Between 2023 and 2027, the majority of companies worldwide expect major changes to their workforce strategies. Those surveyed stated that 81 percent would invest in learning and training on the job. 80 percent said that they would accelerate the automation of processes, and 13 percent said they would reduce the current workforce significantly.

A summary of metrics to understand changes within occupations between 2016 and 2021, with a look at movements in and out of the workforce in addition to workforce demographics.

This statistic depicts the share of individuals in the United States who claim to change jobs every one to five years between 2016 and 2018. During the 2018 survey, 51 percent of respondents stated they change jobs every one to five years.

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 2019, a Statista study on labor shortages showed that between 2019 and 2030, there is a total of 4.05 million net change expected in the supply of workers with higher education in the United States. Similarly, a net change of 8.68 million is excepted in the supply of workers in Mexico, which is the highest change seen in the selected countries' workforces.

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The global meal vouchers and employee benefits solutions market size reached USD 27.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 41.9 Billion by 2033, exhibiting a growth rate (CAGR) of 4.66% during 2025-2033. The increasing emphasis on employee well-being and satisfaction, changing workforce demographics and preferences, rapid technological advancements, and implementation of supportive government policies are some of the major factors propelling the market.

IMARC Group provides an analysis of the key trends in each segment of the global meal vouchers & employee benefits solutions market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on product type.

Financial Wellness Benefits Market is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Financial Wellness Benefits Market Drivers

The market drivers for the Financial Wellness Benefits Market can be influenced by various factors. These may include:

Initiatives for Employee Well-Being: Companies are realising the value of promoting their workers’ overall wellbeing, including their financial stability. Providing financial wellness benefits can increase job satisfaction, productivity, and retention rates while also showing a commitment to the health of your workforce.
Increasing Financial Stress: Debt, insufficient savings, and economic instability are some of the major issues that contribute to financial stress, which is a global problem affecting people. Financial wellness benefits are becoming more widely available as part of larger employee assistance programmes as a result of employers realising the negative effects that financial stress has on worker performance and morale.
The retirement landscape: is changing as defined contribution retirement plans gain popularity and traditional pension plans shrink, placing more responsibility on individuals to save for their retirement. Employees may better manage the complexity of retirement planning and safeguard their financial future with the support of financial wellness benefits like savings matching programmes and retirement planning guidance.
Changing Workforce Demographics: A variety of generations are working together in the workplace, each with their own set of financial difficulties, and the workforce is growing more diverse. Benefits related to financial wellbeing can be customised to meet the demands of various demographic groups, such as Baby Boomers approaching retirement, Gen Xers balancing work and family obligations, and Millennials struggling with student loan debt.
Demand for Comprehensive Benefits Packages: Modern workers look to their employers for more than just a paycheck; they want benefits that cover all aspects of their health, including their financial well-being. Companies that provide comprehensive benefits for financial wellness have an advantage over their competitors in luring and keeping top talent.
Growing Recognition and Education: The demand for financial wellness benefits is driven by a growing recognition of the significance of financial literacy and education. In order to provide their staff the financial knowledge they need to make wise decisions, employers are funding programmes that teach them about debt management, investing, saving, and budgeting.
Healthcare Cost Containment: Employers and employees are under pressure as a result of growing healthcare expenses. Financial wellness benefits, such flexible spending accounts (FSAs) and health savings accounts (HSAs), give companies cost-saving options while assisting employees in managing their healthcare costs.
Regulatory Requirements: Employers are encouraged to give financial wellness efforts top priority by regulatory developments, such as the addition of financial wellness benefits to retirement plan requirements and fiduciary standards. The implementation of comprehensive financial wellness programmes is driven by regulatory compliance.
Remote Work and Flexible Work Schedules: The COVID-19 epidemic has expedited the transition to remote work and flexible work schedules, which emphasises the value of anytime, anywhere digital financial wellness solutions. In order to provide financial wellness services to remote and dispersed workforces, employers are investing in mobile apps and web platforms.
Corporate Social Responsibility (CSR): CSR programmes focus on the welfare of employees in addition to environmental sustainability. Providing financial wellness benefits strengthens the employer brand, attracts investors and consumers who care about social issues, and is in line with CSR goals.

Between 2021 and 2030, the highest growth in the Italian labor force will be among the population aged over 65 years (6.6 percent). On the contrary, the work force among the population aged 25 to 49 years is estimated to drop by 0.7 percent.

Italy's has, indeed, one of the oldest populations in the world. Its median age is forecasted to increase steadily and the number of births has been dropping constantly.

Users need to be aware of intended changes to the presentation of these statistics. For further information please read the "revisions and issues section" on page five of this month's bulletin. Provisional monthly figures for headcount, full time equivalent, role count and turnover of NHS Hospital and Community Health Service (HCHS) staff groups working in England (excluding primary care staff). As expected with provisional statistics, some figures may be revised from month to month as issues are uncovered and resolved. No refreshes of the provisional data will take place either as part of the regular publication process, or where minor enhancements to the methodology have an insignificant impact on the figures at a national level. However, the provisional status allows for this to occur if it is determined that a refresh of data is required subsequent to initial release. Where a refresh of data occurs, it will be clearly documented in the publications. The monthly publication is an accurate summary of the validated data extracted from the NHS's HR and Payroll system. It has a provisional status as the data may change slightly over time where trusts make updates to their live operational systems. Given the size of the NHS workforce and the changing composition, particularly during this period of transition, it is likely that we will see some additional fluctuations in the workforce numbers over the next few months reflecting both national and local changes as a result of the NHS reforms. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating 'Monthly HCHS Workforce' as the subject heading, via enquiries@hscic.gov.uk or 0845 300 6016

Bank of America's workforce has undergone a significant shift in racial diversity over the past six years. The share of white employees decreased from 53.2 percent in 2019 to 47.2 percent in 2024, marking a notable change in the company's demographic composition. Meanwhile, the representation of Hispanic, Asian, and Black racial groups grew steadily. The second-largest racial group in the observed period was Hispanic, whose share increased from 17.9 to 19.2 percent.

Workforce changes, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.

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This project is integrating scientific research in the Arctic with education and outreach, with a strong central focus on engaging undergraduate students and visiting faculty from groups that have had little involvement in Arctic science to date. Science and society in the United States will be stronger in the long-term if the scientific workforce more closely reflects the racial, ethnic, and cultural diversity of its residents. The Arctic research community currently does not. Of the Principal Investigators funded by NSF's Arctic programs in the past five years, only 1% were African American, Hispanic, Native American, or Alaska Native. This project is catalyzing change in these demographics by engaging faculty from Minority Serving Institutions (MSIs) and a diverse group of undergraduate students in cutting-edge Arctic research and providing them encouragement, mentoring, and opportunities to continue pursuing Arctic studies in subsequent years. The central element of the project is a month-long research expedition to the Yukon River Delta in Alaska. The expedition provides a deep intellectual and cultural immersion in the context of an authentic research experience that is paramount for "hooking" students and keeping them moving along the pipeline to careers as Arctic scientists. The overarching scientific issue that drives the research is the vulnerability and fate of ancient carbon stored in Arctic permafrost (permanently frozen ground). Widespread permafrost thaw is expected to occur this century, but large uncertainties remain in estimating the timing, magnitude, and form of carbon that will be released when thawed. Project participants are working in collaborative research groups to make fundamental scientific discoveries related to the vulnerability of permafrost carbon in the Yukon River Delta and the potential implications of permafrost thaw in this region for the global climate system.

Background The nature of primary care provision is changing. GPs and other staff providing primary care are no longer based solely in general practices but may work in a range of other “settings”, for example providing extended hours, GP streaming in Accident and Emergency (A&E) departments, and Out-of-Hours services. There is an increasing need to understand the different ways in which GPs and their colleagues are providing primary care services. This is a complicated and ever-changing area. Most GPs work in general practices. Information about GPs and other practice-based staff is provided directly to NHS Digital on a quarterly basis by the GP practices which submit record-level data via the National Workforce Reporting System (NWRS). Information about these individuals and their associated workforce are published in General Practice Workforce statistics (https://digital.nhs.uk/data-and-information/publications/statistical/general-and-personal-medical-services). Similarly, information about GPs and other healthcare professionals directly employed by hospital trusts should be captured and included in NHS Workforce Statistics (https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics). Additional work is required to identify which parts, if any, of this activity can reasonably be classified as primary care provision. In addition, where available, details of individuals providing NHS funded care in the independent sector are captured and reported in NHS Digital’s Independent Healthcare Provider Workforce Statistics (https://digital.nhs.uk/data-and-information/publications/statistical/independent-healthcare-provider-workforce-statistics). However, there remains an uncertain number of GPs and other healthcare professionals that are providing patient care in these alternative settings and whose information, including details of their working hours, is not collected. As understanding the entirety of the healthcare workforce, both NHS and independent sector, is crucial to meeting the needs of patients and vital for workforce planning, we have been working to better understand the nature of healthcare provision, and in particular, the scale and extent of GP provision outside the more traditional settings. The number of service providers in these alternative settings – which are not necessarily NHS organisations – is large and services are commissioned differently in each CCG making it difficult to identify GPs and to collect accurate and complete workforce data. We are working closely with key stakeholders including Department of Health and Social Care, NHS England and NHS Improvement and Health Education England to explore the best way to collect more accurate and complete data for this part of the GP workforce. This is likely to include reviewing whether sufficient improvements could be made to this quarterly collection to enhance the data quality, as well as considering whether it would be feasible, affordable or preferable to collect record-level data directly from providers. These are new and experimental statistics which are under development. We welcome feedback from users to help us evaluate their suitability and quality. Please send any comments to PrimaryCareWorkforce@nhs.net including “GPs in Alternative Settings” in the subject line. Your feedback about these experimental statistics will help us evaluate their usefulness and inform our future plans. While the experimental statistics designation should not be taken to indicate that the statistics are of poor quality, there are nonetheless a number of data quality considerations that affect the levels of confidence that may be bestowed upon the figures and users are advised to consult the Data Quality section of this release.

The COVID-19 pandemic has significantly affected the global workforce, presenting unprecedented challenges to managers and practitioners of strategic human resource management. Pandemic-influenced changes in the employment relationship highlighting the need for adaptation in order to facilitate a return to pre-pandemic conditions. Crises such as this can have a detrimental effect on employees’ psychological contract, which in turn can hinder the organization’s ability to thrive in the post-COVID-19 era and impede the development of high commitment levels in the aftermath of the crisis. Emotional intelligence plays an increasingly vital role in effectively navigating the crisis and providing support to employees, while also facilitating the reconstruction of the psychological contract. Therefore, this study aims to explain the role of emotional intelligence of strategic human resource management practitioners on affective organizational commitment and the possible mediating effect of the psychological contract in that relationship. A quantitative study took place in February 2023 among 286 HR directors, HR managers, and HR officers in higher education institutions in Georgia. Partial Least Squares for Structural Equation Modelling was applied for data analysis. The results revealed that the emotional intelligence of strategic human resource management practitioners has a positive impact on the psychological contract and the affective organizational commitment. This study supports the idea that emotional intelligence can transform strategic human resource management practitioners into individuals who engage in people-orientated activities. These activities aim to effectively acquire, utilize, and retain employees within an organization. The study also suggests that emotional intelligence can provide solutions to maintain high employee commitment during times of crisis and in the aftermath of unprecedented situations.

This table contains quarterly and annual figures on labor participation in the Netherlands. The population aged 15 to 75 (excluding the institutional population) is divided into the employed, unemployed and non-labour force. The employed labor force is further subdivided on the basis of position in the workplace and average working hours. A breakdown by gender, age and level of education is available for the different classifications. Due to changes in the research design and the EBB questionnaire, a revision of the figures for the 2021 reporting year was carried out in the first quarter of 2022. The figures from 2021 are not directly comparable with the figures up to and including 2020. Data available from 2003 up to and including 2022 Status of the figures: The figures from 2021 are final. Changes as of August 17, 2022: None, this table has been discontinued. Changes as of February 15, 2022: The annual figures for 2021 and the quarterly figures for the third and fourth quarters of 2021 have been published. The figures for the first two quarters of 2021 have been revised. From 2021, the categories 'Employee permanent, no fixed hours' and 'Employee temporary, no fixed hours' will no longer apply for 'Position in the job'. From 2021 they will be counted in the category 'On call/standby worker'. In addition, a new category 'Employee flex, contract unknown' has been added from 2021. For the self-employed, the category 'other self-employed' has been abolished. When will new numbers come out? Not applicable anymore. This table is followed by the table Employed labor force; position in the workplace. See section 3.

Graph and download economic data for Labor Force Participation Rate - Men (LNS11300001) from Jan 1948 to Feb 2025 about males, participation, 16 years +, labor force, labor, household survey, rate, and USA.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Tables for Workplace Geography are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities. Tables B08601, B08602, B08603, and B08604 are also available for Place parts and County Subdivision parts for the 5-year ACS datasets..These tabulations are produced to provide estimates of workers at the location of their workplace. Estimates of counts of workers at the workplace may differ from those of other programs because of variations in definitions, coverage, methods of collection, reference periods, and estimation procedures. The ACS is a household survey which provides data that pertains to individuals, families, and households..Workers include members of the Armed Forces and civilians who were at work last week..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of ...

Abstract

National Labor Force Survey (SAKERNAS) is a survey that is designed to observe the general situation of workforce and also to understand whether there is a change of workforce structure between the enumeration period. Since the survey was initiated in 1976, it has undergone a series of changes affecting its coverage, the frequency of enumeration, the number of households sampled and the type of information collected. It is the largest and most representative source of employment data in Indonesia. For each selected household, the general information about the circumstances of each household member that includes the name, relationship to head of household, sex, and age were collected. Household members aged 10 years and over will be prompted to give the information about their marital status, education and employment.

SAKERNAS is aimed to gather informations that meet three objectives: 1.Employment by education, working hours, industrial classification and employment status, 2.Unemployment by different characteristics and efforts on looking for work, 3.Working age population not in the labor force (e.g. attending schools, doing housekeeping and others).

The data was gathered in August 2010 and covered all provinces in Indonesia with total sample of about 311.776 households, scattered on 19.486 census blocks, both in rural and urban areas. The large number of samples of SAKERNAS 2010 makes it possible for the data to be analyzed at district level. The main household data is taken from core questionnaires SAK10-AK.

Geographic coverage

National coverage*, including urban and rural area, representative until district/city level.

*) Although covering all of Indonesia, there are some circumstances when not all provincial were covered. For example, in 2000, the Province of Maluku excluded in SAKERNAS because horizontal conflicts occurred there. Also, the separation of East Timor from Indonesia in 1999 also changed the scope of SAKERNAS for the years to come. After that, due to the expansion of regional autonomy as a consequence, the proportion of samples per Province is also changed, as in 2006 when the number of provinces are already 33. However, the difference is only on the number of influential scope/level but not to the pattern. On the other hand, changes in the methodology (including sample size) over time is likely to affect the outcome, for example in years 2000 and 2001, when sample size is only 32.384 and 34.176 households, the level of data presentation is only representative to island level, (insufficient sample size even to make it representative to provincial level).

Analysis unit

Individual

Universe

The survey covered all de jure household members (usual residents), aged 10+ years resident in the household. However, Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

Kind of data

Sample survey data

Sampling procedure

Sakernas August 2010 is implemented in the whole territory of the Republic of Indonesia with a total sample of about 311.776 households, scattered on 19.486 census blocks from all provinces, both in rural and urban areas. These 19,486 census blocks are meant to obtain data to estimate until the level of district/city. Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

The sampling method* for SAKERNAS 2010 is probability sampling with two-stage cluster sampling technique where census blocks as the primary sampling unit (PSU) and households as the ultimate sampling unit. These census blocks (PSUs) were selected with probability proportional to size. A number of households were taken randomly from selected census blocks. However, there is documentation explained about how the sample size was determined at the domain level, or stratification measures that were implemented and also the sample size allocation across strata. The sampling frame used for the 2011 and later Sakernas surveys is sample frame of Population Census 2010 (SP 2010). Sampling frame** used in Sakernas August 2010 is the list of chosen census blocks from Sakernas 2007, using the "list head of household names" result of August 2007's listing process. This sampling frame is used for sampling period 2008 to 2010 (February and August).

*) Sampling method used is varied in different years. For example, in SAKERNAS period of 1986-1989 sampling method used is the method of rotation, where most of the households selected at one period was re-elected in the following period. This often happens on quarterly SAKERNAS on that period. At other periods often use multi-stages sampling method (two or three stages depend on whether sub block census included or not), or a combination of multi stages sampling also with rotation method (e.g. SAKERNAS 2006).

**) Commonly annual SAKERNAS sample frame comes from the last population census result undertaken before SAKERNAS. For example, for annual SAKERNAS 2003 used sample frame derived from "listing process" of household results of Population Census 2000. Also can refer to sampling frame of some periodic household based cencus like Economic Census, e.g. in forming block census sample frame of SAKERNAS 2007 using Economic Census 2006 result. In the other hand sample frame used for quarterly SAKERNAS is from the list of households obtained from National Socio-Economic Survey (SUSENAS) Core activities held before Sakernas. For example, for quarterly SAKERNAS 2002/2003 activities, which used sample frame derived from the household of the selected districts of SUSENAS 2002.

Mode of data collection

Face-to-face

Research instrument

In SAKERNAS, the questionnaire has been designed in a simple and concise way. It is expected that respondents will understand the aim of question of survey and avoid the memory lapse and uninterested respondents during data collection. Furthermore, the design of SAKERNAS's questionnaire remains stable in order to maintain data comparison.

A household questionnaire was administered in each selected household, which collected general information of household members that includes name, relationship with head of the household, sex and age. Household members aged 10 years and over were then asked about their marital status, education and occupation.

Cleaning operations

Stages of data processing in Sakernas are through process of: - Batching - Editing - Coding - Data Entry - Validation - Tabulate

Sampling error estimates

Sampling error results are presented at the end of the publication of The State of Labor Force in Indonesia and in publication of The State of Workers in Indonesia.