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.

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, North American Industry Classification System (NAICS) and gender.

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

As of January 2024, the median number of years (tenure) that wage and salary workers had been with their current employer was *** years in the United States. This is a decrease from 2022, when the median tenure was also *** years. 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.

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.

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

Job tenure by professional status and occupation

The "Quality of employment" framework developed under the lead of UNECE (United Nations Economic Commission for Europe) represents a neutral and comprehensive approach to assess quality of employment in its multiple facets. It defines 68 indicators on seven dimensions that address employment quality from the perspective of the employed person. Its design also facilitates international comparison. For statistical institutes, researchers and policy users looking to build and analyse datasets using these indicators, the framework is explained in a Handbook on measuring quality of employment published by UNECE. Using the UNECE framework, Eurostat has compiled data on employment quality for the EU countries that is provided in the Eurostat database.

LFS in one of the sources which provides data for filling some of the indicators. The section 'Quality of employment' reports annual results from the EU-LFS concerning some of those indicators.

In particular:

• Long working hours in main job: percentage of employed persons usually working 49 hours or more per week;
• Weekly working hours: Average number of usual weekly working hours of employed persons;
• Work on weekends: Percentage of employed persons usually working at Saturday or Sunday;
• Job tenure Percentage of employed persons by duration of employment with current employer by number of years;
• Temporary employment agency workers: Percentage of employed persons working for a temporary work agency;
• Precarious employment: Percentage of employees with a short-term contract of up to 3 months.

More information on Eurostat indicators about Quality of employment is available on the Quality of employment webpage.

General information on the EU-LFS can be found in the ESMS page for 'https://ec.europa.eu/eurostat/cache/metadata/en/employ_esms.htm" target="_parent">Employment and unemployment (LFS). Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

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

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 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.

Employee Attrition and Factors

Examining Performance, Financials, and Job Role for Impact on Retention

By [source]

About this dataset

This dataset offers a comprehensive and varied analysis of an organization's employees, focusing on areas such as employee attrition, personal and job-related factors, and financials. Included are numerous parameters such as Age, Gender, Marital Status, Business Travel Frequency, Daily Rate of Pay, Departmental Information such as Distance From Home Office or Education Level Obtained by the employee in question. Also included is a variant series of parameters related to the job being performed such as Job Involvement (level), Job Level (relative to similar roles within the same organization), Job Role specifically meant for that individual(function/task), total working hours in a week/month/year be it overtime or standard hours for a given role. Furthermore detailed aspects include Percent Salary Hike during their tenure with the company from promotion or otherwise , Performance Rating based on specific criteria established by leadership , Relationship Satisfaction among peers at workplace but also taking into account outside family members that can influence stress levels in varying capacities ,Monthly Income considered at its starting point once hired then compared against their monthly payrate with overtime hours included if applicable along with Number Companies Worked before if any. Lastly the Retirement Status commonly known as Attrition is highlighted; covering whether there was an intent to stay with one employer through retirement age or if attrition took place for reasons beyond ones control earlier than expected . Through this dataset you can get an insight into various major aspect regarding today's workforce management philosphies which have changed drastically over time due to advancements in technology

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How to use the dataset

• Understand the variables that make up this dataset. The dataset includes several personal and job-related variables such as Age, Gender, Marital Status, Business Travel, Daily Rate, Department, Distance From Home, Education, Education Field, Employee Count, Employee Number, Environment Satisfaction Hoursly Rate and so on. Knowing what each variable is individuallly will help when exploring employee attrition as a whole.
• Analyze the data for patterns as well as outliers or anomalies either at an individual level or across all of the data points together. Identifying these patterns or discrepancies can offer insight into factors that are related to employee attrition.
• Visualize the data using charts and graphs to allow for easy understanding of which relationships might be causing higher levels of employees leaving the organization over time dimensions like age or job role can be key factors in employee attrition rates visually displaying how they relate to one another can provide clarity into what needs to change within an organization in order to reduce attrition rates
• Explore relationships between pairs of variables through correlation analysis correlations are measures of how strongly two variables are related when looking at employment retention it’s important to analyze correlations at both an individual level and for all variables together showing which pairings have more influence than others when it comes to influencing employee decisions
  5 Use descriptive analytics methods such as scatter plots histograms boxplots etc with aggregated values from each field like average age average monthly income etc These analytics help gain a deeper understanding about where changes need to be made internally
  6 Utilize predictive analytics with more advanced techniques such as regressions clustering decision trees in order identify trendsfrom past data points then build models on those insights from different perspectives helping further prepare organizations against potential high levelsinvolving employees departing ?

Research Ideas

• Identifying performance profiles of employees at risk for attrition through predictive analytics and using this insight to create personalized development plans or retention strategies.
• Using the data to assess the impact of different financial incentives or variations in job role/structure on employee attitudes, satisfaction and ultimately attrition rates.
• Analyzing different age groups' responses to various perks or turnover patterns in order to understand how organizations can better engage different demographic segments

Acknowledgements

If you use this dataset in your research, pl...

Average productivity loss and average cost of productivity loss by work type, job tenure, gender and age group–primary analysis of a US manufacturing company.

Abstract copyright UK Data Service and data collection copyright owner.

The OECD Employment and Labour Market Statistics provide detailed annual information the employment and labour market for the period 1950/60 onwards for all OECD countries (where data is available).
The OECD Employment and Labour Market Statistics includes a range of annual labour market statistics and indicators broken down by sex and age as well as information about part-time and short-time workers, job tenure, hours worked, unemployment duration, trade union, employment protection legislation, minimum wages, labour market programmes for OECD countries and non-member economies.
These data were first provided by the UK Data Service in February 2015.

Main Topics:

• 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

Dataset Description: The dataset contains information about employees from an organization, including their performance, workplace behavior, and other key factors that may influence employee attrition. It is used to predict whether an employee will leave the company based on several relevant attributes. The features in this dataset provide valuable insights into the workforce, allowing for predictive modeling to understand the likelihood of employee turnover Features Explanation:

1. Satisfaction Level: Description: This represents the employee’s self-reported job satisfaction level. Value Range: A float between 0 and 1, where 0 indicates very low satisfaction, and 1 indicates very high satisfaction.

2. Last Performance Rating: Description: The most recent performance evaluation score of the employee. Value Range: A float between 0 and 1, where 0 represents the lowest performance, and 1 represents the highest.

3. Number of Projects: Description: The total number of projects the employee has worked on during their time at the company. Value Range: An integer, with higher numbers indicating more projects handled by the employee.

4. Average Monthly Hours: Description: The average number of hours the employee works in a month. Value Range: A continuous integer value, reflecting work hours per month.

5. Years at Company: Description: The number of years the employee has worked at the company. Value Range: A continuous float, with higher values indicating longer tenure.

6. Had Work Accident: Description:** Indicates whether the employee has had a workplace accident. Value Range: A binary value (0 or 1), where 0 means no accident and 1 means the employee had at least one accident.

7. Promoted in Last 5 Years: Description: Reflects whether the employee received a promotion in the last five years. Value Range:A binary value (0 or 1), where 0 indicates no promotion, and 1 indicates the employee was promoted.

8. Department: Description: The department in which the employee works. Value Range: Encoded as an integer, where different numbers correspond to different departments: 0: Sales 1: Support 2: Technical 3: HR 4: Accounting 5: Management 6: IT 7: Marketing 8: Research and Development (RandD) 9: Product Management

9. Salary: Description: The salary level of the employee. Value Range: Encoded as an integer: 0: Low salary 1: Medium salary 2: High salary

10. Will Left or Not (Target Feature): Description: Indicates whether the employee has left the company. Value Range: A binary value (0 or 1), where 0 means the employee stayed, and 1 means the employee left.

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.

Этот набор данных содержит данные о среднем сроке службы работников с разбивкой по интервалам пребывания на работе (в годах). Данные представлены в разбивке по профессиональному статусу - работники, общей занятости – полу, пятилетним и широким возрастным группам (15-24, 25-54, 55-64, 15-64, всего и т.д.). Продолжительность трудового стажа определяется продолжительностью пребывания работника на своем текущем или основном месте работы или у своего нынешнего работодателя и выражается в годах. Их можно перевести в месяцы, умножив на коэффициент 12 (месяцы). Эта информация полезна для оценки степени текучести кадров на рынке труда и определения сфер экономической деятельности, в которых наблюдается быстрая текучесть кадров. На данный момент данные представлены по ряду европейских стран и будут расширены, чтобы охватить большее число стран. Для облегчения анализа и сравнений во времени были предоставлены исторические данные по странам - членам ОЭСР за максимально длительный период, часто даже до того, как та или иная страна стала членом Организации. Информацию о датах вступления всех стран в ОЭСР можно найти по ссылке Даты ратификации ОЭСР. Информацию по всем странам и всем субъектам ОРС можно найти в прикрепленном файле LFS_NOTES_SOURCES. Это ансамбль костюмов, состоящий из костюмов для юных танцовщиц, которые трудятся в промежутках между танцами (в анне). Люди, которые занимаются вентиляцией по статусу, - это работники разного пола, пятилетние и большие группы по возрасту (15-24, 25-54, 55-64, 15-64, всего и т.д.). История - это история о том, как в течение долгого времени трудились люди, занимающие и работающие на самом деле, и руководитель, и работник, занимающийся на самом деле работой, и как проходит срок годности. Это может быть конвертация в деньги и умножение на фактическое число 12 (mois). Эта информация используется для оценки уровня текучести на рынке труда и для определения областей экономической деятельности, поскольку ротация основных направлений развития происходит быстро или не происходит. Люди, которые являются всего лишь постоянными докладчиками по определенным вопросам, связанным с выплатой заработной платы в Европе, и по ряду других вопросов, связанных с большим количеством выплат. В качестве посредника в проведении статистического анализа и сравнений по временным показателям, мы используем усилия по представлению временной информации, а также, возможно, более длительную информацию для оплаты членских взносов. Я просто хочу, чтобы ты был первым представителем высокопоставленных лиц, корреспондентом отдела по связям с общественностью в организации. О том, как точно определить дату, когда будут выплачены различные взносы в бюджет, и о том, как вести себя с окружающими : Даты выхода в Открытый КОСМОС. Для получения информации от подразделений по расследованию деятельности населения, которые платят больше, и для консультирования по финансовым вопросам LFS_NOTES_SOURCES. This dataset contains data on the average job tenure of workers by job tenure intervals (in years). Data are broken down by professional status - employees, total employment – sex, five-year and broad age groups (15-24, 25-54, 55-64, 15-64, total, etc.). Job tenure is measured by the length of time workers have been in their current or main job or with their current employer and are expressed in numbers of years. They can be converted in months by multiplying by a factor of 12 (months). This information is valuable for estimating the degree of fluidity in the labour market and in identifying the areas of economic activity where the turnover of labour is rapid or otherwise. Data are so far reported for a number of European countries and will be expanded to cover a greater number of countries. In order to facilitate analysis and comparisons over time, historical data for OECD members have been provided over as long a period as possible, often even before a country became a member of the Organisation. Information on the membership dates of all OECD countries can be found at OECD Ratification Dates. Information for all countries and all LFS subjects may be found in the attached file LFS_NOTES_SOURCES. Cet ensemble de données contient des données sur l'ancienneté moyenne des travailleurs par intervalles d'ancienneté (en années). Les données sont ventilées par statut professionnel - salariés, emploi total - sexe, quinquennat et grands groupes d'âge (15-24, 25-54, 55-64, 15-64, total, etc.). L'ancienneté est mesurée par la durée pendant laquelle les travailleurs occupent leur emploi actuel ou principal ou travaillent pour leur employeur actuel et est exprimée en nombre d'années. Elle peut être convertie en mois en multipliant par un facteur de 12 (mois). Ces informations sont utiles pour estimer le degré de fluidité du marché du travail et pour identifier les domaines d'activité économique où la rotation de la main-d'œuvre est rapide ou non. Les données sont jusqu'à présent rapportées pour un certain nombre de pays européens et seront étendues pour couvrir un plus grand nombre de pays. Afin de faciliter les analyses statistiques et les comparaisons dans le temps, l'OCDE s'efforce de présenter la série temporelle la plus longue possible pour chaque pays membre. C'est ainsi que sont souvent présentées des données correspondant à des périodes antérieures à l'adhésion du pays à l'Organisation. On trouvera des précisions sur la date exacte d'adhésion des différents pays de l'OCDE à l'adresse suivante : Dates d'adhésion à l'OCDE. Pour des informations détaillées sur les enquêtes sur la population active pour tous les pays, veuillez consulter le fichier LFS_NOTES_SOURCES.

This is a cleaned and analyzed version of the City of Houston Employee Payroll dataset, specifically focused on withdrawn employees and their financial impact on city departments. This dataset was prepared in response to a City Council Finance Committee request for January 2025 withdrawal analysis.

This dataset transforms the raw payroll data into actionable insights by: - Aggregating data by department and analysis categories - Calculating total financial impact across all compensation types - Computing average salaries, pay grades, and tenure metrics - Providing headcount loss by department - Breaking down impacts by employment type, FLSA status, and pay grade categories

Key Metrics Included: - Headcount_Lost: Number of withdrawn employees per department - Total_Base_Salary_Impact: Cumulative base salary of withdrawn employees - Total_Gross_Pay_Impact: Total gross compensation impact - Total_Overtime_Impact: Overtime pay associated with withdrawn positions - Total_Other_Pay_Impact: Additional compensation impacts - Avg_Annual_Salary: Average salary of withdrawn employees - Avg_Tenure_Years: Average years of service before withdrawal - Pct_Of_Total_Financial_Impact: Percentage contribution to overall fiscal impact

Analysis Sections: 1. OVERALL SUMMARY: City-wide totals and averages 2. DEPARTMENT ANALYSIS: Department-by-department breakdown showing Houston Public Works was most impacted (15 withdrawals, $637,550 base salary impact) 3. Category breakdowns by Employment Type, FLSA Status, and Pay Grade

Use Cases: - Budget planning and reallocation decisions - Workforce retention strategy development - Department-level resource planning - Understanding compensation patterns in workforce attrition - City Council presentations and policy discussions

Data Processing: - Filtered for "Withdrawn" status employees only - Calculated financial impacts across multiple compensation categories - Aggregated by relevant categorical dimensions - Computed tenure and demographic statistics - Anonymized per City of Houston data protection policies

Context: Prepared for City Council Finance Committee presentation (November 2025) analyzing the fiscal and operational impact of January 2025 employee withdrawals across City of Houston departments.

Data Source: City of Houston Open Data Portal - Employee Payroll Database Analysis Date: November 2025

Turnover among U.S. childcare workers was about 65% higher than turnover in the median occupation in 2022, which creates challenges for the broader workforce, according to a new report from the Federal Reserve Bank of Cleveland.