Context

An anonymous salary survey has been conducted annually since 2015 among European IT specialists with a stronger focus on Germany. This year 1238 respondents volunteered to participate in the survey. The data has been made publicly available by the authors. The dataset contains rich information about the salary patterns among the IT professionals in the EU region and offers some great insights.

An accompanying article - IT Salary Survey December 2020 has also been published which goes deeper into the findings.

Acknowledgements 🙏🏻

Thanks to Ksenia Legostay for curating and analyzing the data. Additional thanks to Viktor Shcherban and Sergey Vasilyev for collaborating on the survey.

The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical _location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

This data comes from the 2018 salary survey data. Please notethe following:

1. Where able, the data has been altered to reflect the annual pay of the average employee in this city and in this position based on the city's salary range.
2. Not all salaries and wages of less than 1 FTE could be confirmed. As a result, some salaries for positions with less than 1 FTE are either the annual equivelant or the actual pay of the employee throughout a year (ex. if an employee would make $50,000 as a full time employee but only works half-time, some cities marked this as $50,000 or $25,000.
3. Educational attainment often was provided with a minimum education and experience as well as a preferred level. The minimum of both categories are provided.

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

Explore the progression of average salaries for graduates in Survey Research And Methodology/Statistics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Survey Research And Methodology/Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Survey Research And Methodology/Statistics, providing a clear picture of financial prospects post-graduation.

This statistic depicts the annual compensation among pediatricians in the U.S. according to different sources (organizations), as of 2018. According to Integrated Healthcare Strategies, annual salaries for pediatricians averaged some *** thousand U.S. dollars.

Graph and download economic data for Average Weekly Earnings of All Employees, Total Private (CES0500000011) from Mar 2006 to Jun 2025 about earnings, establishment survey, private, employment, and USA.

In 2024, software developers working as senior executives in the United Stated had an average salary of about *** thousand U.S. dollars, making it the highest paying job for software developers in the United States. engineering manager ranked second with *** thousand U.S. dollars.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

This statistic depicts the annual compensation among orthopedic surgeons in the U.S. according to different sources (organizations), as of 2018. According to Integrated Healthcare Strategies, annual salaries for orthopedic surgeons averaged some *** thousand U.S. dollars.

Graph and download economic data for Average Hourly Earnings of All Employees, Manufacturing (CES3000000003) from Mar 2006 to Jun 2025 about earnings, establishment survey, hours, wages, manufacturing, employment, and USA.

Azerbaijan Average Monthly Salary data was reported at 1,043.600 AZN in Mar 2025. This records a decrease from the previous number of 1,062.900 AZN for Feb 2025. Azerbaijan Average Monthly Salary data is updated monthly, averaging 298.200 AZN from Jan 1995 (Median) to Mar 2025, with 363 observations. The data reached an all-time high of 1,062.900 AZN in Feb 2025 and a record low of 6.800 AZN in Jan 1995. Azerbaijan Average Monthly Salary data remains active status in CEIC and is reported by The State Statistical Committee of the Republic of Azerbaijan. The data is categorized under Global Database’s Azerbaijan – Table AZ.G009: Average Monthly Salary: Statistical Classification of Economic Activities Rev 2.

Explore the progression of average salaries for graduates in Data Science; Computer Science from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Data Science; Computer Science relative to other fields. This data is essential for students assessing the return on investment of their education in Data Science; Computer Science, providing a clear picture of financial prospects post-graduation.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Conemaugh township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Conemaugh township, the median income for all workers aged 15 years and older, regardless of work hours, was $40,547 for males and $24,743 for females.

These income figures highlight a substantial gender-based income gap in Conemaugh township. Women, regardless of work hours, earn 61 cents for each dollar earned by men. This significant gender pay gap, approximately 39%, underscores concerning gender-based income inequality in the township of Conemaugh township.

- Full-time workers, aged 15 years and older: In Conemaugh township, among full-time, year-round workers aged 15 years and older, males earned a median income of $61,923, while females earned $48,333, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Conemaugh township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Conemaugh township median household income by race. You can refer the same here

This dataset includes annual salary, regular pay, incentive pay, and gross pay for employees under the County Executive and independently elected County officials for the years 2016 to the present, and is updated quarterly.

For December files, Annual Salary is the employee's annual salary or annualized wage as of the last pay period for the year, and the pay data fields (Regular Pay, Incentive Pay and Gross Pay) are payments made to the employee through the last pay period of the year. The June file contains the Annual Salary, and the pay data as of the last pay period in June. Note that the June file is replaced by the December file each year.

Union contracted salaries and wages which were not settled during the calendar year reflect the wage as of the end of the year. Regular Pay for these positions includes salaries and wages for the year it was paid, not the year it was earned.

In addition to salary or wages for days worked and retroactively settled contract payments, Regular Pay also includes pay for days such as holidays, sick days, and vacation days. Overtime Pay includes pay for extra work typically at a wage rate different from regular wages as set forth in a collective bargaining agreement. Incentive Pay includes such things as a wellness incentive and longevity pay.

Employee names are included in the dataset with the following exceptions permitted by the Pennsylvania Right to Know Law:

• The names of individuals who were active sworn law enforcement officers during the year; and
• Information that would disclose individually identifiable health information.

Additionally, records related to Court of Common Pleas employees would need to be requested from the Courts.

In March 2022, the salary data files prior to 2021 were updated so that all columns matched for consistent presentation.

Salary information for State of Oklahoma employees by pay band, including average, maximum and minimum, and the size of the range by pay band.

Graph and download economic data for Average Hourly Earnings of Production and Nonsupervisory Employees, Total Private (AHETPI) from Jan 1964 to May 2025 about nonsupervisory, headline figure, earnings, average, establishment survey, hours, wages, production, private, employment, and USA.

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) 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.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

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 twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: 16 years and over (LEU0252881500Q) from Q1 1979 to Q1 2025 about second quartile, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Certified Report of Public Employment and Compensation for as submitted by the EIN Name of “City of Bloomington” This data is reported exactly as entered by local officials. Use of this data must be pursuant to Indiana Code 5-14-3-3(f), thus any information, including the names and addresses of government employees, obtained by viewing, printing and/or downloading will not be used for commercial or political purposes. The following dataset is updated nightly and pulls from the City of Bloomington Payroll records dataset. Please keep the following in mind when viewing or visualizing the data: Compensation is the preferred term over salaries due to the fact that almost all employees are paid hourly. The only Salaried employees are those in elected positions (Mayor, Clerk, City Council people). For historical completed years, an employee’s compensation may include items such as, but not limited to: overtime, certifications, “on call” pay, etc. For past years, the compensation would be as reported to the IRS with an effective date of the last day of the year. All data within the current year is a predicted compensation and may not reflect what the compensation will be by the end of the year. Previous years reflect what compensation was actually earned. The “City of Bloomington '' has a wide variety of employee types: Regular Full Time, Regular Part Time, Temporary, Seasonal, Union and Non Union employees. Temporary and Seasonal employees can have multiple jobs at different pay rates. This data set reflects a combination of all these variables and just sums the compensation into one yearly amount.