Similar Datasets

• Company Bankruptcy Prediction: LINK
• The Boston House-Price Data: LINK
• California Housing Prices Data (5 new features!): LINK
• Spanish Wine Quality Dataset: LINK

Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

In 2023, the Rhode Island had the highest earnings ratio for women, as female workers earned ***** percent of their male counterparts on average. The state of Louisiana had the lowest earnings ratio for female workers, who earned ***** percent of what their male counterparts earn.

As of May 2023, the hourly wage of anesthesiologists employed in the United States ranged from around 107 U.S. dollars per hour to around 219 U.S. dollars per hour, by state. Nebraska had the highest hourly wage for anesthesiologists in the United States, whereas South Carolina had the lowest.

The statistic shows the female to male earnings ratio in the United States in the fourth quarter of 2022, based on the median income in current U.S. dollars, by age group. In the fourth quarter of 2022, the earnings ratio of female to male workers aged between 16 to 24 years was at about 92.9 percent.

In 2021, female employee earnings were outpaced by male earnings across nearly all industries, with sharp disparities in the professional and technical services industry, as well as the finance and insurance industry. In that year, there were no industries in which women earned more than men.

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).

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 United States. 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 United States, the median income for all workers aged 15 years and older, regardless of work hours, was $48,138 for males and $32,546 for females.

These income figures highlight a substantial gender-based income gap in United States. Women, regardless of work hours, earn 68 cents for each dollar earned by men. This significant gender pay gap, approximately 32%, underscores concerning gender-based income inequality in the country of United States.

- Full-time workers, aged 15 years and older: In United States, among full-time, year-round workers aged 15 years and older, males earned a median income of $67,966, while females earned $54,999, leading to a 19% gender pay gap among full-time workers. This illustrates that women earn 81 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 United States.

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

In 2024, the female-to-male earnings ratio in the United States was 80.9 percent. This reflected a significant decrease from a peak of 84 percent in 2022. This data shows the earnings ratio of women in full-time, year-round work, relative to men. The highest gender pay gap in the country was in Louisiana.

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 Maryland. 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 Maryland, the median income for all workers aged 15 years and older, regardless of work hours, was $58,694 for males and $42,513 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 Maryland. 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 Maryland.

- Full-time workers, aged 15 years and older: In Maryland, among full-time, year-round workers aged 15 years and older, males earned a median income of $81,332, while females earned $70,237, 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 state of Maryland.

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 Maryland.

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

A gender wage gap exists within medical specialties in the United States, with females earning less than their male counterparts across all medical specialties, no exceptions. In 2024, gender wage gaps exceeded 11 percent, except one specialty including Oncology, which had a 12 percent pay gap.

Context

In the United Kingdom, it's mandated by law that any organization employing over 250 individuals must disclose their gender pay gap data annually. This is not to be mistaken with equal pay, which is the legal obligation to pay men and women the same for equivalent work. The gender pay gap, on the other hand, is a broader measure that looks at the average differences in pay, seniority, and career advancement between male and female employees. This makes it a more potent indicator of gender equality and institutional bias within organizations.

Content

Geography: United Kingdom

Time period: 2018-2023

Unit of analysis: UK Gender Pay Gap

Dataset: This dataset currently contains data collected by the Gender Pay Gap Service for the 2018 to 2023 reporting years. More data will be added as it becomes available.

At present, the Gender Pay Gap Service only provides data downloads in CSV format, divided by the reporting year. This dataset amalgamates all the available CSV files, with column descriptions and file introductions informed by my firsthand experience working on the Gender Pay Gap Service website for the Government Equalities Office.

Variables

Field	Description	Source
EmployerName	The name of the employer at the time of reporting	Via CoHo API or manually entered by user
EmployerID	Unique ID assigned to each employer that is consistent across every reporting year	Generated by the system
Address	The current registered address of the employer	Via CoHo API or manually entered by user
PostCode	The postal code of the current registered address of the employer	Via CoHo API or manually entered by user
CompanyNumber	The Company Number of the employer as listed on Companies House (null for public sector)	Via CoHo API
SicCodes	List of comma-separated SIC codes used to describe the employer's purpose and sectors of work	Via CoHo API or manually entered by user
DiffMeanHourlyPercent	Mean % difference between male and female hourly pay (negative = women's mean hourly pay is higher)	Entered by a user when reporting GPG data
DiffMedianHourlyPercent	Median % difference between male and female hourly pay (negative = women's median hourly pay is higher)	Entered by a user when reporting GPG data
DiffMeanBonusPercent	Mean % difference between male and female bonus pay (negative = women's mean bonus pay is higher)	Entered by a user when reporting GPG data
DiffMedianBonusPercent	Median % difference between male and female bonus pay (negative = women's median bonus pay is higher)	Entered by a user when reporting GPG data
MaleBonusPercent	Percentage of male employees paid a bonus	Entered by a user when reporting GPG data
FemaleBonusPercent	Percentage of female employees paid a bonus	Entered by a user when reporting GPG data
MaleLowerQuartile	Percentage of males in the lower hourly pay quarter	Entered by a user when reporting GPG data
FemaleLowerQuartile	Percentage of females in the lower hourly pay quarter	Entered by a user when reporting GPG data
MaleLowerMiddleQuartile	Percentage of males in the lower middle hourly pay quarter	Entered by a user when reporting GPG data
FemaleLowerMiddleQuartile	Percentage of females in the lower middle hourly pay quarter	Entered by a user when reporting GPG data
MaleUpperMiddleQuartile	Percentage of males in the upper middle hourly pay quarter	Entered by a user when reporting GPG data
FemaleUpperMiddleQuartile	Percentage of females in the...

Many people say the gender gap in income levels is overstated in the United States, where some say that inequality in the labor force is a thing of the past. Is there a gender gap at all? Is it stronger in some industries than in others?

This dataset, retrieved from the Bureau of Labor Statistics, shows the median weekly incomes for 535 different occupations. The data encompasses information for all working American citizens as of January 2015. The incomes are broken into male and female statistics, preceded by the total median income when including both genders. The data has been re-formatted from the original PDF-friendly arrangement to make it easier to clean and analyze.

Analysis thus far has found that there is indeed a sizable gender gap between male and female incomes. Use of this dataset should cite the Bureau of Labor Statistics as per their copyright information:

The Bureau of Labor Statistics (BLS) is a Federal government agency and everything that we publish, both in hard copy and electronically, is in the public domain, except for previously copyrighted photographs and illustrations. You are free to use our public domain material without specific permission, although we do ask that you cite the Bureau of Labor Statistics as the source.

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 Washington. 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 Washington, the median income for all workers aged 15 years and older, regardless of work hours, was $57,882 for males and $37,403 for females.

These income figures highlight a substantial gender-based income gap in Washington. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the state of Washington.

- Full-time workers, aged 15 years and older: In Washington, among full-time, year-round workers aged 15 years and older, males earned a median income of $82,246, while females earned $65,541, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 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 Washington.

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 Washington 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 West Virginia. 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 West Virginia, the median income for all workers aged 15 years and older, regardless of work hours, was $39,828 for males and $25,683 for females.

These income figures highlight a substantial gender-based income gap in West Virginia. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the state of West Virginia.

- Full-time workers, aged 15 years and older: In West Virginia, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,446, while females earned $45,809, leading to a 23% gender pay gap among full-time workers. This illustrates that women earn 77 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 West Virginia.

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

As of 2023, South Korea is the country with the highest gender pay gap among OECD countries, with a **** percent difference between the genders. The gender pay gap displays the difference between the median wages of full-time employed men and full-time employed women.

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 Sewall'S Point. 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 Sewall'S Point, the median income for all workers aged 15 years and older, regardless of work hours, was $77,073 for males and $37,356 for females.

These income figures highlight a substantial gender-based income gap in Sewall'S Point. Women, regardless of work hours, earn 48 cents for each dollar earned by men. This significant gender pay gap, approximately 52%, underscores concerning gender-based income inequality in the town of Sewall'S Point.

- Full-time workers, aged 15 years and older: In Sewall'S Point, among full-time, year-round workers aged 15 years and older, males earned a median income of $134,861, while females earned $91,875, leading to a 32% gender pay gap among full-time workers. This illustrates that women earn 68 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment 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 Sewall'S Point, 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 Sewall'S Point median household income by race. You can refer the same here

This dataset provides a comprehensive view of wage differences between college graduates and high school graduates in the United States from 1973 to 2022.

The data is sourced from the Economic Policy Institute's State of Working America Data Library and includes adjusted wages.

Interesting Task Ideas:

1. Analyze the overall trend in the wage gap between college graduates and high school graduates over the years.
2. Investigate whether the wage gap has been narrowing or widening for different genders.
3. Determine the year(s) when the wage gap was at its lowest and highest points.
4. Identify the demographic group(s) that have experienced the largest increase in wages over time.
5. Compare the wage gap between men and women within each educational group and analyze how it has changed over the years.
6. Create visualizations to visualize and compare wage trends for high school graduates and college graduates across different time periods.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

13K Reddit Tips

150K TMDb TV Shows

Employment-to-Population Ratio for USA

Clash of Clans Clans Dataset 2023 (3.5M Clans)

Gender Wage Gap in the USA

Photo by Omar Lopez on Unsplash

This dataset contains gender pay gap figures for all employees in London and large employers in London. The pay gap figures for GLA group organisations can be found on their respective websites. The gender pay gap is the difference in the average hourly wage of all men and women across a workforce. If women do more of the less well paid jobs within an organisation than men, the gender pay gap is usually bigger. The UK government publish gender pay gap figures for all employers with 250 or more employees. A cut of this dataset that only shows employers that are registered in London can be found below.

Read a report by the Local Government Association (LGA) that summarises the mean and median pay gaps in local authorities, as well as the distribution of staff across pay quartiles.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

This dataset is one of the Greater London Authority's measures of Economic Development strategy. Click here to find out more.

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 Wisconsin. 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 Wisconsin, the median income for all workers aged 15 years and older, regardless of work hours, was $50,309 for males and $33,948 for females.

These income figures highlight a substantial gender-based income gap in Wisconsin. Women, regardless of work hours, earn 67 cents for each dollar earned by men. This significant gender pay gap, approximately 33%, underscores concerning gender-based income inequality in the state of Wisconsin.

- Full-time workers, aged 15 years and older: In Wisconsin, among full-time, year-round workers aged 15 years and older, males earned a median income of $65,797, while females earned $53,168, leading to a 19% gender pay gap among full-time workers. This illustrates that women earn 81 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 Wisconsin.

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 Wisconsin 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 Pennsylvania. 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 Pennsylvania, the median income for all workers aged 15 years and older, regardless of work hours, was $48,918 for males and $31,608 for females.

These income figures highlight a substantial gender-based income gap in Pennsylvania. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the state of Pennsylvania.

- Full-time workers, aged 15 years and older: In Pennsylvania, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,071, while females earned $55,831, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 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 Pennsylvania.

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