Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Ringcentral Inc. RingCentral, Inc., together with its subsidiaries, provides cloud business communications, contact center, video, and hybrid event solutions in North America and internationally. The company's products include RingEX, a unified communications as a service platform for collaboration across voice, messaging, and video; RingCentral Contact Center, a contact center solution that delivers omni-channel and workforce engagement solutions; and RingCX, a contact center as a service solution for customer engagement with CRM integrations. It also offers artificial intelligence (AI) solutions, such as AI Receptionist, an AI phone agent; AI Assistant, which automates conversation recaps, captures notes, and summarizes actions; RingSense for transforming conversations into actionable conversational intelligence, sentiment and trend analysis, and sales intelligence and analyzing customer interactions; AI-based Quality Management for coaching and operational insights; AI Agent Assist that provides real-time suggestions and contextual responses; AI Supervisor Assist for real-time monitoring, coaching, and sentiment analysis; and RingCentral for Microsoft Teams. In additions, the company provides RingCentral Events, which enables businesses to host virtual, hybrid, and in-person events with AI-powered engagement tools; and sells pre-configured phones and professional services. It serves a range of industries, including financial services, education, healthcare, legal services, real estate, retail, technology, insurance, construction, hospitality, and state and local government, and others. The company sells its products to enterprise customers, and small and medium-sized businesses through resellers and distributors, partners, and global service providers. RingCentral, Inc. was incorporated in 1999 and is headquartered in Belmont, California.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Intuit Inc. Intuit Inc. provides financial management, payments and capital, compliance, and marketing products and services in the United States. The company operates in four segments: Global Business Solutions, Consumer, Credit Karma, and ProTax. The SGlobal Business Solutions segment provides QuickBooks services, which include financial and business management online services, desktop software, payroll solutions, time tracking, merchant payment processing and bill pay solutions, checking accounts, and financing services for small and mid-market businesses; and Mailchimp, a marketing automation and customer relationship management. This segment also offers QuickBooks online services and desktop software solutions comprising QuickBooks Online, QuickBooks Live, QuickBooks Online Advanced, QuickBooks Self-Employed, QuickBooks Solopreneur financial and business management offerings, QuickBooks Online Payroll, QuickBooks Checking, QuickBooks Desktop software subscriptions, and QuickBooks Assisted Payroll. The Consumer segment provides do-it-yourself and assisted TurboTax income tax preparation products and services. The Credit Karma segment offers consumers with a personal finance platform that provides recommendations for credit card, home, auto, and personal loan, and insurance products; online savings and checking accounts; and access to its credit scores and reports, credit and identity monitoring, credit report dispute, credit building tools, and tools. The ProTax segment provides Lacerte, ProSeries, and ProFile desktop tax-preparation software products; and ProConnect Tax Online bill pay tax products, electronic tax filing service, and bank products and related services. It sells products and services through direct sales channels, multichannel shop-and-buy experiences, mobile application stores, and partner and other channels. Intuit Inc. was founded in 1983 and is headquartered in Mountain View, California.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Focus Technology Co Ltd. Focus Technology Co., Ltd. operates e-commerce platforms in the People's Republic of China and internationally. The company operates Made-in-China.com, a service platform that offers an online directory of various products of Chinese suppliers; inQbrands.com, which offers services in the areas of product sourcing, cross-border services, warehousing and logistics, product development and design, branding and marketing, and sales strategy and execution; and Doba platform that offers drop shipping services. It also operates Crov.com, a one stop wholesale service platform; and XinYiZhan Insurance Network, an online insurance shopping platform. In addition, the company is involved in internet applications; investment management; purchasing agent and business services; insurance adjuster services; import and export agency services; educational technology development; loan and guarantee services; engineering project management; trade and business consulting; and customer services. Focus Technology Co., Ltd. was founded in 1996 and is headquartered in Nanjing, the People's Republic of China.

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 Silver Creek 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 Silver Creek township, the median income for all workers aged 15 years and older, regardless of work hours, was $69,444 for males and $40,132 for females.

These income figures highlight a substantial gender-based income gap in Silver Creek township. Women, regardless of work hours, earn 58 cents for each dollar earned by men. This significant gender pay gap, approximately 42%, underscores concerning gender-based income inequality in the township of Silver Creek township.

- Full-time workers, aged 15 years and older: In Silver Creek township, among full-time, year-round workers aged 15 years and older, males earned a median income of $104,167, while females earned $72,813, leading to a 30% gender pay gap among full-time workers. This illustrates that women earn 70 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

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

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Silver Creek 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 Owen 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 Owen County, the median income for all workers aged 15 years and older, regardless of work hours, was $42,036 for males and $28,091 for females.

These income figures highlight a substantial gender-based income gap in Owen County. 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 county of Owen County.

- Full-time workers, aged 15 years and older: In Owen County, among full-time, year-round workers aged 15 years and older, males earned a median income of $58,051, while females earned $48,198, leading to a 17% gender pay gap among full-time workers. This illustrates that women earn 83 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 Owen County.

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

📨 Direct Mail Marketing Response Dataset

This dataset focuses on predicting which customers are most likely to respond to a direct mail marketing promotion.
It is based on real data from a clothing store chain in New England.

• Rows: 28,799 customers
• Features: 51 fields (customer demographics, purchase history, and marketing interactions)
• Target: RESP (whether a customer responded to a promotion)

📊 Dataset Details

Each row corresponds to a unique customer, with information about spending behavior, product preferences, and marketing exposure.

Key Fields

• HHKEY → Unique encrypted customer ID
• ZIP_CODE → Customer’s ZIP code
• FRE → Number of purchase visits
• MON → Total net sales
• AVRG → Average spend per visit
• AMSPEND, PSSPEND, CSSPEND, AXSPEND → Spend across four different franchise brands
• OMONSPEND, TMONSPEND, SMOSPEND → Spend over past 1, 3, and 6 months
• PREVPD → Spend in the same period last year
• GMP → Gross margin percentage
• PROMOS → Number of marketing promotions on file
• DAYS → Number of days customer has been on file
• FREDAYS, LTFREDAY → Time between purchases (recent & lifetime average)
• CLASSES → Number of different product classes purchased
• STYLES → Number of individual items purchased
• STORES → Number of stores shopped at
• MARKDOWN, COUPONS, MAILED, RESPONDE, RESPONSERATE → Promotion and discount engagement
• HI → Product uniformity (lower = more diverse purchases)
• CLUSTYPE → Lifestyle cluster type (encrypted)
• PERCRET → Percent of returns
• CC_CARD, VALPHON, WEB → Flags for credit card, valid phone number, and web shopper status

🛍 Clothing Category Spend Variables

Variables: PSWEATERS, PKNIT_TOPS, PKNIT_DRES, PBLOUSES,PJACKETS, PCAR_PNTS, PCAS_PNTS, PSHIRTS, PDRESSES, PSUITS, POUTERWEAR, PJEWELRY, PFASHION, PLEGWEAR, PCOLLSPND; AC_CALC20

Percentages of spend across 15 clothing/product categories:

sweaters, knit tops, knit dresses, blouses, jackets, career pants, casual pants, shirts, dresses, suits, outerwear, jewelry, fashion, legwear, collectibles

🎯 Target Variable

• RESP → Binary variable indicating whether the customer responded to a mailed promotion.

Credits: https://iscap.pt/~aazevedo/variaveis.html

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 Holden 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 Holden township, the median income for all workers aged 15 years and older, regardless of work hours, was $58,250 for males and $33,472 for females.

These income figures highlight a substantial gender-based income gap in Holden township. Women, regardless of work hours, earn 57 cents for each dollar earned by men. This significant gender pay gap, approximately 43%, underscores concerning gender-based income inequality in the township of Holden township.

- Full-time workers, aged 15 years and older: In Holden township, among full-time, year-round workers aged 15 years and older, males earned a median income of $74,375, while females earned $51,875, leading to a 30% gender pay gap among full-time workers. This illustrates that women earn 70 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

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

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Holden 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 Spring 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 Spring Lake township, the median income for all workers aged 15 years and older, regardless of work hours, was $57,910 for males and $36,558 for females.

These income figures highlight a substantial gender-based income gap in Spring Lake township. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the township of Spring Lake township.

- Full-time workers, aged 15 years and older: In Spring Lake township, among full-time, year-round workers aged 15 years and older, males earned a median income of $83,750, while females earned $63,601, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 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 Spring Lake township.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

HCAHPS Hospital Ratings Survey

Patient Experience Ratings 2018-2020

By Health [source]

About this dataset

This dataset contains ratings of hospitals, based on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). This survey collects data from hospital patients on their experiences during an inpatient stay. The list includes several indicators to help gauge a hospital's quality, such as star ratings based on patient opinions and percentage of positive answers to HCAHPS questions. Additionally, there are measures such as the number of completed surveys, survey response rate percent and linear mean value which assist in evaluating patient experience at each medical institution. With this comprehensive dataset you can easily draw comparisons between hospitals and make informed decisions about healthcare services provided in your area

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides useful information on the quality of care that hospitals provide. This dataset provides ratings and reviews of several hospitals, making it easy to compare hospitals in order to find out which hospital may best meet your needs.

The following guide will walk you through how to use this dataset effectively:

• Navigate the different columns available in this dataset by scrolling through the table. These include Hospital Name, Address, City, State, ZIP Code, County Name, Phone Number and HCAHPS Question among others.
• Examine important information such as the patient survey star rating and HCAHPS linear mean value for each hospital included in the dataset in order to evaluate it's performance against other hospitals based on standards set out by HCAHPS .
• Read any footnotes associated with each column carefully in order to fully understand what exactly is being measured. These may directly affect your evaluation of a particular hospital’s performance compared to others included in this dataset or even more so when compared against external sources of data outside this dataset such as other surveys or studies related to health care quality measurement metrics within that state or region where applicable & relevant (i..e Measure Start Date and Measure End Date).

• Pay careful attention also when evaluating factors related to survey response rates (e..g Survey Response Rate Percent Footnote) & what percentages are being reported here within each category; these figures may selectively bias results so ensure full transparency is achieved by reviewing all potential influencing factors/variables prior commencing investigations/data analysis/interpretation based upon this data-set alone(or any subset thereof).

  By following these steps you should be able set up your own criteria for measuring various aspects of health care quality across different states & cities - ensuring optimal access & safety measures for both patients & healthcare providers alike over time - thus ultimately aiding decision making processes towards improved patient outcomes worldwide!

Research Ideas

• Tracking patient experience trends over time: This dataset can be used to analyze trends in patient experience over time by identifying changes in survey responses, star ratings, and response rates across hospitals.
• Establishing a benchmark for high-quality hospital care: By studying the scores of the top-performing hospitals within each category, healthcare administrators can set standards and benchmarks for quality of care in their own hospitals.
• Comparing hospital ratings to inform decision making: Patients and family members looking to book an appointment at a hospital or doctors office can use this dataset to compare different facilities’ HCAHPS scores and make an informed decision about where they would like to go for their medical treatment

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - **Keep int...

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 Ouachita 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 Ouachita County, the median income for all workers aged 15 years and older, regardless of work hours, was $35,282 for males and $23,359 for females.

These income figures highlight a substantial gender-based income gap in Ouachita County. 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 county of Ouachita County.

- Full-time workers, aged 15 years and older: In Ouachita County, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,220, while females earned $41,169, 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 Ouachita County.

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 Ouachita 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 Butler 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 Butler County, the median income for all workers aged 15 years and older, regardless of work hours, was $49,795 for males and $31,226 for females.

These income figures highlight a substantial gender-based income gap in Butler County. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the county of Butler County.

- Full-time workers, aged 15 years and older: In Butler County, among full-time, year-round workers aged 15 years and older, males earned a median income of $69,156, while females earned $54,502, leading to a 21% gender pay gap among full-time workers. This illustrates that women earn 79 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 Butler County.

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

Overview and functional guide for the new Demand & Supply Planning (DSP) Dashboard, a strategic tool for managing our end-to-end integrated business planning (IBP) cycle. This document outlines the capabilities of our new Demand & Supply Planning Dashboard. This case for the industrial chemical sector, effective planning is critical for balancing service levels, managing working capital, and ensuring production efficiency. This dashboard was developed to transform our monthly S&OP/IBP cycle from a series of disconnected spreadsheets into a dynamic, integrated analytical platform. It is designed to provide clear visibility into forecast accuracy, inventory health, supply risks, and production alignment, enabling us to make more agile and profitable planning decisions.

https://youtu.be/emgQ5tYvrf4?si=TRJnX5EEnufmB7NF">

**1. Data Set Overview ** The dashboard is powered by a monthly, SKU-level dataset that represents a snapshot from our Integrated Business Planning (IBP) cycle. Each data point is essential for a holistic view of our supply chain.

Core Identifiers & Planning Cycle Record_ID, SKU_ID, Month: Unique identifiers for each monthly planning record. SKU_Description, Product_Group, UoM: Details of the product, its category (e.g., Surfactants, Solvents), and its unit of measure.

Demand Planning & Forecasting Forecast_Method: The methodology used for the forecast (e.g., ARIMA, Exponential Smoothing, Collaborative Judgment). Forecast_units: The final consensus forecast in units for the month. Actual_Demand_units: The actual sales or consumption for the month. Forecast_Error_units, Absolute_Error_units, APE: Metrics quantifying the deviation between the forecast and actual demand. MAE_3m, MAPE_3m, Bias_3m: Rolling 3-month averages for Mean Absolute Error, Mean Absolute Percentage Error, and forecast bias.

Supply & Inventory Planning Supplier_LeadTime_days, Transit_days, Total_LeadTime_days: The time required for procurement and delivery of raw materials or finished goods. Safety_Stock_units: The calculated buffer stock required to meet a target service level (e.g., 95%). Reorder_Point_units: The inventory level that triggers a replenishment order. OnHand_Inventory_units, Planned_Receipts_units: The starting inventory and scheduled inbound supply for the month. Replenishment_Order_units: The suggested order quantity based on planning parameters. Production_Plan_units: The planned production volume for in-house manufactured items.

Financial & Health Metrics Unit_Cost_USD, Inventory_Value_USD: The cost per unit and the total financial value of on-hand inventory. Days_of_Inventory: A measure of how long the current on-hand inventory will last based on average demand. Fill_Rate_pct, Stockout_Flag: Metrics indicating our ability to meet customer demand from available stock. SLOB_Flag (Slow-Moving and Obsolete): A flag for inventory that is at high risk of becoming obsolete. Shelf_Life_days, Expiry_Date, Days_to_Expiry: Critical data for managing products with a limited shelf life.

2. Key Metrics & Measurements The dashboard is structured into functional modules (tabs) that align with the core pillars of the Demand & Supply Planning process.

A. Forecasting Performance Weighted MAPE (Mean Absolute Percentage Error): Objective: To measure the overall accuracy of our demand forecast, weighted by volume to prioritize the most significant products. Goal: To achieve a W-MAPE of less than 15%. A lower MAPE indicates a more accurate and reliable demand plan, which is the foundation for all subsequent supply planning decisions. Forecast Bias: Objective: To identify systemic tendencies to either over-forecast (negative bias) or under-forecast (positive bias). Goal: To maintain a forecast bias close to zero. A neutral bias indicates that our forecasting errors are random, not systemic, which allows for more reliable safety stock calculations.

B. Service & Fulfillment Weighted Fill Rate: Objective: To measure our ability to fulfill actual customer demand from the available supply (on-hand + planned receipts) in a given month. Goal: To maintain a Weighted Fill Rate of over 98% for our 'A' class items. This KPI is a direct measure of customer service and our ability to prevent lost sales due to stockouts. Stockout Rate: Objective: To track the percentage of SKU-months where a stockout occurred (i.e., available supply was insufficient to cover actual demand). Goal: To minimize the stockout rate to less than 2%. This metric highlights critical failures in the supply plan that lead to direct revenue loss.

C. Inventory Management Days of Inventory (Value-Weighted): Objective: To measure the average number of days of inventory we hold, weighted by value to accurately reflect our working capital investment. Goal: To optimize our inventory levels, balanci...

Asthma ED Visit Rates by ZIP

Counts and Rates by Age Group in California

By Health [source]

About this dataset

This dataset presents a comprehensive look into the prevalence of asthma among Californian residents in terms of emergency department visits. Using age-adjusted rates and county FIPS codes, it offers an accurate snapshot of the prevalence rates per 10,000 people and provides key insights into how this condition affects certain age groups by ZIP Code. With its easy to use associated map view, this dataset allows users to quickly gain deeper knowledge about this important health issue and craft meaningful solutions to address it

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For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset contains counts and rates of asthma related emergency department visits by ZIP Code and age group in California. This data can be useful when doing research on asthma related trends or attempting to find correlations between environmental factors, prevalence of disease and geography.

• Select a year for analysis - the latest year for which data is available is the default selection, but other years are also listed in the dropdown menu.
• Select an Age Group to analyze - use the provided dropdown menus to select one or more age groups (all ages, 0-17, 18+) if you wish to analyze two different age groups in your analysis.
• Define a geographical area by selecting a ZIP code or County Fips code from which you wish to obtain your dataset from based on its availability or importance in your research question .
• View and download relevant data - after selecting all of the desired criteria (year,Age group(s), ZIP code/County FIPS Code) click “View Data” then “Download” at the bottom right corner of window that opens up
  5 Analyze information found - use software such as Microsoft Excel or open source programs like Openoffice Calc to gain insight into your downloaded dataset through statistics calculations, graphs etc.. In particular look out for anomalies that could signify further investigation

Research Ideas

• Identifying the geographic clusters of asthma sufferers by analyzing the rate of emergency department visits with geographic mapping.
• Developing outreach initiatives to areas with a high rate of ED visits for asthma to provide education, interventions and resources designed towards increasing preventive care and reducing preventable complications due to lack of access or knowledge about available services in these communities.
• Assessing disparities in ED visit rates for asthma between age groups as well as between urban and rural areas or different socio-economic groups within counties or ZIP codes in order to identify areas where there is a need for increased interventions, services and other resources related to asthma care in order to reduce the burden or severity of this chronic condition among particularly vulnerable population groups

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Asthma_Emergency_Department_Visit_Rates_by_ZIP_Code.csv | Column name | Description | |:----------------------|:------------------------------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | ZIP code | The ZIP code of the area the data was collected from. (String...

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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Featured Notebooks

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...

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 Cedar 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 Cedar County, the median income for all workers aged 15 years and older, regardless of work hours, was $53,184 for males and $34,053 for females.

These income figures highlight a substantial gender-based income gap in Cedar County. 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 county of Cedar County.

- Full-time workers, aged 15 years and older: In Cedar County, among full-time, year-round workers aged 15 years and older, males earned a median income of $66,345, while females earned $47,284, 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 Cedar County.

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

Unlocking insights into consumer behavior and retail dynamics, this comprehensive dataset captures the essence of transactions within a retail environment. Featuring ten essential columns, including CustomerID, ProductID, Quantity, Price, TransactionDate, PaymentMethod, StoreLocation, ProductCategory, DiscountApplied(%), and TotalAmount, this dataset encapsulates crucial information for retail analytics. Each entry provides a glimpse into the intricate interactions between customers, products, and sales channels, facilitating the exploration of purchasing patterns, popular products, pricing strategies, and regional preferences.

Please Upvote my Dataset, Let's Support each other.

By delving into the wealth of information contained within this dataset, analysts can uncover valuable insights to drive strategic decision-making. The TransactionDate column offers a temporal dimension, allowing for the identification of seasonal trends, peak purchasing periods, and the impact of marketing campaigns over time. PaymentMethod data sheds light on evolving consumer payment preferences and the effectiveness of different payment strategies. Moreover, the DiscountApplied(%) column provides insights into consumer responsiveness to promotions and discounts, enabling retailers to optimize their pricing strategies for maximum impact. With such rich and diverse data at their disposal, businesses can refine their marketing efforts, enhance customer experiences, and ultimately, thrive in today's competitive retail landscape.

Columns:

1. CustomerID: Unique identifier for each customer.
2. ProductID: Unique identifier for each product.
3. Quantity: The number of units purchased for a particular product.
4. Price: The unit price of the product.
5. TransactionDate: Date and time when the transaction occurred.
6. PaymentMethod: The method used by the customer to make the payment.
7. StoreLocation: The location where the transaction took place.
8. ProductCategory: Category to which the product belongs.
9. DiscountApplied(%): Percentage of the discount applied to the product.
10. TotalAmount: Total amount paid for the transaction.

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 Shattuck. 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 Shattuck, the median income for all workers aged 15 years and older, regardless of work hours, was $29,038 for males and $27,885 for females.

Based on these incomes, we observe a gender gap percentage of approximately 4%, indicating a significant disparity between the median incomes of males and females in Shattuck. Women, regardless of work hours, still earn 96 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Shattuck, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,141, while females earned $48,581, leading to a 18% gender pay gap among full-time workers. This illustrates that women earn 82 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 lower gender pay gap percentage. This indicates that Shattuck 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 Shattuck 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 Salt Lake 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 Salt Lake County, the median income for all workers aged 15 years and older, regardless of work hours, was $53,567 for males and $35,442 for females.

These income figures highlight a substantial gender-based income gap in Salt Lake County. 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 county of Salt Lake County.

- Full-time workers, aged 15 years and older: In Salt Lake County, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,776, while females earned $54,656, 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 Salt Lake County.

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 Salt Lake 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 Eureka 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 Eureka township, the median income for all workers aged 15 years and older, regardless of work hours, was $45,606 for males and $29,432 for females.

These income figures highlight a substantial gender-based income gap in Eureka township. 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 township of Eureka township.

- Full-time workers, aged 15 years and older: In Eureka township, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,853, while females earned $51,429, leading to a 18% gender pay gap among full-time workers. This illustrates that women earn 82 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 Eureka township.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Eureka 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 Butler 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 Butler County, the median income for all workers aged 15 years and older, regardless of work hours, was $48,194 for males and $29,002 for females.

These income figures highlight a substantial gender-based income gap in Butler County. Women, regardless of work hours, earn 60 cents for each dollar earned by men. This significant gender pay gap, approximately 40%, underscores concerning gender-based income inequality in the county of Butler County.

- Full-time workers, aged 15 years and older: In Butler County, among full-time, year-round workers aged 15 years and older, males earned a median income of $60,732, while females earned $45,431, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 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 Butler County.

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