Wages in the United States increased to 31.18 USD/Hour in May from 31.06 USD/Hour in April of 2025. This dataset provides - United States Average Hourly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Manufacturing in the United States increased to 28.92 USD/Hour in May from 28.80 USD/Hour in April of 2025. This dataset provides - United States Average Hourly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for WAGE GROWTH reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2023, members of labor unions had higher median weekly earnings across all age groups. Union members aged between 16 and 24 for example, had a median weekly wage of around 809 U.S. dollars, while non-union members of the same age range had a median weekly wage of 709 U.S. dollars. Non-union affiliated jobs had the lowest median earnings.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

United States US: Wages Index: Manufacturing data was reported at 109.822 2010=100 in 2016. This records an increase from the previous number of 107.005 2010=100 for 2015. United States US: Wages Index: Manufacturing data is updated yearly, averaging 44.941 2010=100 from Dec 1948 (Median) to 2016, with 69 observations. The data reached an all-time high of 109.822 2010=100 in 2016 and a record low of 6.468 2010=100 in 1948. United States US: Wages Index: Manufacturing data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Wages, Labour Cost and Employment Index: Annual.

MLB Players Salaries And Performance

Analyzing Salaries, Contract Length, and Team Performance

By Nate Reed [source]

About this dataset

This dataset contains information about Major League Baseball players’ salaries and contracts, sourced from USA Today. It includes information like the player's salary for the current season, total contract value, position they play, number of years their contract is for and average annual salary. This dataset allows you to explore MLB player contracts at a deeper level, examine differences between players' salaries across different positions and teams, identify which teams are paying their players the most per annum or over the duration of full contracts

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Featured Notebooks

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How to use the dataset

This dataset provides detailed salary and contract information for Major League Baseball players. It contains all the most up-to-date information about each player's contract, including salary, total value, position, years, average annual salary, and team affiliation. With this data you can analyze trends in player salaries and contracts to identify opportunities for maximizing profits.

You can also use this data to compare the relative worth of players at different positions across teams. Use it to research trade value of players - including estimated trade values based on their contracts - as well as provide statistical analysis of the effects that player moves have had on teams' success. Additionally, you can utilize it to build predictive models that use past contracts to predict future salary increases or decreases when negotiating new contracts with existing or prospective players.

Ready to get started? Here are a few tips on how best to utilize this dataset: - Examine the Total Value column first since it is often a key indicator in determining a player's worth; - Look at previous years’ salaries by team for comparision purposes;
- Factor in performance metrics like OPS (on-base plus slugging percentage), ERA (earned run average), WHIP (walks + hits/innings pitched), FIP (fielding independent pitching); - Take into account intangibles such as fan interest/popularity; - Utilize averages across different positions and teams – are certain players way underpaid compared his peers? Conversely are certain overpaid compared his peers? Finding these mismatches could potentially create an arbitrage opportunity if a trade were made.

By understanding how successful teams build rosters using Major League Baseball Player Salaries and Contracts datasets you too can be empowered with data driven decisions when investing in your fantasy baseball team or MLB organization!

Research Ideas

• Analyzing which teams are spending the most on salary, and determining how that is affecting their performance.
• Comparing positions to see which positions earn more money across teams and leagues.
• Identifying trends in salaries for larger contracts vs smaller ones, to help players and teams determine better negotiating strategies for future signings

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: salaries.csv | Column name | Description | |:----------------|:-------------------------------------------------------------| | salary | The amount of money a player is paid for a season. (Numeric) | | name | The name of the player. (String) | | total_value | The total value of the player's contract. (Numeric) | | pos | The position the player plays. (String) | | years | The length of the player's contract. (Numeric) | | avg_annual | The average annual salary of the player. (Numeric) | | team | The team the player plays for. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Nate Reed.

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

This dataset provides a comprehensive overview of key economic and educational statistics in the United States, compiled from the most recent data available from 2022 to 2024. It includes information on poverty line thresholds, minimum and living wages, average and median incomes, starting and average salaries for teachers, and school enrollment rates.

This dataset is essential for exploring salary trends among the general population and educators, as well as school enrollment rates, offering a deep dive into current socioeconomic trends in the United States.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that include salary and compensation data. The dataset consists of salary data by employee reported by State Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

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.

Overview This dataset provides insights into salary distributions across various job classifications, enabling a deeper understanding of compensation trends across industries, experience levels, and geographical locations. It serves as a valuable resource for HR professionals, job seekers, researchers, and policymakers aiming to analyze pay scales, wage gaps, and salary progression trends.

Data Sources The data is aggregated from multiple employment and compensation reports, salary surveys, and publicly available job postings. It has been cleaned, standardized, and structured to ensure consistency and usability for analytical purposes.

Features Job Title: Specific title of the job (e.g., Data Analyst, Software Engineer, Marketing Manager).

Job Classification: Broad category of jobs (e.g., IT, Finance, Healthcare, Education).

Industry: The sector in which the job belongs (e.g., Technology, Banking, Retail).

Experience Level: Categorized as Entry-level, Mid-level, or Senior-level.

Education Requirement: Minimum qualification required for the job role.

Average Salary (INR/USD/Other Currency): The median or mean salary for a particular job classification.

Salary Range: The minimum and maximum salary offered for a role.

Location: Country or region where the job is based.

Employment Type: Full-time, Part-time, Contract, or Remote.

Company Size: Small, Medium, or Large enterprises.

Potential Use Cases Salary Benchmarking: Compare salary expectations across industries and job roles.

Career Planning: Identify lucrative career paths based on salary trends.

Wage Gap Analysis: Examine salary disparities by gender, location, or experience level.

Cost of Living Adjustments: Assess salaries relative to regional economic conditions.

HR and Recruitment Strategies: Optimize compensation packages to attract top talent.

Acknowledgments The dataset is compiled from various salary reports and job market research sources. Special thanks to contributors and organizations providing employment data for analysis.

License This dataset is shared for educational, research, and analytical purposes. Please ensure compliance with relevant data usage policies before any commercial applications.

Get Started The dataset can be explored using Python (Pandas), R, SQL, or visualization tools like Tableau and Power BI. Sample notebooks and analyses are available in the Kaggle notebook section.

This dataset provides values for WAGES IN MANUFACTURING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Occupational Employment and Wage Statistics (OEWS) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OEWS estimates are constructed from a sample of about 41,400 establishments. Each year, forms are mailed to two semiannual panels of approximately 6,900 sampled establishments, one panel in May and the other in November.

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 North Carolina. 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 2022

Based on our analysis ACS 2022 1-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In North Carolina, the median income for all workers aged 15 years and older, regardless of work hours, was $42,649 for males and $30,657 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in North Carolina. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thestate of North Carolina.

- Full-time workers, aged 15 years and older: In North Carolina, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,390, while females earned $48,599, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 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 state of North Carolina.

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 North Carolina.

https://i.neilsberg.com/ch/north-carolina-income-by-gender.jpeg" alt="North Carolina gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 North Carolina median household income by gender. You can refer the same here

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Bachelor's degree and higher: 25 years and over (LEU0252918500Q) from Q1 2000 to Q1 2025 about second quartile, full-time, 25 years +, tertiary schooling, salaries, workers, earnings, education, wages, median, employment, and USA.

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 State College. 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 State College, the median income for all workers aged 15 years and older, regardless of work hours, was $11,239 for males and $8,018 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in State College. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in theborough of State College.

- Full-time workers, aged 15 years and older: In State College, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,601, while females earned $54,907, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 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 borough of State College.

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 State College.

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

The gender wage gap indicator compares the median earnings between male and female workers in Champaign County.

Two worker populations are analyzed: all workers, including part-time and seasonal workers and those that were not employed for the full survey year; and full-time, year-round workers. The gender wage gap is included because it blends economics and equity, and illustrates that a major economic talking point on the national level is just as relevant at the local scale.

For all four populations (male full-time, year-round workers; female full-time, year-round workers; all male workers; and all female workers), the estimated median earnings were higher in 2023 than in 2005. The greatest increase in a population’s estimated median earnings between 2005 and 2023 was for female full-time, year-round workers; the smallest increase between 2005 and 2023 was for all female workers. In both categories (all and full-time, year-round), the estimated median annual earnings for male workers was consistently higher than for female workers.

The gender gap between the two estimates in 2023 was larger for full-time, year-round workers than all workers. For full-time, year-round workers, the difference was $11,863; for all workers, it was approaching $9,700.

The Associated Press wrote this article in October 2024 about how Census Bureau data shows that in 2023 in the United States, the gender wage gap between men and women working full-time widened year-over-year for the first time in 20 years.

Income data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Median Earnings in the Past 12 Months (in 2020 Inflation-Adjusted Dollars) by Sex by Work Experience in the Past 12 Months for the Population 16 Years and Over with Earnings in the Past 12 Months.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (20 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (21 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).

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 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.