➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ You can select raw or clean and AI-enriched datasets;

➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including HR and sales technology and investment.

Employee Data use cases:

✅ Source best-fit talent for your recruitment needs

Coresignal's Employee Data can help source the best-fit talent for your recruitment needs by providing the most up-to-date information on qualified candidates globally.

✅ Fuel your lead generation pipeline

Enhance lead generation with 712M+ up-to-date employee records from the largest professional network. Our Employee Data can help you develop a qualified list of potential clients and enrich your own database.

✅ Analyze talent for investment opportunities

Employee Data can help you generate actionable signals and identify new investment opportunities earlier than competitors or perform deeper analysis of companies you're interested in.

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

MySQL Classicmodels sample database

The MySQL sample database schema consists of the following tables:

• Customers: stores customer’s data.
• Products: stores a list of scale model cars.
• ProductLines: stores a list of product line categories.
• Orders: stores sales orders placed by customers.
• OrderDetails: stores sales order line items for each sales order.
• Payments: stores payments made by customers based on their accounts.
• Employees: stores all employee information as well as the organization structure such as who reports to whom.
• Offices: stores sales office data.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8652778%2Fefc56365be54c0e2591a1aefa5041f36%2FMySQL-Sample-Database-Schema.png?generation=1670498341027618&alt=media" alt="">

Capture/store/manage end-user work activities at designated times as needed for DOWS sampling. The end-user, e.g., CR, receives a desktop alert when a DOWS sample is requested. User selects appropriate DOWS category from drop-down menu and submits the information. Manager reviews end-user submitted data, adds non-reporter data, and submits report.

//// 🌍 Avanteer Employee Data ////

The Largest Dataset of Active Global Profiles 1B+ Records | Updated Daily | Built for Scale & Accuracy

Avanteer’s Employee Data offers unparalleled access to the world’s most comprehensive dataset of active professional profiles. Designed for companies building data-driven products or workflows, this resource supports recruitment, lead generation, enrichment, and investment intelligence — with unmatched scale and update frequency.

//// 🔧 What You Get ////

1B+ active profiles across industries, roles, and geographies

Work history, education history, languages, skills and multiple additional datapoints.

AI-enriched datapoints include: Gender Age Normalized seniority Normalized department Normalized skillset MBTI assessment

Daily updates, with change-tracking fields to capture job changes, promotions, and new entries.

Flexible delivery via API, S3, or flat file.

Choice of formats: raw, cleaned, or AI-enriched.

Built-in compliance aligned with GDPR and CCPA.

//// 💡 Key Use Cases ////

✅ Smarter Talent Acquisition Identify, enrich, and engage high-potential candidates using up-to-date global profiles.

✅ B2B Lead Generation at Scale Build prospecting lists with confidence using job-related and firmographic filters to target decision-makers across verticals.

✅ Data Enrichment for SaaS & Platforms Supercharge ATS, CRMs, or HR tech products by syncing enriched, structured employee data through real-time or batch delivery.

✅ Investor & Market Intelligence Analyze team structures, hiring trends, and senior leadership signals to discover early-stage investment opportunities or evaluate portfolio companies.

//// 🧰 Built for Top-Tier Teams Who Move Fast ////

Zero duplicate, by design

<300ms API response time

99.99% guaranteed API uptime

Onboarding support including data samples, test credits, and consultations

Advanced data quality checks

//// ✅ Why Companies Choose Avanteer ////

➔ The largest daily-updated dataset of global professional profiles

➔ Trusted by sales, HR, and data teams building at enterprise scale

➔ Transparent, compliant data collection with opt-out infrastructure baked in

➔ Dedicated support with fast onboarding and hands-on implementation help

////////////////////////////////

Empower your team with reliable, current, and scalable employee data — all from a single source.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

Abstract

The survey of Nepal manufacturing sector workers was conducted from March 8 to June 15, 2009, at the same time with 2009 Nepal Enterprise Survey. The research aimed to capture employees' perspectives on work environment and their satisfaction with work conditions. Data from 392 workers was analyzed.

Employee Survey topics include workers' demographic characteristics, their job titles, hours, pay, work experience, health expenditures coverage, on-site training, paid leave, compensation when a contract is terminated and work commuting issues. The study also focuses on employees' trade unions membership and participation in trade unions' actions, evaluates workers' satisfaction with their jobs and employers, and assesses if employees consider migrating for work.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is a permanent full-time employee.

Universe

Employees working for manufacturing sectors, as defined in ISIC Revision 3.1, Group D, were focus of the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

From the list of establishments that were randomly selected for 2009 Nepal Enterprise Survey, a sub-set of manufacturing firms with 20 and more workers, was randomly chosen for the Employee Survey. The contractor was instructed to either randomly select respondents from a list of employees, or to walk through an establishment and randomly choose interviewees.

The contractor successfully interviewed 392 employees in 68 businesses; 4-7 workers were interviewed per firm.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instrument is available: - Employee Questionnaire.

Employee Survey topics include employees' demographic characteristics, their job titles, hours, pay, work experience, health expenditures coverage, on-site training, paid leave, compensation when a contract is terminated and work commuting issues. The study also focuses on employees' trade unions membership and participation in trade unions' actions, evaluates workers' satisfaction with their jobs and employers, and assesses if employees consider migrating for work.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Working Age Population: Employed: Southeast: Employees data was reported at 28,911.000 Person th in Mar 2019. This records a decrease from the previous number of 29,118.000 Person th for Dec 2018. Working Age Population: Employed: Southeast: Employees data is updated quarterly, averaging 29,512.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 30,455.000 Person th in Jun 2014 and a record low of 28,626.000 Person th in Mar 2017. Working Age Population: Employed: Southeast: Employees data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA018: Continuous National Household Sample Survey: Working Age Population: Employed: by Employment Status in the Main Job. Employees are people who worked for an employer, usually forcing himself to fulfill a day's work and receiving in return a cash payment, goods, products or benefits (housing, food, clothing, etc.).

Brazil Working Age Population: Employed: Central West: Employees data was reported at 5,294.000 Person th in Mar 2019. This records a decrease from the previous number of 5,372.000 Person th for Dec 2018. Brazil Working Age Population: Employed: Central West: Employees data is updated quarterly, averaging 5,241.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 5,389.000 Person th in Sep 2018 and a record low of 5,051.000 Person th in Mar 2012. Brazil Working Age Population: Employed: Central West: Employees data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA018: Continuous National Household Sample Survey: Working Age Population: Employed: by Employment Status in the Main Job. Employees are people who worked for an employer, usually forcing himself to fulfill a day's work and receiving in return a cash payment, goods, products or benefits (housing, food, clothing, etc.).

The OpenWeb Ninja Glassdoor Data API provides real-time access to extensive company data and employer reviews data from Glassdoor.

Key company data points included in the dataset: Name, Rating, Website, Salary and Job counts, Company size, Revenue, Stock, Competitors, Awards won, and 30+ more data points.

Key employer review data points included in the dataset: Review summary, Pros / Cons, Employee status, Location, Work-Life balance, CEO rating, and 20+ more data points.

OpenWeb Ninja's Glassdoor Data API Stats & Capabilities: - 2M+ Companies/Employers - 80M+ Employee Reviews - 30+ company data points - 20+ review data points - Company search capability

OpenWeb Ninja's Glassdoor Data API common use cases: - Investors and Market Analysts - Market and Industry Trends - Competitive Analysis - Company Insights

The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.

🟡Please Upvote my dataset If you like It.✨

This dataset contains valuable employee information over time that can be analyzed to help optimize key HR functions. Some potential use cases include:

Attrition analysis: Identify factors correlated with attrition like department, role, salary, etc. Segment high-risk employees. Predict future attrition.

Performance management: Analyze the relationship between metrics like ratings, and salary increments. recommend performance improvement programs.

Workforce planning: Forecast staffing needs based on historical hiring/turnover trends. Determine optimal recruitment strategies.

Compensation analysis: Benchmark salaries vs performance, and experience. Identify pay inequities. Inform compensation policies.

Diversity monitoring: Assess diversity metrics like gender ratio over roles, and departments. Identify underrepresented groups.

Succession planning: Identify high-potential candidates and critical roles. Predict internal promotions/replacements in advance.

Given its longitudinal employee data and multiple variables, this dataset provides rich opportunities for exploration, predictive modeling, and actionable insights. With a large sample size, it can uncover subtle patterns. Cleaning, joining with other contextual data sources can yield even deeper insights. This makes it a valuable starting point for many organizational studies and evidence-based decision-making.

.............................................................................................................................................................................................................................................

This dataset contains information about different attributes of employees from a company. It includes 1000 employee records and 12 feature columns.

The columns are:

satisfaction_level: Employee satisfaction score (1-5 scale) last_evaluation: Score on last evaluation (1-5 scale) number_project: Number of projects employee worked on average_monthly_hours: Average hours worked in a month time_spend_company: Number of years spent with the company work_accident: If an employee had a workplace accident (yes/no) left: If an employee has left the company (yes/no) promotion_last_5years: Number of promotions in last 5 years Department: Department of the employee Salary: Annual salary of employee satisfaction_level: Employee satisfaction level (1-5 scale) last_evaluation: Score on last evaluation (1-5 scale)

Brazil Working Age Population: Employed: Northeast: Employees data was reported at 13,396.000 Person th in Mar 2019. This records a decrease from the previous number of 13,616.000 Person th for Dec 2018. Brazil Working Age Population: Employed: Northeast: Employees data is updated quarterly, averaging 13,574.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 14,317.000 Person th in Dec 2014 and a record low of 12,812.000 Person th in Mar 2017. Brazil Working Age Population: Employed: Northeast: Employees data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA018: Continuous National Household Sample Survey: Working Age Population: Employed: by Employment Status in the Main Job. Employees are people who worked for an employer, usually forcing himself to fulfill a day's work and receiving in return a cash payment, goods, products or benefits (housing, food, clothing, etc.).

The Workplace Employment Relations Survey (WERS) is a national survey of the state of employment relations and working life inside British workplaces. The 1998, 2004 and 2011 surveys (WERS98, WERS 2004, WERS 2011) are the fourth, fifth and sixth in the series, respectively, earlier surveys having been carried out in 1980, 1984 and 1990. Prior to 1998, the series was known as the Workplace Industrial Relations Survey (WIRS), the name being changed in order to better reflect the content of the current survey. The UK Data Archive hold the WIRS/WERS series from 1980 onwards under GN 33176.

The purpose of each survey in the WERS series has been to provide large-scale, statistically reliable evidence about a broad range of industrial relations and employment practices across almost every sector of the economy in Great Britain. This evidence is collected with several objectives in mind. It aims to provide a mapping of employment relations practices in workplaces across Great Britain, monitor changes in those practices over time, inform policy development and permit an informed assessment of the effects of public policy, and bring about a greater understanding of employment relations as well as of the labour market.

To that end, the cross-section element of WERS98 and WERS 2004 collected information from managers with responsibility for employment relations or personnel matters; trade union or employee representatives; and employees themselves. Thus, the surveys included the Cross-Section Survey of Managers (MQ), the Cross-Section Survey of Employee Representatives (ERQ), and the Cross-Section Survey of Employees (SEQ). The cross-section surveys in 2004 also included a Financial Performance Questionnaire (FPQ), which examined financial performance of the establishment over the 12 months previous to the survey. (Access to the FPQ data, alongside region identifiers and industry codes for the MQ and panel data, was initially restricted until April 2007, when they were deposited as part of the second edition of End User Licence (EUL) SN 5294.) The panel element of WERS 2004 includes the Screening Questionnaire and the Survey of Managers (comprising the Basic Workforce Data Sheet and the Management Interview).

The 2011 WERS sample consisted of a panel sample containing all the workplaces that had taken part in the 2004 WERS and were still in existence in 2011, and a stratified random sample of establishments drawn from the Inter-Departmental Business Register (IDBR) in August 2010 (the fresh cross-section sample). The key design innovation of the 2011 WERS was the integration of the two elements so that workplaces in the panel sample were eligible for all four components of WERS 2011. Weights were devised to enable the panel sample to be combined with the fresh sample to form a combined cross-sectionally representative sample. The WERS 2011 has four components: a Survey of Managers comprising the Employee Profile Questionnaire (EPQ) and the Management Questionnaire (MQ); a Survey of Worker Representatives (WRQ); a Survey of Employees (SEQ); and a Financial Performance Questionnaire (FPQ) which detailed the financial performance of trading sector establishments in the 12 months before the survey.

Secure Access Dataset:
The Secure Access version of the study includes both the cross-section and panel surveys conducted for WERS98 and WERS 2004. The panel element for 2004 forms Wave 2 of the 1998-2004 panel survey. Wave 1 comprised the cross-sectional managers' survey conducted for WERS98. The study also includes all the WERS 2011 data

The Secure Access version includes additional variables not included in the EUL versions (see SNs 5294, 3955 and 7226). Extra variables that can be found in the Secure Access versions but not in the EUL versions relate to 1) Inter-Departmental Business Register reference numbers for businesses who have consented to the linking of WERS data to other data sources, 2) postcodes, and 3) in 2011 the Financial Performance Questionnaire data are available along with some other more detailed variables.

Geographical references: postcodes
The postcodes available in the 1998 data are pseudo-anonymised postcodes. The real postcodes were not available for this year due to the potential risk of identification of the observations. However, these replacement postcodes retain the inherent nested characteristics of real postcodes, and will allow researchers to aggregate observations to other geographic units, e.g. wards, super output areas, etc. The postcodes available in the 2004 and 2011 data are real postcodes.

Linking to other business studies
These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Additional data in 2011
The 2011 data includes an additional dataset, the Financial Performance Questionnaire, which details the financial performance of trading sector establishments in the 12 months before the survey. There are also region identifiers and the country in which the workplace is located can be identified. In addition industry classification is coded to below the section-level of the Standard Industrial Classification.

Related UK Data Archive studies:
The EUL version of the WERS Cross-Section Survey, 2004 and Panel Survey, 1998-2004; Wave 2 study is held under SN 5294. The EUL version of the WERS Cross-Section Survey 1998 is held under SN 3955. The EUL version of the WERS 2011 is held under SN 7226. Further details and links to these and other WERS studies available under a standard EUL can be found on the Workplace Employee Relations Survey list of datasets webpage.

Related Websites:
The WERS sponsors have established the 2011 Workplace Employment Relations Study: Information and Advice user support website for users of the WERS 2011 data. The site includes provision for users to contact the WERS research team with queries about the data.

Further information about the WERS series is also provided on the gov.uk Workplace Employment Relations Study (WERS) webpage.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest edition:
For the fifth edition (August 2018), the pseudo-anonymised postcodes (NEW_PC) included in the data file 'wers2004_management_idbr_restricted' have been replaced with real postcodes (PCD2). The file contains only those cases where the respondent gave consent for data linkage (MLINKDAT=yes).

Abstract

The survey of Bhutan manufacturing and services sectors workers was conducted from April 15 to June 4, 2009, at the same time with 2009 Bhutan Indicator Survey. The research aimed to capture employees' perspectives on work environment and their satisfaction with work conditions. 486 full-time employees in 61 firms were interviewed.

Employee Survey topics include workers' demographic characteristics, their job titles, hours, pay, work experience, on-site training, paid leave, compensation when a contract is terminated and work commuting issues. The study also focuses on membership in workers' associations, evaluates workers' satisfaction with their jobs and employers, and assesses if employees consider migrating for work.

Geographic coverage

National

Analysis unit

Full-time employees working for establishments in manufacturing and services sectors were focus of the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

From the list of establishments that were randomly selected for 2009 Bhutan Indicator Survey, a sub-set of manufacturing and services firms with 20 and more workers, was randomly chosen for the Employee Survey. The contractor was instructed to either randomly select respondents from a list of employees, or to walk through an establishment and randomly choose interviewees. The enumerators were able to select respondents based on the employee list for two-third of interviews.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instrument is available: - Employee Questionnaire.

Employee Survey topics include workers' demographic characteristics, their job titles, hours, pay, work experience, on-site training, paid leave, compensation when a contract is terminated and work commuting issues. The study also focuses on workers' association membership, evaluates workers' satisfaction with their jobs and employers, and assesses if employees consider migrating for work.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting, employee data / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Abstract

Malaysia Employee Survey was conducted as part of 2007 Malaysia Productivity of the Investment Climate Private Enterprise Survey (PICS). The research aimed to capture employees' perspectives on work environment and dynamics. The survey covered 10615 manufacturing workers and 2918 services sector employees.

Malaysia PICS 2007 was a collaborative effort of the Malaysian Government and the World Bank. The research targeted 1115 businesses working in manufacturing sector and 303 enterprises in services sector.

The sample for the Employee Survey was randomly selected from establishments participating in Malaysia PICS 2007.

Overall, PICS 2007 aimed to achieve following objectives: - Benchmark productivity, the investment climate, competitiveness, and growth in Malaysia; - Identify the key constraints to competitiveness as perceived by firms in the manufacturing and selected business support services sectors; - Highlight the key concerns regarding regulatory burden, skills shortages and weak innovation capabilities; - Enable the analysis of firm performance focusing on determining how investment climate constraints affect productivity and job creation in selected sectors.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Employee Survey was selected from the establishments covered in 2007 Malaysia Productivity of the Investment Climate Private Enterprise Survey (PICS).

Ten full-time workers were sampled from each establishment. Workers were interviewed only if the manager of the business did not mind. If the manager had no objections, enumerators asked for a complete list of full-time employees of the establishment from the personnel manager at about the time the human resources module of PICS 2007 was being completed. The personnel manager was asked to choose any one worker from the list. After that enumerators picked employees at fixed regular intervals until a sample of 10 workers was reached. A worker who could not be interviewed was replaced by another randomly chosen employee.

Malaysia PICS 2007 covered establishments in the manufacturing and business support services sectors. For manufacturing industries, the economic activities were defined according to Divisions under the Malaysia Standard Industrial Classification (MSIC) 2000 (2-digit codes), which is identical to the United Nations Statistical Division's International Standard Industrial Classification of All Economic Activities (ISIC Rev. 3) up to the 4-digit level. In Malaysia PICS 2007, 12 manufacturing industries and 5 business support services sectors were surveyed.

The sampling frame was extracted from the Central Register of Establishments (SIDAP) maintained by the Department of Statistics, Malaysia. The register was updated using information supplied by the Companies Commission of Malaysia (CCM), Employees Provident Fund (EPF), the 2006 Economic Census data, and several regular surveys or censuses conducted by the Department of Statistics, Malaysia (DOSM).

For the manufacturing sector, only establishments with more than 10 employees were covered. For the business support services sector, two employment thresholds were used. Only establishments with more than 10 employees were covered for Information Technology, Telecommunications, and Advertising & Marketing, while only establishments with more than 20 employees were covered for Accounting & Related Services and Business Logistics.

Single-stage stratified systematic sampling was used in drawing samples. The sampling frame was stratified by sector, region, state, and industry. To select the sample, for each sector, establishments within each industry, region and area combination were arranged according to the value of output. Selection was then carried out independently for each sub-stratum based on a linear systematic method.

Malaysia PICS 2007 covered 6 regions: 4 regions in Peninsular Malaysia and 2 regions in East Malaysia. Within each of the 6 regions, states and areas to be covered were selected based on the concentration of establishments.

For details on the Malaysia PICS 2007 sampling coverage, sampling methodology and sampling frame, please review "Sampling Methodology of Malaysia PICS 2007" and "Sample Coverage and Distribution of Malaysia PICS 2007" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Sector Employee Questionnaire - Business Supporting Servicers Employee Questionnaire.

Abstract

The study was conducted in Serbia between October 2008 and February 2009 as part of the first round of The Management, Organization and Innovation Survey. Data from 135 manufacturing companies with 50 to 5,000 full-time employees was analyzed.

The survey topics include detailed information about a company and its management practices - production performance indicators, production target, ways employees are promoted/dealt with when underperforming. The study also focuses on organizational matters, innovation, spending on research and development, production outsourcing to other countries, competition, and workforce composition.

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment is defined as a separate production unit, regardless of whether or not it has its own financial statements separate from those of the firm, and whether it has it own management and control over payroll. So the bottling plant of a brewery would be counted as an establishment.

Universe

The survey universe was defined as manufacturing establishments with at least fifty, but less than 5,000, full-time employees.

Kind of data

Sample survey data [ssd]

Sampling procedure

Random sampling was used in the study. For all MOI countries, except Russia, there was a requirement that all regions must be covered and that the percentage of the sample in each region was required to be equal to at least one half of the percentage of the sample frame population in each region.

In most countries the sample frame used was an extract from the Orbis database of Bureau van Dijk, which was provided to the Consultant by the EBRD. The sample frame contained details of company names, location, company size (number of employees), company performance measures and contact details. The sample frame downloaded from Orbis was cleaned by the EBRD through the addition of regional variables, updating addresses and phone numbers of companies.

Examination of the Orbis sample frames showed their geographic distributions to be wide with many locations, a large number of which had only a small number of records. Each establishment was selected with two substitutes that can be used if it proves impossible to conduct an interview at the first establishment. In practice selection was confined to locations with the most records in the sample frame, so the sample frame was filtered to just the cities with the most establishments.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys. For Serbia, the percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 26.7% (82 out of 307 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

Two different versions of the questionnaire were used. Questionnaire A was used when interviewing establishments that are part of multiestablishment firms, while Questionnaire B was used when interviewing single-establishment firms. Questionnaire A incorporates all questions from Questionnaire B, the only difference is in the reference point, which is the so-called national firm in the first part of Questionnaire A and firm in Questionnaire B. Second part of the questionnaire refers to the interviewed establishment only in both Questionnaire A and Questionnaire B. Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Item non-response was addressed by two strategies: - For sensitive questions that may generate negative reactions from the respondent, such as ownership information, enumerators were instructed to collect the refusal to respond as (-8). - Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

Survey non-response was addressed by maximising efforts to contact establishments that were initially selected for interviews. Up to 15 attempts (but at least 4 attempts) were made to contact an establishment for interview at different times/days of the week before a replacement establishment (with similar characteristics) was suggested for interview. Survey non-response did occur, but substitutions were made in order to potentially achieve the goals.

Additional information about sampling, response rates and survey implementation can be found in "MOI Survey Report on Methodology and Observations 2009" in "Technical Documents" folder.

The Business Register and Employment Survey (BRES) is the official source of employee and employment estimates by detailed geography and industry. It is also used to update the Inter-Departmental Business Register (IDBR), the main sampling frame for business surveys conducted by the Office for National Statistics (ONS), with information on the structure of businesses in the UK.

The survey collects employment information from businesses across the whole of the UK economy for each site that they operate. This allows the ONS to produce employee and employment estimates by detailed geography and industry split by full-time/part-time workers and whether the business is public/private.

The ONS produces a number of different measures of employment including Workforce Jobs and the Annual Population Survey/Labour Force Survey. However, BRES is the recommended source of information on employment by detailed geography and industry.

The BRES has two purposes: collecting data to update local unit information and business structures on the IDBR, and producing published annual employment statistics.

The BRES sample does not include Northern Ireland. Northern Ireland data are received direct from the Northern Ireland Department of Enterprise, Trade and Investment (DETINI) which are used to create UK estimates. The UK Data Archive holds data only for Great Britain.

The BRES replaced the Annual Business Inquiry, Part 1 (ABI/1) in 2009. ABI/1 data for 2009 and earlier are held as part of the Annual Respondents Database under UK Data Archive SN 6644.

Change in sampling from 2015-2016
In 2015, ONS made a strategic decision to include business units with a single PAYE code for which VAT data are available. Prior to 2015, such units were excluded from the sampling frame and therefore not estimated for in ONS outputs. So from January 2016, the coverage of BRES was extended to include a population of solely PAYE based businesses. This improvement in coverage is estimated to have increased the business survey population by around 100,000 businesses, with a total of around 300,000 employment and 200,000 employees between December 2015 and January 2016. The increase in business population has led to an increase in the estimate of employment and employees for the 2015 dataset. Further information is available in documentation file '7463_bres_2015_change_in_firm_sampling.pdf'.

Linking to other business studies
These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest edition information
For the thirteenth edition (February 2024), the 'revised 2021' and 'provisional 2022' data files have been added.

High‑Growth SaaS Workforce Simulation (4‑Year Synthetic HRIS)

Data Overview

This synthetic dataset models a high‑growth SaaS startup workforce over 48 months (May 2021–April 2025), starting with 300 employees and growing via a ramped hiring process. It includes detailed employee master records, monthly “snapshot” views, and a transactional leave‑request log. The goal is to provide a realistic sandbox for teaching people‑analytics techniques—headcount and tenure trends, compensation modeling, performance/regression analysis, survival (attrition) modeling, and leave‑impact studies.

Data Files

Key Features & Schema

1. Master Employee Data (employees_updated.csv)

2. Monthly Snapshots (snapshots_updated.csv)

In addition to master‑data columns carried forward, each snapshot includes: