Dataset Summary This dataset provides monthly synthetic financial statement data for McDonald's Corporation, spanning from January 2005 to December 2024 (20 years, 240 rows). The structure and field types closely follow actual historical reports, but all values are artificially generated to simulate realistic trends, growth, and variability in key financial metrics.

Disclaimer: This dataset is synthetic and was programmatically generated for educational and analytical purposes. It does not reflect actual financial results of McDonald's.

Columns & Descriptions Column Name Description Date Month of the record (YYYY-MM) Market cap ($B) Market capitalization (billion USD) Revenue ($B) Revenue (billion USD) Earnings ($B) Earnings/Net income (billion USD) P/E ratio Price-to-Earnings ratio P/S ratio Price-to-Sales ratio P/B ratio Price-to-Book ratio Operating Margin (%) Operating margin percentage EPS ($) Earnings per share (USD) Shares Outstanding ($B) Shares outstanding (in billions) Cash on Hand ($B) Cash on hand (billion USD) Dividend Yield (%) Dividend yield percentage Dividend (stock split adjusted) ($) Dividend per share, adjusted for splits (USD) Net assets ($B) Net assets (billion USD) Total assets ($B) Total assets (billion USD) Total debt ($B) Total debt (billion USD) Total liabilities ($B) Total liabilities (billion USD)

Data Generation Synthetic Approach: All values are programmatically generated to simulate plausible historical trends and volatility, based on actual McDonald's data structure and real-world financial logic.

Monthly Granularity: Data points are provided for every month, offering high temporal resolution suitable for time-series analysis.

No Real Data: No actual McDonald's confidential or proprietary data is included.

Example Use Cases Financial time series modeling & forecasting

Data visualization practice

Building dashboards and BI demos

Educational purposes (finance, data science, statistics)

Benchmarking financial data analysis algorithms

Acknowledgements Dataset inspired by public McDonald's annual financial reports.

SELECTED ECONOMIC CHARACTERISTICS PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL - DP03 Universe - All families and All People Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Poverty statistics in American Community Survey (ACS) products adhere to the standards specified by the Office of Management and Budget in Statistical Policy Directive 14. The Census Bureau uses a set of dollar value thresholds that vary by family size and composition to determine who is in poverty. Further, poverty thresholds for people living alone or with nonrelatives (unrelated individuals) vary by age (under 65 Year or 65 Year and older). The poverty thresholds for two-person families also vary by the age of the householder. If a family’s total income is less than the dollar value of the appropriate threshold, then that family and every individual in it are considered to be in poverty. Similarly, if an unrelated individual’s total income is less than the appropriate threshold, then that individual is considered to be in poverty.

This dataset provides a comprehensive collection of key U.S. macroeconomic indicators spanning the past 25 years (approximately 1998–2023). It includes monthly data on:

M2 Money Supply (M2SL): A broad measure of money in circulation, including cash, checking deposits, and easily convertible near money. Federal Funds Effective Rate (FEDFUNDS): The interest rate at which depository institutions trade federal funds with each other overnight. Interest Rates: Various benchmark interest rates relevant to economic analysis. 10-Year Treasury Constant Maturity Rate (GS10): Reflects market expectations for long-term interest rates and economic growth. All data are sourced from the Federal Reserve Economic Data (FRED) database and are seasonally adjusted where applicable.

This dataset is ideal for economic research, financial modeling, market forecasting, and machine learning applications where macroeconomic variables are relevant. The data is cleaned, merged, and formatted for immediate use, with date-stamped entries aligned on a monthly frequency.

Source: Federal Reserve Economic Data (FRED) — https://fred.stlouisfed.org/

License: CC0: Public Domain

This dataset contains a series of indicators related to income and expenditure for Kiribati, Tuvalu and Vanuatu based on Household Income and Expenditure Surveys (HIES). Indicators included are the following: Number of households, Proportion of households, Number of persons, Proportion of persons, Income, Income per household, Income per person, Proportion of income, Expenditure, Expenditure per household, Expenditure per person, Proportion of expenditure. The table provides a breakdown by geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories). This dataset has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

Household Saving Rate in the United States decreased to 4.60 percent in August from 4.80 percent in July of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Very Important

• Check out the new must-see kernel for this dataset Click Here
• Make Sure to upvote for more datasets and kernel :D

Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

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

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

Consumer Spending in the United States increased to 16445.70 USD Billion in the second quarter of 2025 from 16345.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Personal Spending in the United States increased 0.60 percent in August of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Person County, NC, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Person County, NC reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Person County households based on income levels.

Key observations

• For Family Households: In Person County, the majority of family households, representing 14.89%, earn $100,000 to $124,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 1.08%, have incomes falling $150,000 to $199,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Person County, the majority of non-family households, accounting for 13.32%, have income $10,000 to $14,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 1.08%, earn $150,000 to $199,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Person County, NC (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Person County, NC
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Person County, NC
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Person County, NC

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 Person County median household income. 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 Alcorn County. 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 Alcorn County, the median income for all workers aged 15 years and older, regardless of work hours, was $31,545 for males and $23,248 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 26% between the median incomes of males and females in Alcorn County. With women, regardless of work hours, earning 74 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecounty of Alcorn County.

- Full-time workers, aged 15 years and older: In Alcorn County, among full-time, year-round workers aged 15 years and older, males earned a median income of $48,190, while females earned $35,523, 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 Alcorn County, 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 Alcorn County 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 Stillwater. 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 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Stillwater, the median income for all workers aged 15 years and older, regardless of work hours, was $56,601 for males and $40,465 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 Stillwater. 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 thecity of Stillwater.

- Full-time workers, aged 15 years and older: In Stillwater, among full-time, year-round workers aged 15 years and older, males earned a median income of $80,000, while females earned $74,709, resulting in a 7% gender pay gap among full-time workers. This illustrates that women earn 93 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 Stillwater.

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 Stillwater.

https://i.neilsberg.com/ch/stillwater-mn-income-by-gender.jpeg" alt="Stillwater, MN gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-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 Stillwater median household income by gender. 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 Norway. 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 Norway, the median income for all workers aged 15 years and older, regardless of work hours, was $49,167 for males and $38,750 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 Norway. 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 Norway.

- Full-time workers, aged 15 years and older: In Norway, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,333, while females earned $38,750, leading to a 39% gender pay gap among full-time workers. This illustrates that women earn 61 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 lower gender pay gap percentage. This indicates that Norway offers better opportunities for women in non-full-time positions.

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 Norway 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 Heron Lake township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Heron Lake township, the median income for all workers aged 15 years and older, regardless of work hours, was $45,313 for males and $40,000 for females.

Based on these incomes, we observe a gender gap percentage of approximately 12%, indicating a significant disparity between the median incomes of males and females in Heron Lake township. Women, regardless of work hours, still earn 88 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Heron Lake township, among full-time, year-round workers aged 15 years and older, males earned a median income of $65,938, while females earned $61,250, resulting in a 7% gender pay gap among full-time workers. This illustrates that women earn 93 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 township of Heron Lake township.

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 Heron Lake township, 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 Heron Lake township 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 Hutchinson. 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 Hutchinson, the median income for all workers aged 15 years and older, regardless of work hours, was $39,785 for males and $26,452 for females.

These income figures highlight a substantial gender-based income gap in Hutchinson. Women, regardless of work hours, earn 66 cents for each dollar earned by men. This significant gender pay gap, approximately 34%, underscores concerning gender-based income inequality in the city of Hutchinson.

- Full-time workers, aged 15 years and older: In Hutchinson, among full-time, year-round workers aged 15 years and older, males earned a median income of $55,082, while females earned $39,260, leading to a 29% gender pay gap among full-time workers. This illustrates that women earn 71 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 Hutchinson.

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 Hutchinson 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 Pendleton. 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 Pendleton, the median income for all workers aged 15 years and older, regardless of work hours, was $36,312 for males and $33,883 for females.

Based on these incomes, we observe a gender gap percentage of approximately 7%, indicating a significant disparity between the median incomes of males and females in Pendleton. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Pendleton, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,776, while females earned $53,339, resulting in a 3% gender pay gap among full-time workers. This illustrates that women earn 97 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 Pendleton.

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