The Job Openings and Labor Turnover Survey (JOLTS) program provides national estimates of rates and levels for job openings, hires, and total separations. Total separations are further broken out into quits, layoffs and discharges, and other separations. Unadjusted counts and rates of all data elements are published by supersector and select sector based on the North American Industry Classification System (NAICS). The number of unfilled jobs—used to calculate the job openings rate—is an important measure of the unmet demand for labor. With that statistic, it is possible to paint a more complete picture of the U.S. labor market than by looking solely at the unemployment rate, a measure of the excess supply of labor. Information on labor turnover is valuable in the proper analysis and interpretation of labor market developments and as a complement to the unemployment rate. For more information and data visit: https://www.bls.gov/jlt/

Job Openings and Labor Turnover Survey data from the U.S. Bureau of Labor Statistics

Bureau of Labor Statistics - Job Openings and Labor Turnover Survey (JOLTS) 2000-2025From the BLS:Job Openings and Labor Turnover Survey Overview PageThe Job Openings and Labor Turnover Survey (JOLTS) is a monthly survey that has been developed to address the need for data on job openings, hires, and separations.PurposeThese data serve as demand-side indicators of labor shortages at the national level. Prior to JOLTS, there was no economic indicator of the unmet demand for labor with which to assess the presence or extent of labor shortages in the United States. The availability of unfilled jobs—the job openings rate—is an important measure of the tightness of job markets, parallel to existing measures of unemployment.ScopeData from a sample of approximately 21,000 U.S. business establishments are collected by the Bureau of Labor Statistics through JOLTS Data Collection Centers in Atlanta and Kansas City. The JOLTS survey covers all nonagricultural industries in the public and private sectors for the 50 States and the District of Columbia.Data ElementsJOLTS collects data on Total Employment, Job Openings, Hires, Quits, Layoffs & Discharges, and Other Separations. For more information on the JOLTS data elements, see the JOLTS data definitions page.Reference PeriodsTotal Employment - the pay period that includes the 12th of the month.Job Openings - the last business day of the month.Hires and Separations - the entire calendar month.

A curated dataset of the most current U.S. employee turnover statistics for 2024–2025, including voluntary and total turnover rates, monthly quit rates, sector-level comparisons, job-level differences, reasons for leaving, preventability, and cost impacts. Compiled by HIGH5 from sources including Mercer, the U.S. Bureau of Labor Statistics (JOLTS), Gallup, Work Institute, and others.

Graph and download economic data for Job Openings: Accommodation and Food Services (JTS7200JOR) from Dec 2000 to Aug 2025 about accommodation, job openings, vacancy, food, services, and USA.

Graph and download economic data for Job Openings: Manufacturing (JTS3000JOL) from Dec 2000 to Aug 2025 about job openings, vacancy, manufacturing, and USA.

The United States' Bureau of Labor Statistics accounted for ***** million people working in the hospitality and leisure industry in the U.S. as of December 2024. This figure shows an increase over the previous year's figure of ***** million.

Graph and download economic data for Layoffs and Discharges: Total Nonfarm (JTSLDL) from Dec 2000 to Aug 2025 about discharges, layoffs, nonfarm, and USA.

Abstract

The Labor Turnover Survey (LTS) aims to generate quarterly data on labor turnover as indicators of labor market activity in large business enterprises. The main topics include total accession due to expansion and replacement, as well as employer-initiated and employee-initiated total separation.

The information gathered in this survey is intended to generate timely labor market signals as sound basis in planning, policy formulation and decision making in goverment, business and industry.

Geographic coverage

National capital region

Analysis unit

Enterprise

Universe

The top 25,000 corporations in the Philippines as listed by the Securities and Exchange Commission (SEC).

The universe was limited to the SEC list for two reasons: budget constraints and the decision to come up with manageable sample size that can provide DOLE officials with a quick and timely assessment of the labor market situations on a quarterly basis.

Kind of data

Sample survey data [ssd]

Sampling procedure

The enterprise is the unit of enumeration in the LTS and it has for its sampling domain 18 major industry groups (1-digit) based on the 2009 PSIC. The coverage of the survey was limited to business enterprises in Metro Manila due to budget constraints and in line with the instruction of DOLE officials to conduct a quick/timely assessment of the labor market activity through a sample survey with manageable sample size. Metro Manila accounts for one-third of the country's gross domestic product and about two-thirds of the total large business enterprises in the Philippines.

The sampling frame was extracted from the top 25,000 corporations in the Philippines as compiled by the Securities and Exchange Commission (SEC). The frame contains the names of 15,600 enterprises in Metro Manila listed in order of their gross revenue and sales in 2012.

For LTS 2013, the population size was 15,660. Since the proportion of the initial sample size to the population size is not negligible, a revised estimate of the sample size is obtained to take into account the finite population correction. To ensure the precision of estimates in each domain, the sample size (763) was allocated in each domain using Kish's allocation formula. The sample enterprises in each domain were drawn through simple random sampling.

Sampling deviation

A replacement of a sample enterprise was done when the sampled enterprise fell in one of the following situations during the field operation: cannot be located; refuse to answer; temporarily closed; duplicate of another sample enterprise; permanently closed; or on strike.

Mode of data collection

Other [oth]

Cleaning operations

The data was manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the enterprise to ensure completeness, consistency and reasonableness of entries in accordance with the Field Operations Manual. The forms were again checked for data consistency and completeness by field supervisors. The designated personnel undertook the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries were returned to the establishments for verification, personally or through phone interview.

Sampling error estimates

Not computed.

Data appraisal

The results were validated with the previous year results in particular, the trend and patterns of data distribution across industry.

Results were also checked in terms of their coherence with the results of the National Acounts, i.e, gross domestic product (GDP). It has been observed that the pattern of movement in the LTS data series closely follow that of GDP. This could be because NCR accounted for a sizeable share of GDP. The "rule of thumb" in LTS validation is that a high GDP is associated with a positive turnover rate and vice versa.

Graph and download economic data for Quits: Manufacturing (JTS3000QUR) from Dec 2000 to Aug 2025 about quits, manufacturing, and USA.

Graph and download economic data for Quits: Accommodation and Food Services (JTS7200QUR) from Dec 2000 to Aug 2025 about quits, accommodation, food, services, and USA.

Statistics on Products in the Services Sector: Employment-related activities (Division 78 CNAE2009) (businesses with 20 or more employees): breakdown of turnover by activity sector of the client and occupation sections. National.

Analyzing an Employees Report with the provided data on employee numbers, monthly salary, performance appraisal rates, departmental distribution, geographical distribution, trends in employee numbers over the years, employee satisfaction, and gender distribution can yield valuable insights for informed decision-making and strategic planning. Here's a breakdown of the analysis:

Employee Numbers:

The total number of employees in the company is a key metric for assessing the organization's size and growth potential. Analyze historical data on employee numbers over the years to identify trends. Is the workforce expanding, contracting, or remaining stable? Monthly Salary:

Examine the distribution of monthly salaries to understand compensation structures within the organization. Calculate key statistics such as median and quartiles to assess salary equity. Identify any outliers in salary data, which may require further investigation. Performance Appraisal:

Calculate the average performance appraisal rate to gauge overall employee performance. Break down performance ratings by department to identify areas of excellence and potential improvement. Departmental Distribution:

Determine the number of employees in each department to assess departmental size and potential resource allocation. Analyze turnover rates by department to identify areas with high attrition. Geographical Distribution:

Examine the geographical origin of employees, including their area and country of residence. Identify locations with a high concentration of employees, which may have implications for office space, remote work policies, or recruitment strategies. Trends in Employee Numbers Over the Years:

Visualize the trend in employee numbers over the years using line charts or graphs. Look for patterns, such as seasonal fluctuations or long-term growth trends. Employee Satisfaction:

Analyze employee satisfaction survey results to assess the overall satisfaction level of employees. Identify areas where employees are particularly satisfied or dissatisfied and consider action plans. Gender Distribution:

Calculate the percentage of male and female employees to understand gender diversity. Assess whether there are any significant gender imbalances in specific departments or roles. To facilitate data cleaning and filtering for enhanced decision-making:

Data Cleaning: Ensure data integrity by addressing missing values, outliers, and inconsistencies in the dataset. This will result in more accurate and reliable analyses.

Filtering Options: Provide filters and interactive dashboards in the report to allow users to explore data based on various criteria such as department, performance rating, salary range, location, and gender. This empowers stakeholders to tailor the analysis to their specific needs.

Decision-Making Insights: Summarize key findings and insights from the analysis to assist decision-makers in identifying areas for improvement, resource allocation, and strategic planning.

Labor Turnover and Downsizing Trends in Taipei City: Time Series Statistical Data

Annual estimates of foreign-owned businesses by industry group, section, employment and turnover group, and country breakdown.

How long indoor sex workers stay employed in collectives is a poorly understood aspect of sex worker agency in industrialized democracies. This study provides estimates of turnover, the rate at which workers leave employment, using a subsample of 76 collectives representing 3545 workers over a one-year period. All the collectives provided data on individual workers via external websites. The collectives were identified in a larger random sample of 783 advertisers from a popular Canadian classifieds site used by sex workers, all of whom provided URLs as part of their ad contact information. Monthly between October 2022 and October 2023, individual workers associated with the subsample of advertisers were identified from web pages maintained by these advertisers and scheduling data was collected where available. Worker turnover was estimated based on whether workers were visible one month to the next. Over the year, estimated turnover ranged from 12.0% to 16.0% (mean 14.2% SD 1.1%). Turnover was not affected by month or number of workers in the collectives. Mean 41.1% workers (SD 23.5%, N = 51 advertisers) were scheduled on any given day. Workers were visible for a mean 5.5 months (SD 4.5) with those visible for one month being the largest single group. Most sex workers in collectives are likely not permanent full time employees, and the extremely brief work histories of many suggest that failure in the industry may be common for this subpopulation.

Abstract

Introduction

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess and evaluate, objectively and realistically, the changes in the growth, composition and structure of organized manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The survey has so far been conducted annually under the statutory provisions of the Collection of Statistics (COS) Act, 1953 and the rules framed there-under in 1959 except in the State of Jammu & Kashmir where it is conducted under the J&K Collection of Statistics Act, 1961 and rules framed there under in 1964. From ASI 2010-11 onwards, the survey is to be conducted annually under the statutory provisions of the Collection of Statistics (COS) Act, 2008 and the rules framed there-under in 2011except in the State of Jammu & Kashmir where it is to be conducted under the J&K Collection of Statistics Act, 1961 and rules framed there under in 1964.

ASI schedule is the basic tool to collect required data for the factories registered under Sections 2(m)(i) and 2(m)(ii) of the Factories Act, 1948. In addition to Sections 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, bidi & cigar units, employing 10 or more workers with the aid of power and 20 or more workers without the aid of power and registered under the Bidi & Cigar Workers (Conditions of Employment) Act, 1966 are also covered in ASI. Although the scope of the ASI is extended to all registered manufacturing establishments in the country, establishments under the control of the Defence Ministry, oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

The schedule for ASI, at present, has two parts. Part-I of ASI schedule, processed at the CSO (IS Wing), Kolkata, aims to collect data on assets and liabilities, employment and labour cost, receipts, expenses, input items: indigenous and imported, products and by-products, distributive expenses, etc. Part-II of ASI schedule is processed by the Labour Bureau. It aims to collect data on different aspects of labour statistics, namely, working days, man-days worked, absenteeism, labour turnover, man-hours worked etc. The concepts and definition of various terms used in collection of ASI data are given in Chapter Two, and the details of the schedule, item descriptions and procedures for collecting information for each item.

Geographic coverage

The ASI extends its coverage to the entire country upto state level.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design adopted in ASI has undergone considerable changes from time to time, taking into account the technical and other requirements. The earlier sampling design had been adopted from ASI 2007-08 to ASI 2011-12. From ASI 2012-13, a new sampling design has been adopted following the recommendation of Dr. S. L.Shetty Committee and approved by the SCIS subsequently. According to the new sampling design, all the factories in the updated frame are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector consists of the following units: a) All industrial units belonging to the six less industrially developed states/ UT's viz.Manipur, Meghalaya, Nagaland, Sikkim, Tripura and Andaman & Nicobar Islands. b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more employees, and (ii) all factories covered under Joint Returns. c) After excluding the Census scheme units, as defined above, all units belonging to the strata (State x District x Sector x 4 digit NIC 2008) having less than or equal to 4 units are also considered under Census Scheme. It may be noted that in the formation of stratum, the sectors are considered as Bidi, Manufacturing and Electricity.

Sample Sector All the remaining units in the frame are considered under Sample Scheme. For all the states, strata are formed for each State x District x Sector x 4 digit NIC2008 factories. The units are arranged in descending order of their number of employees. Samples are drawn as per Circular Systematic Sampling technique for this scheme. An even number of units with a minimum of 4 units are selected and distributed in four sub-samples. It may be noted that all the 4 sub-samples from a particular stratum may not have equal number of units. Out of these 4 sub-samples, two pre-assigned sub-samples are given to NSSO (FOD) and the other two-subsamples are given to State/UT for data collection.

The entire census units plus all the units belonging to the two sub-samples given to NSSO (FOD) are treated as the Central Sample.

The units belonging to the two sub-samples allocated to States/UTs are to be canvassed by the respective States/UTs. Hence, State/UT has to use the data (collected by NSSO (FOD) and processed by CSO (IS Wing)) along with the state sample data while deriving the district level estimates for their respective State/UT.

The entire census units plus all the units belonging to the two sub-samples given to NSSO (FOD) plus all the units belonging to the two sub-samples given to State/UT are required for pooling of Central and State Samples.

Sampling deviation

The sampling design adopted in ASI has undergone considerable changes from time to time, taking into account the technical and other requirements. The present sampling design has been adopted from ASI 2007-08. All the factories in the updated frame are divided into two sectors, viz., Census and Sample.

Mode of data collection

Statutory return submitted by factories as well as Face to Face

Research instrument

Annual Survey of Industries Questionnaire is divided into different blocks:

BLOCK A.IDENTIFICATION BLOCK - This block has been designed to collect the descriptive identification of the sample enterprise. The items are mostly self-explanatory.

BLOCK B. TO BE FILLED BY OWNER OF THE FACTORY - This block has been designed to collect the particulars of the sample enterprise. This point onwards, all the facts and figures in this return are to be filled in by owner of the factory.

BLOCK C: FIXED ASSETS - Fixed assets are of a permanent nature having a productive life of more than one year, which is meant for earning revenue directly or indirectly and not for the purpose of sale in ordinary course of business. They include assets used for production, transportation, living or recreational facilities, hospital, school, etc. Intangible fixed assets like goodwill, preliminary expenses including drawing and design etc are excluded for the purpose of ASI. The fixed assets have, at the start of their functions, a definite value, which decreases with wear and tear. The original cost less depreciation indicates that part of value of fixed assets, which has not yet been transferred to the output. This value is called the residual value. The value of a fixed asset, which has completed its theoretical working life should always be recorded as Re.1/-. The revalued value is considered now. But depreciation will be taken on original cost and not on revalued cost.

BLOCK D: WORKING CAPITAL & LOANS - Working capital represents the excess of total current assets over total current liabilities.

BLOCK E : EMPLOYMENT AND LABOUR COST - Particulars in this block should relate to all persons who work in and for the establishment including working proprietors and active business partners and unpaid family workers. However, Directors of incorporated enterprises who are paid solely for their attendance at meeting of the Board of Directors are to be excluded.

BLOCK F : OTHER EXPENSES - This block includes the cost of other inputs as both the industrial and nonindustrial service rendered by others, which are paid by the factory and most of which are reflected in the ex-factory value of its production during the accounting year.

BLOCK G : OTHER INCOMES - In this block, information on other output/receipts is to be reported.

BLOCK H: INPUT ITEMS (indigenous items consumed) - This block covers all those goods (raw materials, components, chemicals, packing material, etc.), which entered into the production process of the factory during the accounting year. Any material used in the production of fixed assets (including construction work) for the factory's own use should also be included. All intermediate products consumed during the year are to be excluded. Intermediate products are those, which are produced by the factory but are, subjected to further manufacture. For example, in a cotton textile mill, yarn is produced from raw cotton and the same yarn is again used for manufacture of cloth. An intermediate product may also be a final product in the same factory. For example, if the yarn produced by the factory is sold as yarn, it becomes a final product

A dataset that combines federal and state administrative data on employers and employees with core Census Bureau censuses and surveys, while protecting the confidentiality of people and firms that provide the data. This data infrastructure facilitates longitudinal research applications in both the household / individual and firm / establishment dimensions. The specific research is targeted at filling an important gap in the available data on older workers by providing information on the demand side of the labor market. These datasets comprise Title 13 protected data from the Current Population Surveys, Surveys of Income and Program Participation, Surveys of Program Dynamics, American Community Surveys, the Business Register, and Economic Censuses and Surveys. With few exceptions, states have partnered with the Census Bureau to share data. As of December 2008, Connecticut, Massachusetts, New Hampshire and Puerto Rico have not signed a partnership agreement, while a partnership with the Virgin Islands is pending. LEHD's second method of developing employer-employee data relations through the use of federal tax data has been completed. LEHD has produced summary tables on accessions, separation, job creation, destruction and earnings by age and sex of worker by industry and geographic area. The data files consist of longitudinal datasets on all firms in each participating state (quarterly data, 1991- 2003), with information on age, sex, turnover, and skill level of the workforce as well as standard information on employment, payroll, sales and location. These data can be accessed for all available states from the Project Website. Data Availability: Research conducted on the LEHD data and other products developed under this proposal at the Census Bureau takes place under a set of rules and limitations that are considerably more constraining than those prevailing in typical research environments. If state data are requested, the successful peer-reviewed proposals must also be approved by the participating state. If federal tax data are requested, the successful peer-reviewed proposals must also be approved by the Internal Revenue Service. Researchers using the LEHD data will be required to obtain Special Sworn Status from the Census Bureau and be subject to the same legal penalties as regular Census Bureau employees for disclosure of confidential information. Basic instructions on how to download the data files and restrictions can be found on the Project Website. * Dates of Study: 1991-present * Study Features: Longitudinal * Sample Size: 48 States or U.S. territories

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

The combined data source integrates information from four distinct tables, providing a comprehensive overview of various aspects related to the organization's workforce. Here's a brief description of the combined data source:

Employee Office Survey (Feedbacks): - Captures employee feedback data for each office location from the years 2017 to 2022. - Includes ratings or feedback scores for employees, shedding light on their performance and satisfaction levels. - Provides insights into the evolving trends in employee feedback over the specified timeframe.

Job Position Structure: - Details the organizational hierarchy and job roles within different departments. - Outlines the structure with information on departments, job levels, and specific job roles. - Serves as a reference for understanding the organization's job hierarchy and the diversity of roles available.

Office Locations (Canada and US): - Encompasses information about office locations, particularly highlighting 5 offices in Canada and 3 offices in the United States. - Includes details such as office codes, city locations, provinces (or states), and countries. - Offers a geographical perspective on the distribution of offices across North America.

Employee Attrition Information: - Provides insights into employee attrition, including details such as leaving years, reasons for leaving, and relieving statuses. - Helps in understanding patterns and factors contributing to employee turnover within the organization. - Acts as a valuable resource for analyzing workforce dynamics and making informed HR decisions.

The combined data source thus offers a holistic view of the organization's workforce dynamics, encompassing employee feedback, job structure, office locations, and attrition information. This integrated dataset enables a more comprehensive analysis of the relationships and trends within the organization over time.