This dataset provides values for LABOR FORCE PARTICIPATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Gender Disaggregated Labor Database (GDLD) is a global micro labor force database based on World Bank household survey collection and other public resources. This database has harmonized the economic activities and occupation categories from local classification to international comparable classifications. It fills an important information gap in global sex statistics by providing detailed accounts on education, employment levels, wages, labor income, and employment status at very disaggregated economic activity level and occupation category than is usually available.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Mexico Unit Labour Costs: Index data was reported at 153.807 2015=100 in 2022. This records an increase from the previous number of 141.759 2015=100 for 2021. Mexico Unit Labour Costs: Index data is updated yearly, averaging 96.662 2015=100 from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 153.807 2015=100 in 2022 and a record low of 69.230 2015=100 in 2005. Mexico Unit Labour Costs: Index data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Mexico – Table MX.OECD.PDB: Unit Labour Costs: OECD Member: Annual.

Context

This dataset includes labour statistics broken down by sex and country.

Content

Labour statistics represents: civilian employment, employees, unemployment, etc. Data are collected from sample household surveys on a monthly or quarterly basis. Data are expressed in levels, rates, quantities or indexes 2015=100.

Source

www.oecd.org
https://stats.oecd.org/viewhtml.aspx?datasetcode=ALFS_SUMTAB&lang=en

Inspiration

Some basic statistics for the world's labour force market.

Mexico Unit Labor Cost Index: Real data was reported at 101.710 2008=100 in Feb 2019. This records an increase from the previous number of 100.900 2008=100 for Jan 2019. Mexico Unit Labor Cost Index: Real data is updated monthly, averaging 95.307 2008=100 from Jan 2007 (Median) to Feb 2019, with 146 observations. The data reached an all-time high of 130.440 2008=100 in Dec 2007 and a record low of 88.401 2008=100 in Oct 2012. Mexico Unit Labor Cost Index: Real data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.G063: Unit Labor Cost Index: 2008=100.

BR: Labour Force Participation Rate: National Estimate: Female: Aged 15-24 data was reported at 48.289 % in 2023. This records a decrease from the previous number of 50.084 % for 2022. BR: Labour Force Participation Rate: National Estimate: Female: Aged 15-24 data is updated yearly, averaging 49.396 % from Dec 1960 (Median) to 2023, with 41 observations. The data reached an all-time high of 57.300 % in 2005 and a record low of 23.010 % in 1960. BR: Labour Force Participation Rate: National Estimate: Female: Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Labour Force. Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

This dataset provides values for EMPLOYMENT COST INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

International comparisons of employment, unemployment and economic inactivity, published monthly. Organisation for Economic Co-operation and Development.

CR: Wages Index: % Change over Previous Period data was reported at 0.000 % in Dec 2017. This stayed constant from the previous number of 0.000 % for Sep 2017. CR: Wages Index: % Change over Previous Period data is updated quarterly, averaging 0.000 % from Jun 2000 (Median) to Dec 2017, with 71 observations. The data reached an all-time high of 7.731 % in Sep 2008 and a record low of 0.000 % in Dec 2017. CR: Wages Index: % Change over Previous Period data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Costa Rica – Table CR.IMF.IFS: Wages, Labour Cost and Employment Index: Quarterly.

This Dataset containes the details of the AI, ML, Data Science Salary (2020- 2025). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.

The data is processed and updated on a weekly basis so the rankings may change over time during the year.

Attribute Information

• work_year: The year the salary was paid.
• experience_level: The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
• employment_type: The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance
• job_title: The role worked in during the year.
• salary: The total gross salary amount paid.
• salary_currency: The currency of the salary paid as an ISO 4217 currency code.
• salary_in_usd: The salary in USD (FX rate divided by avg. USD rate of respective year) via statistical data from the BIS and central banks.
• employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code.
• remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote/hybird 100 Fully remote (more than 80%)
• company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code.
• company_size: The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Acknowledgements

https://aijobs.net/

Photo by Anastassia Anufrieva on Unsplash

Armenia AM: Labour Force Participation Rate: Modeled ILO Estimate: % of Total Population Aged 15+ data was reported at 61.636 % in 2024. This records a decrease from the previous number of 62.033 % for 2023. Armenia AM: Labour Force Participation Rate: Modeled ILO Estimate: % of Total Population Aged 15+ data is updated yearly, averaging 61.942 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 65.543 % in 1990 and a record low of 60.278 % in 2006. Armenia AM: Labour Force Participation Rate: Modeled ILO Estimate: % of Total Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Labour Force. 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.;International Labour Organization. “ILO Modelled Estimates and Projections database (ILOEST)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

The International Price Program (IPP) produces Import/Export Price Indexes (MXP) containing data on changes in the prices of nonmilitary goods and services traded between the U.S. and the rest of the world. For more information and data visit: https://www.bls.gov/mxp/

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

CZ: Labour Force Participation Rate: National Estimate: Female: % of Female Population Aged 15+ data was reported at 51.750 % in 2023. This records a decrease from the previous number of 52.161 % for 2022. CZ: Labour Force Participation Rate: National Estimate: Female: % of Female Population Aged 15+ data is updated yearly, averaging 51.692 % from Dec 1991 (Median) to 2023, with 32 observations. The data reached an all-time high of 60.830 % in 1991 and a record low of 49.320 % in 2010. CZ: Labour Force Participation Rate: National Estimate: Female: % of Female Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Czech Republic – Table CZ.World Bank.WDI: Labour Force. 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.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

The “Employment by activites and status (ALFS)” dataset is a subset of the Annual Labour Force Statistics database which presents annual labour force statistics for OECD member countries, Brazil and 4 geographical areas (Major Seven, Euro zone, European Union and OECD-Total).

This dataset contains employment statistics broken down by economic activities as defined by the ISIC Rev. 4 classification and by professional status as defined by the ICSE-1993 including employees, employers and own-account workers, and unpaid family workers. It also shows the number of employees broken down by economic activities (ISIC Rev. 4).

Economic activities are defined according to the Major divisions of the International standard International Classification (ISIC) Rev. 4 with the exception of the United-States wich compiled since 2003, employment data by sector following the North American Industrial Classification System (NAICS); NAICS sector are then proxied to ISIC Rev. 4 and are therefore not strictly comparable with other countries’ data.

The professional status is defined in the International Classification by status in Employment (ICSE-1993). To be considered as an unpaid family worker, the hour-threshold varies from one hour to 18 hours a week.

Data are presented in thousands of persons, or as indices with base year 2015=100.

Annual data in this dataset are typically calculated as averages of infra-annual estimates. This can lead to differences with annual data published by National Statistics Institutes.

Historical data of employment statistics defined according to " https://stats.oecd.org/wbos/fileview2.aspx?IDFile=c2fdf1ac-9dba-4317-8a8d-d872f514a7d8"> ISIC Rev. 2 are also available from 1970 to 2014

Historical data of employment statistics defined according to ISIC Rev. 3 are available from 1983 to 2023. Data for Canada are currently only available in ISIC Rev. 3

Argentina AR: Labour Force Participation Rate: National Estimate: Aged 15-24 data was reported at 39.124 % in 2023. This records an increase from the previous number of 38.024 % for 2022. Argentina AR: Labour Force Participation Rate: National Estimate: Aged 15-24 data is updated yearly, averaging 48.740 % from Dec 1960 (Median) to 2023, with 38 observations. The data reached an all-time high of 58.420 % in 1960 and a record low of 33.099 % in 2020. Argentina AR: Labour Force Participation Rate: National Estimate: Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Argentina – Table AR.World Bank.WDI: Labour Force. Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

United States US: Wages Index: Manufacturing data was reported at 109.822 2010=100 in 2016. This records an increase from the previous number of 107.005 2010=100 for 2015. United States US: Wages Index: Manufacturing data is updated yearly, averaging 44.941 2010=100 from Dec 1948 (Median) to 2016, with 69 observations. The data reached an all-time high of 109.822 2010=100 in 2016 and a record low of 6.468 2010=100 in 1948. United States US: Wages Index: Manufacturing data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Wages, Labour Cost and Employment Index: Annual.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.