Wages in the United States increased 4.86 percent in August of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Wages in Germany increased 1.90 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Germany Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Wages In the Euro Area increased 3.70 percent in June of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Euro Area Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Wages in Italy increased 2.80 percent in July of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Italy Hourly Wage Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates 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.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

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.

Wages in Poland increased 7.50 percent in September of 2025 over the same month in the previous year. This dataset provides - Poland Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average weekly earnings, average hourly wage rate and average usual weekly hours by union status and type of work, last 5 years.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

Wages in Norway increased 4.80 percent in June of 2025 over the same month in the previous year. This dataset provides - Norway Wages in Manufacturing YoY- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 Gifford. 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 Gifford, the median income for all workers aged 15 years and older, regardless of work hours, was $51,016 for males and $38,558 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 24% between the median incomes of males and females in Gifford. With women, regardless of work hours, earning 76 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thevillage of Gifford.

- Full-time workers, aged 15 years and older: In Gifford, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,227, while females earned $52,083, leading to a 26% gender pay gap among full-time workers. This illustrates that women earn 74 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.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Gifford, showcasing a consistent income pattern irrespective of employment status.

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 Gifford median household income by race. You can refer the same here

Earnings and employment statistics from Pay As You Earn (PAYE) Real Time Information (RTI), UK, NUTS 1, 2 and 3 areas and local authorities, monthly, seasonally adjusted.

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.

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 Charlotte. 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 Charlotte, the median income for all workers aged 15 years and older, regardless of work hours, was $53,958 for males and $27,500 for females.

These income figures highlight a substantial gender-based income gap in Charlotte. Women, regardless of work hours, earn 51 cents for each dollar earned by men. This significant gender pay gap, approximately 49%, underscores concerning gender-based income inequality in the city of Charlotte.

- Full-time workers, aged 15 years and older: In Charlotte, among full-time, year-round workers aged 15 years and older, males earned a median income of $61,597, while females earned $42,917, leading to a 30% gender pay gap among full-time workers. This illustrates that women earn 70 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 Charlotte.

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 Charlotte median household income by race. You can refer the same here

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 Milwaukee. 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 Milwaukee, the median income for all workers aged 15 years and older, regardless of work hours, was $36,705 for males and $28,986 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 21% between the median incomes of males and females in Milwaukee. With women, regardless of work hours, earning 79 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Milwaukee.

- Full-time workers, aged 15 years and older: In Milwaukee, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,215, while females earned $47,635, resulting in a 9% gender pay gap among full-time workers. This illustrates that women earn 91 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Milwaukee.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Milwaukee.

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 Milwaukee median household income by race. You can refer the same here

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)

Number of employees, average hourly and weekly earnings (including overtime), and average weekly hours for the industrial aggregate excluding unclassified businesses, last 5 months.