As of January 2024, the median number of years (tenure) that wage and salary workers in the manufacturing industry had been with their current employer was *** years in the United States. Workers in the education and health services industry had a median of *** years with their current employer. Employee tenure is a measure of how long wage and salary workers have been with their current employer. Data on employee tenure can be used as a gauge of employment security, where an increase in tenure means improving security, and a decrease in tenure shows deteriorating security.

Number of employed persons by job tenure, North American Industry Classification System (NAICS) and gender.

This statistic shows the average job tenure of employees in a Canada in 2022, distinguished by major industry. In 2022, the average job tenure for Canadian employees in agriculture stood at ***** months.

Number of employed persons by job tenure, National Occupational Classification (NOC) and sex, last 5 years.

In 2023, the average tenure of contract and temporary employees in the United States was **** weeks. Tenure is defined as the duration of employment with the staffing firm.

Employees in Greece spend an average of 13 years with their employers as of 2023, the longest average job tenure among European countries. Among the provided countries, Denmark had the shortest average job tenure, at 7.5 years. In most European countries, men spend more time with a single employer on average than women. Notable exceptions to this trend come from a number of post-communist countries in central and eastern Europe - Romania, Bulgaria, Lithuania, Latvia, and Estonia-.

Number of employed persons by job tenure, type of work (full- and part-time employment), gender, and age group, annual.

In 2024, approximately ** percent of respondents stated that they have been working in the cleaning/maintenance department within facility management in the United States for four to seven years. This is roughly the same when compared to 2023.

• administrative data • age dependency ratio • age groups • age structure • agriculture • armed force • average salary • average tenure s • average wages • births • broad economic activities • civil employment • civilian labour • collective dismissals • constant prices • current prices • death rates • dependent employment • discouraged workers • dismissals • duration of unemployment • earning-dispersion measures • earnings • employee density • employee turnover • employee union • employees • employment • employment protection legislation • employment ratio • employment status • exchange rate • finance • full-time • full-year equivalent employee • gender • gross earnings • health • incidents • independent workers • industry • Involuntary part time workers • job tenure • jobs • labour • labour force • labour force forecasts • labour market • labour market fluidity • labour market programmes • labour regulation • low pay incidence • median wages • membership • migration rates • minimum wages • national legislation • natural increase rates • pension age • population • population baseline • population estimates females • population projections • PPP • professional status • real estate • rigidness • salary earner ratio • salary earners • self-employed • services • short-time workers • standardised age groups • statistics • strict regulation • strictness of legislation • survey data • synthetic indicators • total employment • total increase rates • trade union • trade union members • transport • turnover rate • unemployment • union members • unpaid family workers • unpaid workers • vital statistics • weekly hours • working age ratio

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High)

Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

This dataset includes six tables which were custom ordered from Statistics Canada. All tables include commuting characteristics (mode of commuting, duration/distance), labour characteristics (employment income groups in 2015, Industry by the North American Industry Classification System 2012), and visible minority groups. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Place of Work (POW), Census Tract (CT) within CMA Vancouver. The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. However, it will be provided upon request. GNR values for POR and POW are different for each geography. Universe: The Employed Labour Force having a usual place of work for the population aged 15 years and over in private households that are rented (Tenure rented), full year-full time workers (40-52weeks) Variables: Visible minority (15) 1. Total - Visible minority 2. Total visible minority population 3. South Asian 4. Chinese 5. Black 6. Filipino 7. Latin American 8. Arab 9. Southeast Asian 10. West Asian 11. Korean 12. Japanese 13. Visible minority, n.i.e. 14. Multiple visible minorities 15. Not a visible minority Commuting duration and distance (18) 1. Total - Commuting duration 2. Less than 15 minutes 3. 15 to 29 minutes 4. 30 to 44 minutes 5. 45 to 59 minutes 6. 60 minutes and over 7. Total - Commuting distance 8. Less than 1 km 9. 1 to 2.9 km 10. 3 to 4.9 km 11. 5 to 6.9 km 12. 7 to 9.9 km 13. 10 to 14.9 km 14. 15 to 19.9 km 15. 20 to 24.9 Km 16. 25 to 29.9 km 17. 30 to 34.9 km 18. 35 km or more Main mode of commuting (7) 1. Total - Main mode of commuting 2. Driver, alone 3. 2 or more persons shared the ride to work 4. Public transit 5. Walked 6. Bicycle 7. Other method Employment income groups in 2015 (39) 1. Total – Total Employment income groups in 2015 2. Without employment income 3. With employment income 4. Less than $30,000 (including loss) 5. $30,000 to $79,999 6. $30,000 to $39,999 7. $40,000 to $49,999 8. $50,000 to $59,999 9. $60,000 to $69,999 10. $70,000 to $79,999 11. $80,000 and above 12. Median employment income ($) 13. Average employment income ($) 14. Total – Male Employment income groups in 2015 15. Without employment income 16. With employment income 17. Less than $30,000 (including loss) 18. $30,000 to $79,999 19. $30,000 to $39,999 20. $40,000 to $49,999 21. $50,000 to $59,999 22. $60,000 to $69,999 23. $70,000 to $79,999 24. $80,000 and above 25. Median employment income ($) 26. Average employment income ($) 27. Total – Female Employment income groups in 2015 28. Without employment income 29. With employment income 30. Less than $30,000 (including loss) 31. $30,000 to $79,999 32. $30,000 to $39,999 33. $40,000 to $49,999 34. $50,000 to $59,999 35. $60,000 to $69,999 36. $70,000 to $79,999 37. $80,000 and above 38. Median employment income ($) 39. Average employment income ($) Industry - North American Industry Classification System (NAICS) 2012 (54) 1. Total - Industry - North American Industry Classification System (NAICS) 2012 2. 11 Agriculture, forestry, fishing and hunting 3. 21 Mining, quarrying, and oil and gas extraction 4. 22 Utilities 5. 23 Construction 6. 236 Construction of buildings 7. 237 Heavy and civil engineering construction 8. 238 Specialty trade contractors 9. 31-33 Manufacturing 10. 311 Food manufacturing 11. 41 Wholesale trade 12. 44-45 Retail trade 13. 441 Motor vehicle and parts dealers 14. 442 Furniture and home furnishings stores 15. 443 Electronics and appliance stores 16. 444 Building material and garden equipment and supplies dealers 17. 445 Food and beverage stores 18. 446 Health and personal care stores 19. 447 Gasoline stations 20. 448 Clothing and clothing accessories stores 21. 451 Sporting goods, hobby, book and music stores 22. 452 General merchandise stores 23. 453 Miscellaneous store retailers 24. 454 Non-store retailers 25. 48-49 Transportation and warehousing 26. 481 Air transportation 27. 482 Rail transportation 28. 483 Water...

Reductions in job requisitions from clients emerged as the primary challenge for global staffing firms in 2025, with ** percent of surveyed professionals citing it as their main concern. This underscores the increasingly competitive landscape for skilled workers, even as economic uncertainty looms large. The persistent struggle to find qualified candidates highlights a growing mismatch between available talent and industry demands. Economic uncertainty and industry growth While talent scarcity tops the list of challenges, ** percent of respondents pointed to economic uncertainty as a significant hurdle. This concern comes despite the U.S. staffing and recruiting industry's robust growth, with sales reaching approximately *** billion U.S. dollars in 2022, an increase of ** billion from the previous year. The industry's resilience is further evidenced by the success of global leaders like Randstad. The revenue of Randstad was **** billion U.S. dollars in 2023.
Evolving workforce dynamics The staffing industry is adapting to shifting workforce trends, with 16 percent of professionals noting the transition to non-traditional work arrangements as a challenge. This shift is reflected in the average tenure of contract and temporary employees in the United States, which stood at 10 weeks in 2022. Additionally, ** percent of respondents highlighted the need for reskilling workers, indicating a growing emphasis on workforce development to meet changing market demands. The staffing industry revenue worldwide may be a reflection of this, as its turnover decreased between 2022 and 2023. As the industry navigates these challenges, it continues to play a crucial role in bridging the gap between employers and job seekers in an increasingly dynamic labor market.

This statistic shows the monthly average change in private sector jobs by presidential tenure, from the month preceding their first full month in office to last month in office in the United States in the post-war era. The monthly average change in jobs was highest when Bill Clinton was U.S. president. An average of 214,000 jobs were created each month of his presidency.