Labor Force Participation Rate in the United States increased to 62.40 percent in September from 62.30 percent in August of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Time use survey (TUS) Purpose and brief description The time use survey tries to sketch an as precise picture as possible about the every-day activities of people. In a time use survey, the respondents are asked to record all their activities and their times. Furthermore, additional information about the activities is also asked, such as with whom and where the respondent was.The time use survey tries to sketch an as precise picture as possible about the every-day activities of people. In a time use survey, the respondents are asked to record all their activities and their times. Furthermore, additional information about the activities is also asked, such as with whom and where the respondent was. Statbel carried out this survey in 1999, 2005 and 2013. Population Members of private households where at least one person is in the age group 15-76. Only individuals aged 10 or older are interviewed. Sample frame Demographic data from the National Register. Data collection method In the past, households were visited by an interviewer who gave them instructions about the survey. The members of the household were asked to record their activities in a diary during 2 specific days (one weekday and one weekend day). In the next survey, the data will be collected via a digital platform, composed of a web application and a smartphone application. The fieldwork period is not yet known. Sample size In 1999, 8,382 respondents aged 12 or older from 4,275 households registered their time use for two days. In 2005, there were 6,400 respondents aged 12 or older from 3,474 households. Finally, there were 5,559 respondents aged 10 or older from 2,744 households in 2013. Response rate The response rate amounted to 22.1% in 2013. Periodicity From 2030 onwards, this survey will be part of the IESS (Integrated European Social Statistics) and will be organised every 10 years for Eurostat. Release calendar The results are available at the latest 15 months after the end of the data collection. The most recent results are those of 2013. Definitions A household consists either of a single person, usually living alone, or of two or more persons who, whether or not related to one another by kinship, usually live in one and the same dwelling and live there together. The most common way to present time use data is by using three parameters: The duration per respondent (dpr.): this is the average time spent on a given activity in a given period, calculated for all participants to the research (respondents).this is the average time spent on a given activity in a given period, calculated for all participants to the research (respondents). The participation rate (part.): this is the percentage of respondents who performed a given activity in a given time span. The duration per participant (dpp.): this is the average time spent on a given activity in a given time span, calculated for all participants to the activity. The given period is always a registration day (24h). These three parameters are not independent of each other. The duration per respondent is the product of the duration per participant and the participation rate (number between 0 and 1 expressed as a percentage): Duration per respondent = duration per participant x participation rate This rule holds as far as one looks at the parameters for the registration days (Monday to Sunday) separately, but does not hold for the constructed average weekday and weekend day because we only have the registration of one particular weekday and one particular weekend day per respondent. The parameters for the average weekday and the average weekend day are estimates, taking into account the number of respondents who filled in a particular day for Monday to Friday for the average weekday and for Saturday and Sunday for the average weekend day, respectively. A weighting procedure minimises the deviation in the relationship between duration per respondent, participation rate and duration per participant. An example An example could help to interpret the results: In a weekday the respondents spent on average 2 h 44 on the activity 'work' (=duration per respondent). However, not all participants to the survey worked on the weekdays they kept their diaries. 37.2% of respondents effectively performed the activity 'work' on the recorded weekdays (= participation rate). Respondents who effectively worked on the recorded weekdays spent an average of 7 h 21 on the activity 'work' on a weekday (= duration per participant). Duration per respondent = duration per participant x participation rate 2H44’ = 7h21’ x 37.2% HETUS guidelines Eurostat provides guidelines to carry out the time use survey. They are available here. Reports and articles Technical report of the 2013 Belgian Time-Use Survey SourceTM in opdracht van EUROSTAT SOURCE™ (Software Outreach and Redefinition to Collect E-data through MOTUS) is a project coordinated by Statbel in collaboration with Destatis (the national statistical institute of German

Key information about France Labour Force Participation Rate

• France Labour Force Participation Rate dropped to 75.2 % in Sep 2025, compared with 75.3 % in the previous quarter.
• France Labour Force Participation Rate is updated quarterly, available from Mar 2003 to Sep 2025, with an average rate of 71.9 % .
• The data reached the an all-time high of 75.3 % in Jun 2025 and a record low of 70.2 % in Sep 2003.
• France Labour Force Participation Rate is reported by reported by National Institute of Statistics and Economic Studies.
• In the latest reports, France Population reached 68.6 million people in Dec 2024.
• Unemployment Rate of France increased to 7.7 % in Sep 2025.

France Unemployment Rate: Average: sa: MF: Age: Above 49 data was reported at 6.100 % in Sep 2018. This records a decrease from the previous number of 6.300 % for Jun 2018. France Unemployment Rate: Average: sa: MF: Age: Above 49 data is updated quarterly, averaging 5.400 % from Mar 1975 (Median) to Sep 2018, with 175 observations. The data reached an all-time high of 7.100 % in Jun 2015 and a record low of 1.800 % in Mar 1975. France Unemployment Rate: Average: sa: MF: Age: Above 49 data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G024: Unemployment Rate: Seasonally Adjusted.

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.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

Participation rate in education, population aged 18 to 34, by age group and type of institution attended, Canada, provinces and territories. This table is included in Section E: Transitions and outcomes: Transitions to postsecondary education of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

France Unemployment Rate: Average: sa: Age: Women: 15 to 24 data was reported at 19.000 % in Sep 2018. This records a decrease from the previous number of 19.400 % for Jun 2018. France Unemployment Rate: Average: sa: Age: Women: 15 to 24 data is updated quarterly, averaging 22.600 % from Mar 1996 (Median) to Sep 2018, with 91 observations. The data reached an all-time high of 26.000 % in Jun 2013 and a record low of 16.800 % in Jun 2001. France Unemployment Rate: Average: sa: Age: Women: 15 to 24 data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G024: Unemployment Rate: Seasonally Adjusted.

Graph and download economic data for Average Weekly Insured Unemployment Rate, State Programs, Excluding Puerto Rico for United States (M08310USM156SNBR) from Jan 1949 to May 1969 about insurance, average, unemployment, rate, and USA.

Abstract (en): Using recent results in the measurement error literature, we show that the official US unemployment rate substantially underestimates the true level of unemployment, due to misclassification errors in the labor force status in the Current Population Survey. During the period from January 1996 to August 2011, the corrected monthly unemployment rates are between 1 and 4.4 percentage points (2.1 percentage points on average) higher than the official rates, and are more sensitive to changes in business cycles. The labor force participation rates, however, are not affected by this correction.

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 October 30, 2025: The figures for the 3rd 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 January 2026.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan—another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.

The Student Performance Analysis dataset provides a detailed overview of academic achievement, study habits, and behavioral factors that influence student outcomes. It captures the relationship between study hours, attendance rate, participation, and total performance scores across different grade levels.

This dataset is designed for educators, researchers, and data scientists who want to explore how lifestyle and study behaviors affect student success. It can be used for data visualization, machine learning, regression analysis, and predictive modeling tasks in education analytics.

Features

• Student ID - Unique identifier for each student
• Name - Student’s name (for reference)
• Grade - Academic grade or level (A, B, C, etc.)
• Gender - Student’s gender (M/F)
• Study Hours - Average hours spent studying daily
• Weekly Self-Study Hours - Independent study time per week
• Total Score - Combined performance score based on tests and assignments
• Attendance Rate - Percentage of classes attended

Initial data analysis checklist for data screening in longitudinal studies.

Persons of working age in the newly formed German states for two specific years

This dataset provides valuable insights into the work and household characteristics of married individuals in the United States. With 753 observations representing individuals, the dataset offers a comprehensive view of various factors that influence work patterns and family dynamics.

Details

These data seem to have come from the same source as carData::Mroz, though each data set has variables not in the other. The variables that are shared have different names. On 2019-11-04 Bruno Rodrigues explained that Ecdat::Mroz['work'] had the two labels incorrectly swapped, and wooldridge::mroz['inlf'] was correct; wooldridge matches carData::Mroz['lfp'].

Source

Mroz, T. (1987) “The sensitivity of an empirical model of married women's hours of work to economic and statistical assumptions”, Econometrica, 55, 765-799. 1976 Panel Study of Income Dynamics.

References

Greene, W.H. (2003) Econometric Analysis, Prentice Hall, https://archive.org/details/econometricanaly0000gree_f4x3, Table F4.1.

Movie Scores vs. Unemployment Rate

Examining the Correlation between Cinema Popularity and Economic Stability

By [source]

About this dataset

This dataset examines the potential correlation between US unemployment rates and movie scores in order to explore how difficult economic times can influence how viewers rate films. With data spanning from 2009-2018, this dataset contains information on the yearly unemployment rate as well as the average movie score on a scale from 1-10 for that same year. Our goal is to investigate whether economic unrest and hardship have any effect on film ratings in order to shed light both on an often overlooked part of moviegoers' opinions, and also on our society's attitudes towards certain topics during times of crisis

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Research Ideas

• Predicting the success of a movie, given the economic conditions for that year.
• Determining how a year's unemployment rate affects viewers' overall opinion of movies from that same period.
• Analyzing whether people rate movies differently in times of economic difficulty than when the economy is booming

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: result.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | year | The year in which the movie was released. (Integer) | | UnEmployeeRate | The unemployment rate in the country during the year the movie was released. (Float) | | movieScore | The average score of the movie based on reviews from critic websites such as Rotten Tomatoes, IMDb etc., with 10 being highest rated movies. (Float) |

Acknowledgements

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

Iowa Law requires Iowa Workforce Development to establish a tax table for each year. The unemployment insurance rate table trigger formula is primarily based on the UI trust fund balance, unemployment benefit payment history and covered wage growth. The formula is designed to enable the trust fund to keep pace with potential liabilities as covered unemployment and wages grow.

This dataset contains the contribution rate table and the average tax rate for employers subject to the Iowa Unemployment Insurance system. There are eight rate tables each having 21 ranks. Table one has highest average tax rate. Table eight has the lowest average tax rate.

The highest average tax rate (based on taxable wages) was 3.38% in 1984 (Table 1). The lowest average tax rate was 0.94% in 1998 (Table 8). [Time Period: 1980-2018]

France Unemployment Rate: Average: sa: Age: Men: 25 to 49 data was reported at 8.100 % in Sep 2018. This records an increase from the previous number of 8.000 % for Jun 2018. France Unemployment Rate: Average: sa: Age: Men: 25 to 49 data is updated quarterly, averaging 8.000 % from Mar 1996 (Median) to Sep 2018, with 91 observations. The data reached an all-time high of 10.100 % in Jun 2015 and a record low of 5.700 % in Jun 2008. France Unemployment Rate: Average: sa: Age: Men: 25 to 49 data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.G024: Unemployment Rate: Seasonally Adjusted.

Abstract (en): This research is an exploration of a spatial approach to identify the contexts of unemployment-crime relationships at the county level. Using Exploratory Spatial Data Analysis (ESDA) techniques, the study explored the relationship between unemployment and property crimes (burglary, larceny, motor vehicle theft, and robbery) in Virginia from 1995 to 2000. Unemployment rates were obtained from the Department of Labor, while crime rates were obtained from the Federal Bureau of Investigation's Uniform Crime Reports. Demographic variables are included, and a resource deprivation scale was created by combining measures of logged median family income, percentage of families living below the poverty line, and percentage of African American residents. The purpose of this research was to develop and implement an exploratory spatial approach to identifying the contexts of unemployment-crime (U-C) relationships, focusing on the utility of the Exploratory Spatial Data Analysis (ESDA) in finding the county level contexts of U-C linkages. Exploratory Spatial Data Analysis (ESDA) techniques were used to study both the global and the local context of unemployment rates, index crimes, and resource deprivation. Annual data on unemployment rates were obtained from the United States Department of Labor's Bureau of Labor Statistics Web site for the years 1995 through 2000. Information on reported crime rates was obtained from the Geospatial and Statistical Data Center of the University of Virginia from data collected by the United States Department of Justice and the Federal Bureau of Investigation (Uniform Crime Reports). The study focused on crimes classified as property crimes under the Uniform Crime Reports (burglary, larceny, motor vehicle theft, and robbery) and on total index crimes. The Crime Index total is the sum of selected serious offenses including murder and non-negligent manslaughter, rape, robbery, aggravated assault, and the three property crimes, and was included in the study because reporting rates are most valid for index crimes. Finally, information on both age distributions and measures used in the resource deprivation scale were obtained from the county-level census files of the Geospatial and Statistical Data Center at the University of Virginia. The resource deprivation scale was created from 1990 Census data combining the following measures: logged median family income, percentage of families living below the poverty line, and percentage of African American residents. The data include the Federal Information Processing Standards (FIPS) county codes for the state of Virginia, the name of county or city, and region variable to indicate if the county is in the western, northern, or eastern region of the state. Crime rate variables include burglary crime rates, larceny crime rates, motor vehicle theft crimes rates, robbery crime rates, and the index crime rates. Four measures of unemployment are provided: unemployment rates, lagged unemployment rates, the average unemployment rates from 1995 to 2000, and the average unemployment rates from 1994 to 2000. Demographic variables included in the data are the number of males per 100 females, 1990, the percent of the population by age, 1990, and the Resource Deprivation Affluence Component scale. none Presence of Common Scales: none Response Rates: Not applicable. All property and index crime in all counties of Virginia from 1995 through 2000. Smallest Geographic Unit: county All reported property and index crimes and unemployment rates in all counties in Virginia are included in the study. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (2002-IJ-CX-0010). record abstractsThe files are provided in a WinZip archive with 12 files in three folders. The Statistical Data Files folder provides the data in Microsoft Excel files. The Geographic Data Files folder provides the geographic files for use with mapping software. The Report Files folder provides the final report, the cover for the final report, and two lists of measures.