This dataset presents the share of Qatari nationals working in the private sector as a proportion of the total Qatari labor force, disaggregated by gender. The figures are expressed as decimal fractions, enabling tracking of private sector engagement among Qatari males and females. This data supports labor policy analysis and efforts to promote national workforce inclusion in the private sector.

Civilian labor force data consists of the number of employed persons, the number of unemployed persons, an unemployment rate and the total count of both employed and unemployed persons (total civilian labor force). Labor force refers to an estimate of the number of persons, 16 years of age and older, classified as employed or unemployed. The civilian labor force, which is presented in these data tables, excludes the Armed Forces, i.e., the civilian labor force equals employed civilians plus the unemployed. Employed persons are those individuals, 16 years of age and older, who did any work at all during the survey week as paid employees, in their own business, profession or farm, or who worked 15 hours or more as unpaid workers in a family operated business. Also counted as employed are those persons who had jobs or businesses from which they were temporarily absent because of illness, bad weather, vacation, labor-management dispute, or personal reasons. Individuals are counted only once even though they may hold more than one job. Unemployed persons comprise all persons who did not work during the survey week but who made specific efforts to find a job within the previous four weeks and were available for work during the survey week (except for temporary illness). Also included as unemployed are those who did not work at all, were available for work, but were not actively seeking work because they were either waiting to be called back to a job from which they were laid off or waiting to report to a new job within 30 days. The unemployment rate represents the number of unemployed persons as a percent of the total civilian labor force.

This dataset presents the share of Qatari nationals working in the private sector as a proportion of the total Qatari labor force, disaggregated by gender. The figures are expressed as decimal fractions, enabling tracking of private sector engagement among Qatari males and females. This data supports labor policy analysis and efforts to promote national workforce inclusion in the private sector.

This dataset presents the share of Qatari nationals working in the private sector as a proportion of the total Qatari labor force, disaggregated by gender. The figures are expressed as decimal fractions, enabling tracking of private sector engagement among Qatari males and females. This data supports labor policy analysis and efforts to promote national workforce inclusion in the private sector.

Public and private sector employment, UK, published quarterly, seasonally adjusted.

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools, and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC), and with the membership, staff tracks collaborative efforts to recruit business prospects and locations. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities, and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe. This dataset provides the target and actual job creation numbers for the City of Tempe and the Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population. This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created. Additional Information Source:Contact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

Analysis of ‘Female Employment vs Socioeconimic Factors’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdmuhtasimbillah/female-employment-vs-socioeconimic-factors on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastructures are responsible for this discrepancy, it goes way beyond these. And to show the effects of multiple socioeconomic factors on the participation of women in the total workforce, percentage of female employment in the total labor force has been considered. Using multiple linear regression model, the relationship between these factors can be analyzed.

Content

For the current study, the data set has been chosen from a survey performed on the population of Bangladesh. The datasets selected for this study span over 25 years (from 1995 to 2019). Data has been collected separately from multiple datasets from the World Bank databank for the employed women percentage and the related predictor variables. These datasets were compiled into one dataset and it corresponds to the 25 data points for the variables. There is one response variable which is the percentage of the employed women and 10 exlnanatory variables of predictors. Brief descriptions of these variables are given below.

PerFemEmploy Employment to population ratio (%) of women who are of age 15 or older. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.

FertilityRate Fertility rate (birth per women). Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

RatioMaletoFemale Ratio of female to male labor force participation rate. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.

PerFemEmployers Employers, female (% of female employment). Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a "self-employment jobs" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).

Agriculture Employment in agriculture, female (% of female employment). Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

Industry Employment in industry, female (% of female employment). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).

Services Employment in services, female (% of female employment). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).

Wage.Salaried Wage and salaried workers, female (% of female employment). Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.

ContrFamWorkers Contributing family workers, female (% of female employment). Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.

OwnAccount Own-account female workers (% of employment). Own-account workers are workers who, working on their own account or with one or more partners, hold the types of jobs defined as "self-employment jobs" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of "self-employed".

Vulnerable Vulnerable employment, female (% of female employment). Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.

Inspiration

Linear model as well as other statistical methods can be applied on this dataset to analyze if there is any viable relationship between the predictor and the response variables.

--- Original source retains full ownership of the source dataset ---

The labour force participation rate is the percentage of economically active population aged 15-64 on the total population of the same age. According to the definitions of the International Labour Organisation (ILO) for the purposes of the labour market statistics people are classified as employed, unemployed and outside the labour force. The economically active population (also called labour force) is the sum of employed and unemployed persons. Persons outside the labour force are those who, during the reference week, were neither employed nor unemployed. The MIP Scoreboard indicator is the three-year change in percentage points, with an indicative threshold of -0.2 pp. In the table, values are expressed also as percentage of total population. The data source is the quarterly EU Labour Force Survey (EU LFS). The survey covers the resident population in private households.

Employment by industry and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

Number of days lost per full-time employee in a year, by public and private sector and gender, annual.

Percentage and average percentage of workforce anticipated to work on-site or remotely over the next three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Virginia Works (Department of Workforce Development and Advancement) today announced that Virginia’s seasonally adjusted unemployment rate in January remained unchanged at 3.0 percent, which is 0.1 percentage point below the rate from a year ago. According to household survey data in January, the number of employed residents increased by 8,212 to 4,448,520 and the number of unemployed residents increased by 346 to 139,731. The labor force increased by 8,558 to 4,588,251. Virginia’s seasonally adjusted unemployment rate is 0.7 percentage points below the national rate, which remained unchanged at 3.7 percent.

The Commonwealth’s labor force participation rate increased by 0.1 percentage points to 66.6 percent in January. The labor force participation rate measures the proportion of the civilian population age 16 and older that is employed or actively looking for work.

In January, Virginia’s nonagricultural employment, from the monthly establishment survey increased by 8,700 to 4,200,000. December’s preliminary estimate of employment, after revision, increased by 34,300 to 4,191,300. In January, private sector employment increased by 4,200 to 3,458,500 while government employment increased by 4,500 to 741,500. Within that sector, federal government jobs increased by 700 to 190,500, state government employment increased by 3,700 to 157,400, and local government increased by 100 to 393,600 over the month. Seasonally adjusted total nonfarm employment data is produced for eleven industry sectors. In January, six experienced over-the-month job gains, one remained unchanged, and four experienced a decline. The largest job gain occurred in Professional and Business Services (+5,100) to 807,900. The second largest job gain occurred in Government (+4,500) to 741,500. The third largest job gain occurred in Financial Activities (+1,400) to 224,000. The other gains were in Manufacturing (+1,100) to 248,600; Education and Health Services (+500) to 594,600; and Mining and Logging (+100) to 7,300.

Abstract copyright UK Data Service and data collection copyright owner. The primary aim of the research project was to present an overview of Britain's relative competitive performance in the postwar period. Previous research in this area has concentrated on either the total economy or on manufacturing. The aim of the project was to broaden the scope of research by examining competitive performance for all sectors of the aggregate economy. To do so, a dataset was constructed to enable measurement of productivity (both labour and total factor productivity) and unit labour costs comparing Britain to four of her major competitors, i.e. the US, France, Germany and Japan. The research was concerned with to what extent the performance at the aggregate economy level was affected by the inclusion of non-market services (health, education and government), which are poorly measured in the national accounts. Differences in performance between service sectors and production industries were also analysed. Main Topics: The dataset contains the raw data necessary to enable an evaluation of Britain's relative competitive performance at the sector level. Hence the dataset contains annual time series, from 1950 to 1995, on real output (generally value added), number of persons engaged, average annual hours worked, net capital stocks, labour force skills and labour's share of value added. It also includes benchmark estimates of relative productivity levels for 1993. The data are available for a maximum of 33 sectors, some of which are broad sectors and some comprise sub-industries with these broad sectors. The sectors included are : 1. Agriculture, forestry and fishing; 2. Mining and oil refining (2.1 oil and gas extraction, 2.2 other mining, 2.3 mineral oil refining); 3. Utilities (3.1 electricity, 3.2 gas, 3.3 water supply); 4. Manufacturing 5. Construction 6. Distributive trades (6.1 wholesale trade, 6.2 retail trade, 6.3 hotels and catering); 7. Transport and communications (7.1 rail transport, 7.2 water transport, 7.3 air transport, 7.4 other transport & transport services, 7.5 communications); 8. Financial & business services (8.1 banking & finance, 8.2 insurance, 8.3 business services); 9. Miscellaneous personal services; 10. Non-market services (10.1 health, 10.2 education, 10.3 government) plus the total for the aggregate economy and the total over market sectors (excluding non-market sectors). The manufacturing sectors are : 4.1. Chemicals and allied products (4.11 chemicals, 4.12 rubber and plastics); 4.2. Metals (4.21 basic metals and 4.22 metal products); 4.3. Engineering industries (4.31 mechanical engineering, 4.32 office machinery, 4.33 electrical engineering, 4.34 mo tor vehicles, 4.45 other transport equipment, 4.36 instrument engineering); 4.4. Textile and related products (4.41 textiles, 4.42 clothing, footwear and leather); 4.5. Food, drink and tobacco and 4.6. Other manufacturing (4.61 non-metallic mineral products, 4.62 wood & furniture, 4.63 paper & printing, 4.64 miscellaneous manufacturing. The data originate from official publications but include adjustment to render them internationally comparable. Data limitations imply that the series are not always available for all sectors in all countries. The most complete data series are for the US, the UK and Germany - less detail is available for France and Japan. The data represent in some cases a complete, and in some cases a partial transcription of original sources. Adjustments were made to the original sources to render them consistent across both time and countries. For example, in constructing real output for the US, the data are those published for Construction, indexed to 1993=100. For the communications sector, however, the US industry definition includes telecommunications, radio and TV, whereas communications in all other countries comprise telecommunications and postal services. Hence for the US output of the postal services (included with the Federal Government) was estimated and added to output in telecommunications. For further details, please see documentation. The dataset contains the following files : NISECQ.xls: Real value added by sector, (index, 1993=100). NISECMQ.xls: Real value added in manufacturing industries, (index, 1993=100). NISECE.xls: Number of persons engaged by sector, (thousands of persons). NISECME.xls: Number of persons engaged in manufacturing, (thousands of persons). NISECHR.xls: Annual average hours per worker by sector, (number of hours). NISECMHR.xls: Annual average hours per worker in manufacturing, (number of hours). NISECPR.xls: Value added per hour worked by sector, (index, 1993=100). NISECMPR.xls: Value added per hour worked in manufacturing, (index, 1993=100). NISECK.xls: Capital services by sector, (index, 1993=100). NISECMK.xls: Capital services in manufacturing, (index, 1993=100). (this file does include a spreadsheet for Japan ) NISECLS.xls: Labour's share of value added by sector, (proportions). NISECMLS.xls: Labour's share of value added in manufacturing, (proportions). (this file does include a spreadsheet for Japan) NISECSK.xls: Labour force skills by sector, divided into higher level, intermediate level and low skills (percent of the workforce). Note: these data are available only for the US, the UK and Germany. NISECMSK.xls: Labour force skills in manufacturing, divided into higher level, intermediate level and low skills (percent of the workforce). Note: these data are available only for the US, the UK and Germany. NISECLV.xls: Relative levels of value added per hour worked and capital per hour worked in 1993, (UK=100). This contains one spreadsheet for sectors and one for manufacturing. No information recorded

Proportion of providers returning data to the further education vacancy data collection. Data provided for the following providers types: General Further Education Colleges, Sixth Form Colleges, Private Sector Public Funded Providers and Other Public Funded Providers.

Number of employees by North American Industry Classification System (NAICS) and type of employee, last 5 years.

Proportion of providers returning data to the further education vacancy data collection. Data provided for LA Providers with an Education remit, School based providers, Independent Training Providers (ITP) and Special Post-16 Institutions. These are sub groups of Private Sector Public Funded Providers and Other Public Funded Providers.

Full Time Employment in the United States decreased to 134480 Thousand in August from 134837 Thousand in July of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

IntroductionIn sub-Saharan Africa, there is a need to better understand and guide the practice of primary care physicians (PCPs), especially in a crisis context like the COVID-19 pandemic. This study analyses the experiences of PCPs facing COVID-19 in Benin and draws policy lessons.MethodsThe study followed a fully mixed sequential dominant status design. Data were collected between April and August 2020 from a sample of PCPs in Benin. We performed descriptive analyses on the quantitative data. We also performed bivariate analyses for testing associations between various outcomes and the public/private status of the PCPs, their localization within or outside the cordon sanitaire put in place at the beginning of COVID-19, and their practice' category. A thematic content analysis was done on qualitative data. Results from both analyses were triangulated.ResultsNinety PCPs participated in the quantitative strand, and 14 in the qualitative. The median percentage of the COVID-19 control measures implemented in the health facilities, as reported by the PCPs, was 77.8% (interquartile range = 16.7%), with no difference between the various groups. While 29.4% of the PCPs reported being poorly/not capable of helping the communities to deal with COVID-19, 45.3% felt poorly/not confident in dealing with an actual case. These percentages were bigger in the private sector. The PCP's experiences were marked by anxiety and fear, with 80.2% reporting stress. Many PCPs (74.1%) reported not receiving support from local health authorities, and 75.3% felt their concerns were not adequately addressed. Both percentages were higher in the private sector. The PCPs especially complained of insufficient training, insufficient coordination, and less support to private providers than the public ones. For 72.4 and 79.3% of the PCPs, respectively, the pandemic impacted services utilization and daily work. There were negative impacts (like a decrease in the services utilization or the quality of care), but also positive ones (like improved compliance to hygiene measures and new opportunities).ConclusionOur study highlighted the need for more structured support to PCPs for optimizing their contribution to epidemics control and good primary healthcare in Benin. Efforts in this direction can build on several good practices and opportunities.

This dataset shows the percentage of State Employees that work in Corrections by state in the year 2006. This data was brought to our attention by the Pew Charitable Trusts in their report titled, One in 100: Behind Bars in America 2008. The main emphasis of the article emphasizes the point that in 2007 1 in every 100 Americans were in prison. To note: The District of Columbia is not included. D.C. prisoners were transferred to federal custody in 2001