In 2023, California had the largest civilian labor force in the United States with about 19.31 million people. Wyoming had the smallest labor force with around 295,000 workers.

Reversing an upward trend, 2020 saw a drastic decline in employment in small brewing businesses in the United States. Employment has since rebounded, reaching an all-time high of 191,421 in 2023.

The average for 2023 based on 178 countries was 20.4 million people. The highest value was in China: 781.1 million people and the lowest value was in Tonga: 0.04 million people. The indicator is available from 1991 to 2023. Below is a chart for all countries where data are available.

Small area estimates of unemployment rate, employment rate and number of employed persons for census metropolitan areas, census agglomerations and self-contained labour areas. Data are unadjusted for seasonality and updated monthly.

Digital Workplace Market size was valued at 40.81 USD Billion in 2024 and is projected to reach 181.27 USD Billion by 2031, growing at a CAGR of 22.60% from 2024 to 2031.

Global Digital Workplace Market Drivers

Trends in Remote Work: The need for digital workplace solutions has grown dramatically as remote and hybrid work models have become more common. Companies are looking for all-inclusive platforms that facilitate smooth coordination, exchange of ideas, and increased output between geographically dispersed teams.

Adoption of Cloud Computing: One of the main forces behind the development of digital workplace solutions is the expanding use of cloud-based technology. Cloud platforms facilitate the deployment and management of digital workplace tools more effectively for organisations by providing scalability, flexibility, and accessibility.

Emphasis on Employee Experience: Businesses are giving improving the employee experience more attention. Through the provision of intuitive and user-friendly interfaces, digital workplace solutions facilitate increased employee engagement, contentment, and overall productivity.

Developments in Collaboration Tools: The digital workplace industry is being driven by the widespread use of sophisticated collaboration tools including virtual whiteboards, project management software, video conferencing, and instant messaging. These resources are crucial for encouraging collaboration and knowledge exchange.

Workforce Mobility: As the number of remote and mobile workers rises, there is an increasing demand for digital workplace solutions that are available from any location and on any device. These days, it’s essential for modern companies to have adaptable interfaces and mobile-friendly software.

Data Security and Compliance: Businesses are giving top priority to digital workplace solutions with strong security features as data privacy laws become more stringent. This covers identity access management, encryption, safe file sharing, and compliance measures.

AI and Automation: Increasing productivity and efficiency in digital workplaces is the result of integrating artificial intelligence (AI) and automation. The use of virtual assistants, automated workflows, and AI-driven insights is revolutionising the way employees communicate and work together.

Initiatives for Digital Transformation: In an effort to modernise their infrastructure and processes, many organisations are embarking on digital transformation projects. A crucial component of this transition is the implementation of digital workplace solutions, which help businesses become more competitive and adaptable.

Emphasis on Cost Optimisation: By reducing the need for office space, increasing operational efficiencies, and lowering the cost of IT infrastructure, digital workplace solutions can save money. Businesses are searching more and more for solutions with observable cost advantages.

Changing Workforce Expectations and Demographics: Newer generations entering the workforce are used to digital tools and anticipate contemporary workspaces that make use of cutting-edge technologies. The adoption of digital workplace solutions is being driven by the need to meet these expectations.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

This datasets presents smooth values of the number of people in the labour force of Local Government Area (LGA) regions for each quarter starting December 2010 up to June 2018. The boundaries used for the dataset follow the 2018 edition of the Australian Statistical Geography Standard (ASGS).

Small Area Labour Markets presents regional estimates of unemployment and the unemployment rate at two small area levels:

• For approximately 2,100 Australian Bureau of Statistics’ (ABS) Statistical Area Level 2s (SA2s), on a State/Territory and Metropolitan/Non-metropolitan basis; and

• For each of Australia’s 540 Local Government Areas (LGAs).

The estimates in Table 1 and 2 are smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is presented in the Explanatory Notes, as well as on page 43 of the PDF Publication.

Please note:

• AURIN has spatially enabled the original data.

• Where data values were "-" (no data provided) in the original data they have been set to null.

• Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution. Indeed, quarter-to-quarter comparisons may not be indicative of actual movements in the labour market. It is therefore recommended that year-on-year comparisons be used.

Global Workforce Analytics Market was valued at USD 1203.07 Million in 2024 and is projected to reach USD 4841.27 Million by 2031, growing at a CAGR of 19.01% during the forecast period 2024-2031.

Global Workforce Analytics Market Drivers

The increased demand for cloud-based software is expected to greatly boost the workforce analytics market. Cloud-based workforce analytics solutions have various benefits over traditional on-premises software, making them more appealing to businesses of all sizes. Cloud-based solutions provide scalability, allowing firms to simply increase their workforce analytics capabilities as their needs change, without the need for costly infrastructure expenditures or IT upkeep. This scalability is especially useful for firms with rapid growth or shifting workforce dynamics.

Cloud-based software provides greater flexibility and accessibility, allowing users to receive workforce analytics insights from any place or device with an internet connection. This flexibility is critical for businesses with spread workforces or remote employees because it enables smooth collaboration and decision-making across teams and departments. Furthermore, cloud-based workforce analytics solutions frequently offer shorter deployment times and automatic upgrades, ensuring that enterprises have access to the most recent features and functions without the need for manual intervention.

The total dollar amount invested in businesses with fewer than 500 employees per 50 businesses. Small business lending data to include FFIEC insured bank loans under $1 million to businesses made by large banks; venture capital investments; US Export-Import Bank loans; Small Business Innovation Research (SBIR) and Small business Technology Transfer Research (STTR) grants; government, philanthropic, and institutional grants supporting small businesses; Maryland Department of Commerce loans; State of Maryland Neighborhood BusinessWorks loans; Baltimore Development Corporation loans; and Small Business Administration 7a and 504 loans. Source: Johns Hopkins University, 21st Century Cities Initiative Years Available: 2016, 2017, 2018, 2019, 2020, 2021

Abstract

This data is from a quantitative survey administered in 2023 to 2,000 married Nepali women and men from 4 provinces in the country about their own beliefs regarding norms-related behaviors, their expectations of how common it is for others in their social group to engage in those behaviors, and the expected social consequences surrounding those behaviors. It is the primary dataset used to author the working paper titled "Women’s Labor Force Participation in Nepal: An Exploration of The Role of Social Norms" - which presents rigorous evidence on whether and the extent to which social norms matter for women's labor force participation in Nepal.

Geographic coverage

The survey data includes a representative sample of households from 4 out of 7 provinces in Nepal: 1. Bagmati Province 2. Sudurpashchim Province 3. Madhesh Province 4. Gandaki Province

Analysis unit

Individual

Universe

The sampling frame is a list of all wards within each selected province.

Kind of data

Sample survey data [ssd]

Sampling procedure

Ward (cluster) selection: The sampling frame consisted of the list of all wards within each selected province. Each province comprises districts and within each district are municipalities (urban and rural municipalities) which are further broken down into wards – the smallest administrative units. The list of wards and their population figures were taken from the latest available 2021 Census. First, the universe of all districts was stratified by urban and rural to ensure greater statistical power for detecting differences between the 2 localities. The stratification by urban-rural proportionate to the population proportion of each group within a province resulted in a self-weighted sample, allowing for analysis of data at the province level and further at locality level within each province. To select the wards, a random start point was generated to negate any bias in the list and to provide an independent chance of selection from the list. The sampling method used here, probability proportionate to size (PPS), gives an independent chance of selection to each ward as per its population size, i.e., a higher chance of selection to wards with a higher population size.38 As a first step of random selection of wards, the cumulative frequency (CF) of the population of households in a ward was calculated. Since the unit of analysis for our study purpose was households having certain criteria and we expected the main outcome variables (social norms) to vary at household levels (as opposed to at an individual level), the household population figures served as the basis for sampling purpose (as opposed to the population size of individuals for a ward). Applying PPS, in the first step, the required number of wards were selected for Categories 1 and 2 households (households with working and non-working females respectively). Following this, the clusters allocated for Category 3 (households with migrant population) households were taken as a subset of the wards selected for Categories 1 and 2.

Selection of the random starting point within each ward during in-field random sampling of households: The selection of the random starting point within a PSU was done by the survey supervisors. For every ward, a predefined landmark for the starting point was chosen. The predefined landmark consisted of i) school, ii) health post, iii) central marketplace, or iv) ward office. The selection of a predefined landmark was the basis of the starting point which was made at the central office. The chosen landmark for every cluster was rotated to account for randomization and to avoid interviewer bias. Once the landmark was chosen, each enumerator used the spin-the-bottle method to randomize the direction in which the survey took place. After starting with a household, enumerators used a skip interval to survey every third household in rural and every fifth household in urban areas. Once the household was chosen, the interviewer used the screener to ascertain the eligibility as per the category quota set aside for them.

Respondent selection: The respondents were selected based on a screener instrument that surveyed the following factors: 1. Gender: Since the views about social norms and labor market outcomes vary by gender, both males and females within a household were interviewed. However, for households with migrant men, only the women were interviewed. 2. Age group: For all women, the screener was applied so as to ensure that only women within the economically active age range, i.e., between the ages of 18-59 years were interviewed. For spouses of female respondents, they had to be at least 18 years of age with no maximum age limit set. 3. Ethnicity: Nepal has more than a hundred ethnic groups residing across the country, and thus the major 8-10 groups are captured in the sample. The other objective of applying a screener for monitoring ethnic composition was to ensure that marginalized ethnic groups such as Dalits were sufficiently represented in the survey. 4. Marital Status: Only married men and women were interviewed since marriage and the responsibilities that come with are sown to impose greater social barriers and restrictions on mobility and work of females. 5. Location: The survey was carried out in both rural and urban locations in a total of 4 provinces. 6. General demographic factors include: • Perceived economic situation: Low to middle-income • It was ensured that both the respondents (male and female for Categories 1 and 2) and female respondent for Category 3 belonged to the second generation of the selected household (for example, not the in-laws residing in a household but their son and his wife.

Mode of data collection

Computer Assisted Personal Interview [capi]

Since 1990, the employment rate of women in the United States has stayed more or less steady, reaching a peak of 57.5 percent in 2000. In 1990, the female employment rate was 54.3 percent, and in 2023, the employment rate was 55.4. Women in the workforce

There have historically been less women then men in the workforce. Additionally, women face many hurdles to equal treatment when they are employed, such as wage discrepancies, sexual harassment, and being expected to carryout the majority of household and family related tasks even while working full-time.

Women have historically been the primary caregivers and homemakers through many cultures worldwide. Despite this, the number of women joining the workforce has increased globally. Women in history faced the additional barrier of not being able to attend university, which barred them from gaining an education and access to professional job. However, as our cultures have modernized, women have been granted equal access to university in many societies. In 2014 in the United States, the number of university degrees awarded to women exceeded that of men for the first time. In 2021, 39.1 percent of women had completed at least four years of university compared to 36.6 percent of men. Despite this, the unemployment rate of women in the United States has fluctuated significantly since 1990. In 2021, Nebraska was the state with the highest percentage of women participating in the civilian labor force, second to the District of Columbia.

The wage gap

Today, the wage gap is still a problem for women, although improvements have been made. There is no state in the U.S. where women earn more than men, but women in Vermont had the smallest wage gap to men in 2021. Additionally, there are no occupations in which women out-earn men, even in occupations that traditionally employ more women. A more detailed look at wage inequality in the United States can be found here.

This datasets presents smooth values of the number of people in the labour force of Statistical Area Level 2 (SA2) regions for each quarter starting December 2010 up to June 2018. The boundaries used for the dataset follow the 2011 edition of the Australian Statistical Geography Standard (ASGS). Small Area Labour Markets presents regional estimates of unemployment and the unemployment rate at two small area levels: For approximately 2,100 Australian Bureau of Statistics’ (ABS) Statistical Area Level 2s (SA2s), on a State/Territory and Metropolitan/Non-metropolitan basis; and For each of Australia’s 540 Local Government Areas (LGAs). The estimates in Table 1 and 2 are smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is presented in the Explanatory Notes, as well as on page 43 of the PDF Publication. Please note: AURIN has spatially enabled the original data.

Transport for NSW provides projections of workforce at the small area (Travel Zone or TZ) level for NSW. The latest version is Travel Zone Projections 2024 (TZP24), released in January 2025.\r \r TZP24 replaces the previously published TZP22. The projections are developed to support a strategic view of NSW and are aligned with the NSW Government Common Planning Assumptions .\r \r TZP24 Workforce Projections cover persons who reside in Occupied Private Dwellings, aged 15 years and over, and are presented by their usual place of residence.\r \r The following Workforce variables are presented in TZP24:\r \r *\tEmployed People, 15 years and over \r *\tUnemployed People, 15 years and over \r *\tPeople not in the workforce, 15 years and over \r \r The projections in this release, TZP24, are presented annually from 2021 to 2031 and 5-yearly from 2031 to 2066, and are in TZ21 geography.\r \r Please note, TZP24 is based on best available data as at early 2024 and the projections incorporate results of the National Census conducted by the ABS in August 2021.\r \r Key Data Inputs used:\r \r *\tTZP24 Population and Dwellings projections\r *\tWorkforce participation rates - NSW Treasury\r *\tHistorical labour force data - ABS Labour Force Survey\r \r For a summary of the TZP24 Projections method please refer to the TZP24 Factsheet .\r \r For more detail on the projection process please refer to the TZP24 Technical Guide .\r \r Additional land use information for population and employment as well as Travel Zone 2021 boundaries for NSW (TZ21) and concordance files are also available for download on the Open Data Hub.\r \r A visualisation of the workforce projections is available on the Transport for NSW Website .\r \r Cautions\r \r The TZP24 dataset represents one view of the future aligned with the NSW Government Common Planning Assumptions population and employment projections.\r \r The projections are not based on specific assumptions about future new transport infrastructure, but do take into account known land-use developments underway or planned, and strategic plans.\r \r *\tTZP24 is a strategic state-wide dataset and caution should be exercised when considering results at detailed breakdowns.\r *\tThe TZP24 outputs represent a point in time set of projections (as at early 2024).\r *\tThe projections are not government targets.\r *\tTravel Zone (TZ) level outputs are projections only and should be used as a guide. As with all small area data, aggregating of travel zone projections to higher geographies leads to more robust results.\r *\tAs a general rule, TZ-level projections are illustrative of a possible future only.\r *\tMore specific advice about data reliability for the specific variables projected is provided in the “Read Me” page of the Excel format summary spreadsheets on the TfNSW Open Data Hub.\r *\tCaution is advised when comparing TZP24 with the previous set of projections (TZP22) due to addition of new data sources for the most recent years, and adjustments to methodology.\r \r \r Further cautions and notes can be found in the TZP24 Technical Guide

According to Cognitive Market Research, The global workforce management market size is USD 6.5 Billion in 2023 and will expand at a compound annual growth rate (CAGR) of 9.60% from 2023 to 2030

The demand for workforce managements is Increased usage of advanced technologies such as artificial intelligence.
Demand for cloud remains higher in the workforce management market.
The Consumer Goods and Retail category held the highest workforce management market revenue share in 2023.
North American workforce management will continue to lead, whereas the European workforce management market will experience the most substantial growth until 2030.

Adoption of Workforce Analytics Approach by Organizations to Provide Viable Market Output

Workforce analytics may identify and address flaws in a company's workforce operations and provide a better solution to achieve better business outcomes. According to an IBM Business Services report, workforce analytics plays a crucial part in HR's transition from more administrative areas to a more strategic approach. This piques organizations' interest in implementing workforce analytics to improve workforce operations. Organizations are more concerned with increasing the productivity of existing employees than with recruiting new ones. Workforce analytics could identify the characteristics of high-performing workers and teams, as well as the conditions that allow for improved workforce performance. As a result of the causes above, implementing workforce analytics solutions with this software is a growing workforce management trend.

Growing Use in SMEs to Propel Market Growth

As online consumption grows, organizations are under pressure to keep up, necessitating the addition of capacity for continued cloud-based services. Using cutting-edge technologies to better mobile workforce management can be costly, especially for small and medium-sized businesses. Various organizations can use the cloud-based solution flexibly and "as a service," allowing smaller firms to implement cutting-edge technologies with low variable costs and boost their competitiveness. Cloud-based usage has expanded as a result of the affordable and readily available mobile technology and the increased desire for economical technology solutions for small and mid-sized organizations. These cloud-based solutions appeal to both corporations looking to modernize their present procedures and SMEs looking for their first system.

Market Dynamics of Workforce Management

Issues Concerning Implementation and Integration to Restrict Market Growth

The majority of software-as-a-service evaluations focus on choosing a vendor that works well with and comparing feature sets to current problem areas. New workforce management software implementation can be a costly and time-consuming process that must be done right in order to reap the benefits that any company desires. Most firms only sometimes invest enough time and energy in two areas to ensure the success of a personnel management software implementation. The first step is testing, which, if done correctly, may disclose previously unknown issues that may be addressed before going live. The second aspect is training to ensure that the current personnel can reap the benefits of effective adoption and use. Such factors impede market expansion.

Impact of COVID–19 on the Workforce Management Market

During the COVID-19 epidemic, the workforce management market grew steadily. The pandemic has compelled businesses all over the world to establish remote operations and implement an effective management system for their staff in order to boost productivity and sustain the growth of their firm during the pandemic. Furthermore, numerous workforce management organizations have released innovative solutions for effective human resource management solutions in businesses. The introduction of innovative products and services has aided firms in mitigating the effects of the COVID-19 epidemic. Introduction of Workforce Management

Workforce management systems are used to design best-fit schedules for employees, track time and attendance, and manage employee absence and leave. The manufacturing industry has embraced workforce management systems because they enable them to monitor employee productivity and manage and retain high-performing staff. As a result, the workforce management market is expected to develop dur...

This web map shows the Hong Kong Labour Force Distribution by Sex by Small TPU in 2021. It is a subset of the 2021 Population Census made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

Workforce Management Market size was valued at USD 7.69 in 2023 and is projected to reach USD 12.63 Billion by 2031, growing at a CAGR of 7.06% from 2024 to 2031.

Key Market Drivers:
Rise in Remote and Hybrid Work Models: The shift to remote work, accelerated by the COVID-19 pandemic, has created a surge in demand for WFM solutions. According to a 2023 U.S. government labor report, 58% of employees have the option to work remotely part-time, necessitating flexible workforce management tools to handle scheduling and time-tracking challenges.
Increased Focus on Employee Productivity: Organizations are investing in WFM to boost employee efficiency and reduce labor costs. Data from the Bureau of Labor Statistics (BLS) reveals that companies that effectively manage their workforce see 20% higher productivity rates, highlighting the financial benefits of WFM solutions.

The Small Area Labour Markets publication presents regional estimates of unemployment, labour force and the unemployment rate for the Australian Statistical Geography Standard (ASGS) ABS Statistical Area Level 2s (SA2s) and Local Government Areas (LGAs). This dataset is provided by Department of Jobs and Small Business.

This table contains annual figures and quarterly figures on the connection with the labor market of the Dutch population aged 15 to 65 by gender, age and level of education. The population is divided between the employed, the unemployed and the non-working population. The non-working population is again subdivided into persons who want to work twelve hours or more, but have not sought or were not available, persons who do not want to work and persons who are unable to work. The classification indicates the degree of attachment to the labor market. The bond is greatest for the employed labor force, and the smallest for people who are unable to work. Data available from 2000 to 2014 Status of the figures: The figures in this table are final. Changes as of February 26, 2015 None, this table has been discontinued. Changes as of February 13, 2015: The quarterly figures for the fourth quarter of 2014 and the annual figure for 2014 have been added. When will new numbers come out? This table has been discontinued. The update of February 13, 2015 was the last update of this table. On 26 February 2015, new revised tables on the labor force were published. This revision of labor force statistics has two parts. The definitions have been adapted to the internationally agreed definitions and the data collection has been improved by being the first statistics office in Europe to conduct surveys via the internet. For more information on the revision, see the link to the press release in section 3.

The lowest female employment rate worldwide was found in Iraq, reaching only eight percent. The Palestinian Territories and Sudan followed behind. A high number of the countries on the list are predominantly Muslim. Bahamas had the highest female employment rate in the world.

The dataset exists to observe the entrepreneurial activity of Austin over a long time period. The data comes from the U.S. Census County Business Pattern table and is capturing data at the Travis County level. It contains the cumulative count of firms by employee size and count of firms by employee size by industry. This data can be used to see changes of employer growth by industry; to project where workforce growth could be occurring; or to simply see how many small businesses there are in Austin.

View more details and insights related to this data set on the story page: data.austintexas.gov/stories/s/ndb5-si22