with data available by age, race, Hispanic or Latino ethnicity, sex, occupation, industry and full- or part-time status.

The Survey of Union Membership was jointly sponsored by Labour Canada and Statistics Canada. This survey attempted to answer such questions as: 1. how many workers had their wages and other conditions of work determined by a collective agreement; 2. among those employees who were covered by collective agreements, how many were actually union members; 2. which industries and provinces were actually the most unionized; 3. did the wages and pension plans of union members and non-union workers differ significantly

Graph and download economic data for Employed full time: Wage and salary workers: Property, real estate, and community association managers occupations: 16 years and over: Men (LEU0254580800A) from 2000 to 2024 about community, management, occupation, full-time, males, real estate, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Property, real estate, and community association managers occupations: 16 years and over: Women (LEU0254687600A) from 2000 to 2024 about community, management, occupation, females, full-time, real estate, salaries, workers, 16 years +, wages, employment, and USA.

Labor union members are more likely to vote than their non-unionized counterparts, and when they do vote, are more likely to support the Democratic Party. However, a sizable minority of union members vote Republican. This is puzzling, given that the national Republican Party has long been hostile toward organized labor. Extant research has clearly demonstrated that union and non-union members differ in their voting behavior, but we know little about such variation among union members. I explore this latter phenomenon here, arguing that the frequency of workplace political discussion plays an important role in shaping how labor union members vote. I test this with data from the 2004 National Annenberg Election Survey (NAES). Overall, I find that workplace discussion of politics is positively and significantly associated with the probability that labor union members vote Democrat. This appears to occur via a “learning” mechanism, in which greater workplace discussion of politics leads union members to recognize which candidate is more “pro-labor.” Overall, these findings help us to better understand the consequences of the workplace, political discussion, and the politics of American labor unions.

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 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. 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. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Property, real estate, and community association managers occupations: 16 years and over: Women (LEU0254741000A) from 2000 to 2024 about community, management, second quartile, occupation, females, full-time, real estate, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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.

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.

The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been 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 categories. Data from the survey provide information on major labour market trends such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates, employment including the self-employed, full and part-time employment, and unemployment. It publishes monthly standard labour market indicators such as the unemployment rate, the employment rate and the participation rate. The LFS is a major source of information on the personal characteristics of the working-age population, including age, sex, marital status, educational attainment, and family characteristics. Employment estimates include detailed breakdowns by demographic characteristics, industry and occupation, job tenure, and usual and actual hours worked. This dataset is designed to provide the user with historical information from the Labour Force Survey. The tables included are monthly and annual, with some dating back to 1976. Most tables are available by province as well as nationally. Demographic, industry, occupation and other indicators are presented in tables derived from the LFS data. The information generated by the survey has expanded considerably over the years with a major redesign of the survey content in 1976 and again in 1997, and provides a rich and detailed picture of the Canadian labour market. Some changes to the Labour Force Survey (LFS) were introduced which affect data back to 1987. There are three reasons for this revision: The revision enables the use of improved population benchmarks in the LFS estimation process. These improved benchmarks provide better information on the number of non-permanent residents. There are changes to the data for the public and private sectors from 1987 to 1999. In the past, the data on the public and private sectors for this period were based on an old definition of the public sector. The revised data better reflects the current public sector definition, and therefore result in a longer time series for analysis. The geographic coding of several small Census Agglomerations (CA) has been updated historically from 1996 urban centre boundaries to 2001 CA boundaries. This affects data from January 1987 to December 2004. It is important to note that the changes to almost all estimates are very minor, with the exception of the public sector series and some associated industries from 1987 to 1999. Rates of unemployment, employment and participation are essentially unchanged, as are all key labour market trends. The article titled Improvements in 2006 to the LFS (also under the LFS Documentation button) provides an overview of the effect of these changes on the estimates. The seasonally-adjusted tables have been revised back three years (beginning with January 2004) based on the latest seasonal output.

Migrants in the European Union constitute a much higher share of occupations in which national authorities have identified a labor shortage than in occupations without labor shortages, as of 2021. Migrants comprised almost ** percent of the workforce in all labor shortage occupations in the EU that year, while they made up just *** percent of the workforce in non-shortage occupations. Many European governments see increasing targeted labor migration for work in sectors experiencing shortages as a key way of relieving the widespread labor and skills shortages being experience across the continent.

The WageIndicator Survey is a continuous, multilingual, multi-country web-survey, counducted across 65 countries since 2000. The web-survey generates cross sectional and longitudinal data which might provide data especially about wages, benefits, working hours, working conditions and industrial relations.The survey has detailed questions about earnings, benefits, working conditions, employment contracts and training, as well as questions about education, occupation, industry and household characteristics.Research Focus:The WageIndicator Survey is a multilingual questionnaire and aims to collect information on wages and working conditions. As labour markets and wage setting processes vary across countries, country specific translations have been favoured over literal translations. The WageIndicator Survey includes regularly extra survey questions for project targeting specific countries, for specific groups or about specific events.These projects usually address a specific audience (employees of a company, employees in an industry, readers of a magazine, members of a trade union or an occupational association, and alike). The data of the project questions are included in the dataset.Sample:The target population of the WageIndicator is the labour force, that is, individuals in paid employment as well as job seekers. In addition to workers in formal dependent employment the survey aims to include apprentices, employers, own-account workers, freelancers, workers in family businesses, workers in the informal sector, unemployed workers, job seekers individuals who never had a job, as well as retired workers and housewifes school pupils or students with a job on the side and persons performing voluntary work.The WageIndicator data is derived from a volunteer survey, inviting webvisitors to the national WageIndicator websites to complete the web-survey. Annually, the websites receive millions of web-visitors.Bias:Non-Probability web based surveys are problematic because not every individual has the same probability of being selected into the survey. The probability of being selected depends on national or regional internet access rates and on numbers of visitors accessing the webiste. Data of such surveys form a convenience rather than a probability sample. Due to the non-probability based nature of the survey and its selectivity the obtained results cannot be generalized for the population of interest; i.e. the labor force.Comparisons with representative studies found an underrepresentation of male labour force, part-timers, older age groups, and low educated persons.Besides other strategies to reduce the bias the WageIndicators provides different weighting schemes in order to correct for selection bias.Data Characteristics:The data is organised in annual releases. The data of the period 2000-2005 is released as one dataset. Each data release consists of a dataset with continuous variables and one with project variables. The continuous variables can be merged across years. All variable and value labels are in English. The data does not include the text variables and verbatims form open-ended survey questions, these are available in Excel-Format upon request.Spatial Coverage:The survey started in 2000 in the Netherlands. Since 2004, websites have been launched in many European countries, in North and South America and in countries in Asia. From 2008 on web sites have been launched in more African countries, as well as in Indonesia and in a number of post-Soviet countries.For each country each, the questions have been translated. Multilingual countries employ multilingual questionnaires. Country-specific translations and locally accepted terminology have been favored over literal translations.Rights: Due to the confidential character of the WageIndicator microdata, direct access to the data is only provided by means of research contracts. Access is in principle restricted to universities and research institutes. Date: 2000 -

The WageIndicator Survey is a continuous, multilingual, multi-country web-survey, counducted across 65 countries since 2000. The web-survey generates cross sectional and longitudinal data which might provide data especially about wages, benefits, working hours, working conditions and industrial relations.The survey has detailed questions about earnings, benefits, working conditions, employment contracts and training, as well as questions about education, occupation, industry and household characteristics.Research Focus:The WageIndicator Survey is a multilingual questionnaire and aims to collect information on wages and working conditions. As labour markets and wage setting processes vary across countries, country specific translations have been favoured over literal translations. The WageIndicator Survey includes regularly extra survey questions for project targeting specific countries, for specific groups or about specific events.These projects usually address a specific audience (employees of a company, employees in an industry, readers of a magazine, members of a trade union or an occupational association, and alike). The data of the project questions are included in the dataset.Sample:The target population of the WageIndicator is the labour force, that is, individuals in paid employment as well as job seekers. In addition to workers in formal dependent employment the survey aims to include apprentices, employers, own-account workers, freelancers, workers in family businesses, workers in the informal sector, unemployed workers, job seekers individuals who never had a job, as well as retired workers and housewifes school pupils or students with a job on the side and persons performing voluntary work.The WageIndicator data is derived from a volunteer survey, inviting webvisitors to the national WageIndicator websites to complete the web-survey. Annually, the websites receive millions of web-visitors.Bias:Non-Probability web based surveys are problematic because not every individual has the same probability of being selected into the survey. The probability of being selected depends on national or regional internet access rates and on numbers of visitors accessing the webiste. Data of such surveys form a convenience rather than a probability sample. Due to the non-probability based nature of the survey and its selectivity the obtained results cannot be generalized for the population of interest; i.e. the labor force.Comparisons with representative studies found an underrepresentation of male labour force, part-timers, older age groups, and low educated persons.Besides other strategies to reduce the bias the WageIndicators provides different weighting schemes in order to correct for selection bias.Data Characteristics:The data is organised in annual releases. The data of the period 2000-2005 is released as one dataset. Each data release consists of a dataset with continuous variables and one with project variables. The continuous variables can be merged across years. All variable and value labels are in English. The data does not include the text variables and verbatims form open-ended survey questions, these are available in Excel-Format upon request.Spatial Coverage:The survey started in 2000 in the Netherlands. Since 2004, websites have been launched in many European countries, in North and South America and in countries in Asia. From 2008 on web sites have been launched in more African countries, as well as in Indonesia and in a number of post-Soviet countries.For each country each, the questions have been translated. Multilingual countries employ multilingual questionnaires. Country-specific translations and locally accepted terminology have been favored over literal translations.Rights: Due to the confidential character of the WageIndicator microdata, direct access to the data is only provided by means of research contracts. Access is in principle restricted to universities and research institutes.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Property, real estate, and community association managers occupations: 16 years and over (LEU0254527400A) from 2000 to 2024 about community, management, second quartile, occupation, full-time, real estate, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). The LFS collects monthly information on the labour market activities of Canada's working age population. This product is for users who prefer to do their own analysis by focusing on specific subgroups in the population or by cross-classifying variables that are not in our catalogued products. This file 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. These and more are available by province and for the three largest census metropolitan areas (Montreal, Toronto, Vancouver). This is a monthly file, and is available going back to 1976.

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. 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. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996.

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.