Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August after the 15th of the month.

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.

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

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2023. This dataset is a prime candidate for conducting analyses on salary disparities, the relationship between department/division and salary, and the distribution of salaries across gender and grade levels.

Statistical models can be applied to predict base salaries based on factors such as department, grade, and length of service. Machine learning techniques could also be employed to identify patterns and anomalies in the salary data, such as outliers or instances of significant inequity.

Some analysis to be performed with this dataset can include:

• Gender Pay Gap Analysis: An examination of salary differences between genders within similar roles, grades, and departments to identify any disparities that need to be addressed.
• Departmental Salary Analysis: Analyzing the distribution of salaries across different departments and divisions to understand how compensation varies within the organization.
• Impact of Overtime and Longevity Pay: Evaluating how overtime and longevity pay contribute to the overall compensation of employees and identifying trends or patterns in these payments. ​

Overview: 📃

This dataset provides anonymized sample employee records commonly found in HR information systems. It includes details such as employee ID, name, job title, department, business unit, gender, ethnicity, age, hire date, and annual salary. It is ideal for educational projects, algorithm demonstrations (such as B-tree implementation), HR analytics exploration, salary-related analysis examples, and more.

Columns:

• EEID: Unique Employee Identifier
• Full Name: Sample employee names
• Job Title: Employee's role (e.g., Director, Sr. Manager)
• Department: Department affiliation (e.g., IT, Engineering)
• Business Unit: Business subdivision (e.g., Manufacturing, Specialty Products)
• Gender: Employee gender (Female or Male)
• Ethnicity: Employee ethnicity (Asian, Caucasian, Other)
• Age: Age of the employee
• Hire Date: Date the employee was hired
• Annual Salary: Annual salary in numeric format

City-Parish employees' annual salaries and other payroll related information. Information is calculated after the last payroll is run for the year specified. Some fields, such as job title and department, are accurate as of the time the data was captured for Open Data BR. For example, if an employee worked for three departments throughout the year, only the department they worked for at the time we collected the data will be shown. ***In November of 2018, the City-Parish switched to a new payroll system. This data contains employee information from 2018 onward. For prior year data, please see the Legacy City-Parish Employee Annual Salaries https://data.brla.gov/Government/Legacy-City-Parish-Employee-Annual-Salaries/g5c2-myyj

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August after the 15th of the month.

Employee payroll data for all Cook County employees excluding Forest Preserves, indicating amount of base salary paid to an employee during the County fiscal quarter. Salaries are paid to employees on a bi-weekly basis.

Any pay period that extended between quarters will be reported to the quarter of the Pay Period End Date. (e.g. If a Pay Period runs 02/21-03/05, that pay period would be reported in the Q2 period, as the end of the pay period falls in March - Q2)

The county fiscal quarters are:

Q1: December - February

Q2: March - May

Q3: June - August

Q4: September - November

The Employee Unique Identifier field is a unique number assigned to each employee for the purpose of this data set, that is not their internal employee ID number, and allows an employee to be identified in the data set over time, in case of a name change or other change. This number will be consistent within the data set, but we reserve the right to regenerate this number over time across the data set.

ISSUE RESOLVED: As of 4/19/2018 there was an issue regarding employee FY2016 and FY2017 payroll in which records were duplicated in the quarterly aggregation, resulting in inflated base pay amounts. Please disregard any data extracted from this dataset prior to the correction date and use this version moving forward.

KNOWN ISSUE: Several records are missing Bureau and Office information. We are working on correcting this and will update the dataset when this issue has been resolved.

For data prior to Fiscal Year 2016, see datasets at https://datacatalog.cookcountyil.gov/browse?tags=payroll

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August.

To analyze the salaries of company employees using Pandas, NumPy, and other tools, you can structure the analysis process into several steps:

Case Study: Employee Salary Analysis In this case study, we aim to analyze the salaries of employees across different departments and levels within a company. Our goal is to uncover key patterns, identify outliers, and provide insights that can support decisions related to compensation and workforce management.

Step 1: Data Collection and Preparation Data Sources: The dataset typically includes employee ID, name, department, position, years of experience, salary, and additional compensation (bonuses, stock options, etc.). Data Cleaning: We use Pandas to handle missing or incomplete data, remove duplicates, and standardize formats. Example: df.dropna() to handle missing salary information, and df.drop_duplicates() to eliminate duplicate entries. Step 2: Data Exploration and Descriptive Statistics Exploratory Data Analysis (EDA): Using Pandas to calculate basic statistics such as mean, median, mode, and standard deviation for employee salaries. Example: df['salary'].describe() provides an overview of the distribution of salaries. Data Visualization: Leveraging tools like Matplotlib or Seaborn for visualizing salary distributions, box plots to detect outliers, and bar charts for department-wise salary breakdowns. Example: sns.boxplot(x='department', y='salary', data=df) provides a visual representation of salary variations by department. Step 3: Analysis Using NumPy Calculating Salary Ranges: NumPy can be used to calculate the range, variance, and percentiles of salary data to identify the spread and skewness of the salary distribution. Example: np.percentile(df['salary'], [25, 50, 75]) helps identify salary quartiles. Correlation Analysis: Identify the relationship between variables such as experience and salary using NumPy to compute correlation coefficients. Example: np.corrcoef(df['years_of_experience'], df['salary']) reveals if experience is a significant factor in salary determination. Step 4: Grouping and Aggregation Salary by Department and Position: Using Pandas' groupby function, we can summarize salary information for different departments and job titles to identify trends or inequalities. Example: df.groupby('department')['salary'].mean() calculates the average salary per department. Step 5: Salary Forecasting (Optional) Predictive Analysis: Using tools such as Scikit-learn, we could build a regression model to predict future salary increases based on factors like experience, education level, and performance ratings. Step 6: Insights and Recommendations Outlier Identification: Detect any employees earning significantly more or less than the average, which could signal inequities or high performers. Salary Discrepancies: Highlight any salary discrepancies between departments or gender that may require further investigation. Compensation Planning: Based on the analysis, suggest potential changes to the salary structure or bonus allocations to ensure fair compensation across the organization. Tools Used: Pandas: For data manipulation, grouping, and descriptive analysis. NumPy: For numerical operations such as percentiles and correlations. Matplotlib/Seaborn: For data visualization to highlight key patterns and trends. Scikit-learn (Optional): For building predictive models if salary forecasting is included in the analysis. This approach ensures a comprehensive analysis of employee salaries, providing actionable insights for human resource planning and compensation strategy.

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August after the 15th of the month.

Payroll information for all Los Angeles City Employees including the City's three proprietary departments: Water and Power, Airports and Harbor. Data is updated bi-weekly by the Los Angeles City Controller's Office. Payroll information for employees of the Department of Water and Power is updated every three months.

The R code is used to calculate and standardize payroll indicators from common-format personnel records. Specifically, it provides working examples for deriving each key indicator (wage bill growth, employment growth, average pay trajectories, pay equity, gender pay gap, turnover, promotions/career progression, and retirement projections), so governments can compute these measures for the whole administration, for specific agencies, or for employee subgroups (e.g., by job, gender, contract). The code also illustrates recommended calculations and helps address common data errors, enabling comparable, reproducible salary indicators across countries.

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August after the 15th of the month.

This dataset provides a detailed SQL-based employee database, which is ideal for practicing SQL queries and performing database-related operations. The dataset is structured to simulate a real-world organizational database, featuring various tables related to employee information, job roles, departments, and more.

The dataset is sourced from the GitHub repository https://github.com/cmoeser5/Employee-Database-SQL. It is intended for educational purposes, particularly for learning and practicing SQL.

Tables Included - employees: Contains records of employees with fields such as employee ID, name, job title, and department. - departments: Lists departments within the organization with fields including department ID and department name. - jobs: Includes details about job roles with fields such as job ID, job title, and job description. - salaries: Provides salary information for employees, including employee ID, salary amount, and salary date. - titles: Contains historical job title data for employees, including employee ID, job title, and title date.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset contains salary and bonus details of employees across various roles and industries. It includes key attributes such as base salary, annual bonus, job position, experience level, and department. The data can be used for data science projects related to salary prediction, employee compensation analysis, and financial insights. By exploring trends, correlations, and patterns, users can gain valuable insights into salary distributions and bonus structures. The dataset is suitable for machine learning applications such as regression modeling and classification. It is ideal for students, researchers, and professionals looking to analyze financial compensation trends in the workforce.

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HR Payroll Software Market Size 2025-2029

The hr payroll software market size is valued to increase by USD 7.84 billion, at a CAGR of 15.9% from 2024 to 2029. Digital transformation of HR functions will drive the hr payroll software market.

Major Market Trends & Insights

North America dominated the market and accounted for a 40% growth during the forecast period.
By Component - Software segment was valued at USD 2.6 billion in 2023
By Deployment - On-Premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 263.97 million
Market Future Opportunities: USD 7840.00 million
CAGR from 2024 to 2029 : 15.9%

Market Summary

HR payroll software has become an indispensable tool for businesses seeking to streamline their human resources functions and ensure compliance with labor regulations. The global market for HR payroll software is witnessing significant growth, driven by the increasing adoption of cloud-based solutions and the need for operational efficiency. According to recent studies, businesses that have implemented HR payroll software have seen a notable improvement in payroll processing time, reducing it by up to 50% compared to manual processes. Moreover, the integration of HR payroll software with other business systems, such as time and attendance and benefits administration, enables end-to-end automation of HR processes.
This not only enhances operational efficiency but also reduces the risk of errors and inconsistencies. However, the market is not without challenges. Data Security and privacy concerns continue to be a major concern for businesses, particularly with the increasing number of data breaches. A real-world scenario illustrating the benefits of HR payroll software is supply chain optimization. A manufacturing company with a large and geographically dispersed workforce implemented HR payroll software to automate its payroll processes. The software enabled the company to process payroll in real-time, reducing the time taken for payroll processing from a week to just a few hours.
This led to significant cost savings and improved employee satisfaction, as employees received their salaries on time and accurately. In conclusion, the adoption of HR payroll software is a strategic move for businesses seeking to optimize their HR functions, ensure compliance, and gain operational efficiency. With the market witnessing significant growth and innovation, businesses can look forward to more advanced features and capabilities in the future.

What will be the Size of the HR Payroll Software Market during the forecast period?

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How is the HR Payroll Software Market Segmented ?

The hr payroll software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


Deployment

  On-Premises
  Cloud


End-user

  Large Enterprises
  Small and Medium Enterprises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market is a dynamic and ever-evolving landscape, with businesses increasingly relying on digital solutions to automate and streamline payroll processing, compliance, and related HR functions. This software segment, encompassing applications and platforms, facilitates accurate salary calculation, tax deduction, benefits management, and payslip generation. Modern systems offer integrated features, such as time and attendance tracking, talent management acquisition, and employee self-service portals, creating a unified HR ecosystem. Cloud-based solutions are gaining popularity due to their scalability, real-time data access, and cost savings, with over 80% of businesses opting for this deployment model.

Additionally, these platforms provide essential features like payroll reconciliation, garnishment processing, leave tracking, Performance Management, and reporting dashboards, enhancing operational efficiency and data security. Integrations with HRIS, HCM, and API solutions further extend their functionality, making HR payroll software an indispensable tool for businesses of all sizes.

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The Software segment was valued at USD 2.6 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 40% to the growth of the global market during the forecast period.Technavio's an