Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

In the first quarter of its 2025 fiscal year, Apple generated around ** billion U.S. dollars in revenue from the sales of iPhones. Apple iPhone revenue The Apple iPhone is one of the biggest success stories in the smartphone industry. Since its introduction to the market in 2007, Apple has sold more than *** billion units worldwide. As of the third quarter of 2024, the Apple iPhone’s market share of new smartphone sales was over ** percent. Much of its accomplishments can be attributed to Apple’s ability to keep the product competitive throughout the years, with new releases and updates. Apple iPhone growth The iPhone has shown to be a crucial product for Apple, considering that the iPhone’s share of the company’s total revenue has consistently grown over the years. In the first quarter of 2009, the iPhone sales were responsible for about ********* of Apple’s revenue. In the third quarter of FY 2024, this figure reached a high of roughly ** percent, equating to less than ** billion U.S. dollars in that quarter. In terms of units sold, Apple went from around **** million units in 2010 to about *** million in 2023, but registered a peak in the fourth quarter of 2020 with more than ** million iPhones sold worldwide.

Apple’s iPhone sales accounted for around ** percent of the company’s overall revenue in the first quarter of fiscal year 2025, the largest share of all Apple products. Over the years, services as well as wearables, home and accessories have made a growing contribution to Apple’s net sales. Apple’s revenue growth amid the pandemic In the first quarter of financial year 2025, Apple’s global revenue reached around *** billion U.S. dollars. The Americas are Apple’s largest regional market and contributed to around ** percent of the firm’s sales in that quarter. Who are Apple’s competitors? Having a broad family of products, Apple competes with different companies in different markets. Samsung is Apple’s largest adversaries in the global smartphone market, where the company had a share of almost ** percent in the second quarter of 2024. Similarly, Apple has a solid position in the PC market without a leading advantage. The situation is reversed in the tablet market and the smartwatch market, where Apple has remained the leader since the early days, staying ahead of Samsung, Huawei, Amazon, etc.

International Apple Pricing Strategy

Understanding Apple Product Prices in Relation to Local Salaries Worldwide

By [source]

About this dataset

This dataset offers a unique and powerful insight into the international markets of Apple products. It shows how Apple prices its products in different countries, and how those prices compare with average monthly salaries in those countries, giving a view on the affordability of these products. By looking at this data one can also get a better idea of what local markets look like around the world, as well as which countries may be better for price conscious shopping. All this data allows for deeper understanding of product pricing differences and potential spending power across regions to inform decisions by product or market makers about where to focus their efforts

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an insight into Apple's international pricing strategies, which can be leveraged to draw conclusions about the company's approach to local markets around the world. To use this dataset, first look at how Apple prices its products in different countries by studying the columns 'price' and 'EUR Average Monthly Salary' and 'USD Average Monthly Salary'. Then examine how those prices compare with local salaries in those countries by comparing the columns 'EUR Average Monthly Salary' and 'USD Average Monthly Salary'. Finally, take a closer look at what types of products Apple offers in each location by studying columns such as 'sku', 'category', and ‘name’. By exploring these datasets you can gain insights into Apple's international pricing strategy while taking into account differences between local economies

Research Ideas

• Market segmentation: This dataset can provide valuable insights for companies looking to target different markets depending on the average local salary and purchasing power compared to Apple's current prices in that market.
• Price Optimization: Analyzing departments such as pricing, revenue management and strategic marketing could leverage this dataset develop smarter pricing strategies while also reflecting local income disparities as an integral factor​ in optimizing product prices across regions.
• Sales Planning & Budgeting: Companies can use this information to plan their annual budgets and forecast estimated sales performance across each of their markets according by benchmarking against Apple's current global prices for different products

Acknowledgements

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

License

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

Columns

File: average_monthly_salary_index.csv | Column name | Description | |:-------------------------------|:------------------------------------------------------------------| | Country | The country in which the data was collected. (String) | | EUR Average Monthly Salary | The average monthly salary in Euros for the country. (Float) | | USD Average Monthly Salary | The average monthly salary in US Dollars for the country. (Float) |

File: preus_mac_ipad_iphone.csv | Column name | Description | |:--------------|:----------------------------------------------------| | sku | Unique identifier for each product. (String) | | price | Price of the product in the local currency. (Float) | | category | Category of the product. (String) | | name | Name of the product. (String) | | country | Country where the product is sold. (String) | | store | Store where the product is sold. (String) |

Acknowledgements

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

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Apple Creek. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Apple Creek. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Apple Creek, householders within the 45 to 64 years age group have the highest median household income at $89,167, followed by those in the 25 to 44 years age group with an income of $74,107. Meanwhile householders within the under 25 years age group report the second lowest median household income of $63,438. Notably, householders within the 65 years and over age group, had the lowest median household income at $44,375.

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.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Apple Creek median household income by age. You can refer the same here

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Apple Valley. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Apple Valley. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Apple Valley, the median household income stands at $116,094 for householders within the 45 to 64 years age group, followed by $86,250 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $37,000.

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.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Apple Valley median household income by age. You can refer the same here

Data exported from Apple's Health app, covering aspects such as:

• Headphone audio levels
• Step counts
• Walking distance
• Basal energy / active energy expenditure
• Walking symmetry
• Flights climbed

Note that the datetime ranges of the exported files vary as several features have only been recorded as they were implemented over the years. Data was collected on 04/10/24 10:14:46PM.

Possible Areas to Explore

• Predictive Forecasting / Model Tuning: Explore how different models perform with varying features. Experiment with subsets of established features combined with created features, and use larger files for cross-validation and multiple trial analysis.
• EDA: What do yearly averages and medians look like? What do extremes for each value look like? What insights do we uncover when we begin to aggregate and transform the data?
• Data Transformation: Use data manipulation to perform joins, aggregations, and filtering on the datasets. Examine how different tools and methods perform regarding efficiency and practicality, especially with larger sets.
• Trend Analysis: What trends and behaviors are evident from our analysis? Which behaviors have gradually changed over time, and what trends changed abruptly?
• Data Visualization: What can our data tell us at a glance? When we drill down on more granular data, what do we learn in the process?

Note: Apple did not provide column descriptions, so the descriptions provided in their place may be inaccurate. Since this data was only passively recorded, a large number of the Apple Watch and self-inputted data columns are missing. More info about the columns may be found 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 Apple River town. 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 Apple River town, the median income for all workers aged 15 years and older, regardless of work hours, was $40,288 for males and $33,162 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 18% between the median incomes of males and females in Apple River town. With women, regardless of work hours, earning 82 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of Apple River town.

- Full-time workers, aged 15 years and older: In Apple River town, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,743, while females earned $42,857, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 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 Apple River town, 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 Apple River town median household income by race. You can refer the same here

Update: (2025-04-17) Also added to this dataset are the presentation slides and script used at the Web Archiving Conference 2025 (WAC2025) on 2025-04-10.

Collected in this dataset are the abstract and related materials prepared for a submission to The 2025 General Assembly (GA) and Web Archiving Conference (WAC) . The abstract has been accepted for a 15-minute presentation with a 5-minute Q&A at the conference which is to be held at the National Library of Norway in Oslo from 8-10 April 2025.

The full abstract (in PDF) and the figures (in PNG) are collected into this dataset. The text from the abstract is also copied below.

Recently Orphaned Newspapers: From Archived Webpages to Reusable Datasets and Research Outlooks

2024-09-17

Tyng-Ruey Chuang
Chia-Hsun Wang
Hung-Yen Wu

Topics:

• IMPROVING DISCOVERY & ACCESS

Keywords:

• Orphan Works
• Online News
• IPTC (International Press Telecommunications Council)
• ninjs: News in JSON
• FAIR Data
• Traditional Chinese Language Resources

We report on our progress in converting the web archives of a recently orphaned newspaper into accessible article collections in IPTC (International Press Telecommunications Council) standard format for news representation. After the conversion, old articles extracted from a defunct news website are now reincarnated as research datasets meeting the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Specifically, we focus on Taiwan's Apple Daily and work on the WARC files built by the Archive Team in September 2022 at a time when the future of the newspaper seemed dim. We convert these WARC files into de-duplicated collections of pure text in ninjs (News in JSON) format.

The Apple Daily in Taiwan had been in publication since 2003 but discontinued its print edition in May 2021. In August 2022, its online edition was no longer being updated, and the entire news website has become inaccessible since March 2023. The fate of Taiwan's Apple Daily followed that of its (elder) sister publication in Hong Kong. The Apple Daily in Hong Kong was forced to cease its entire operation after midnight June 23, 2021. Its pro-democracy founder, Jimmy Lai (黎智英), was arrested under Hong Kong's security law the year before.

Being orphaned and offline, past reports and commentaries from the newspapers on contemporary events (e.g. the Sunflower Movement in Taiwan and the Umbrella Movement in Hong Kong) become unavailable to the general public. Such inaccessibility has impacts on education (e.g. fewer news sources to be edited into Wikipedia), research (e.g. fewer materials to study the early 2000s zeitgeist in Hong Kong and Taiwan), and knowledge production (e.g. fewer traditional Chinese corpora to work with).

Our work in transforming the WARC records into ninjs objects produces a collection of unique 953,175 news articles totaling in 4.3 GB. The articles are grouped by the day/month/year they were published hence it is convenient to look into a specific date for the news that were published on that day. Metadata about each article — headline(s), subject(s), original URI, unique ID, among others — are mapped into the corresponding fields in the ninjs object for ready access.

Figure 1 shows the ninjs object derived from a news article that was published on 2014-03-19, archived on 2021-09-29, and converted by us on 2024-02-17. Figure 2 is a screenshot of the webpage where the news was originally published, as kept in the WayBack Machine of the Internet Archive. Figure 3 displays the text file of the ninjs object in Figure 1 (noted that character strings are expressed in JSON's escaped Unicode format). Currently the images and videos accompanying the news article have not been extracted. This is evident in that the video (playable in the WayBack Machine) shown in Figure 2 is missing in the ninjs object in Figure 1. Another process is in the plan to preserve and link to these media files in the produced ninjs object.

In our presentation, we shall elaborate on the technical details (such as the accuracy and coverage of the conversion) and exemplary use cases of the collection. We will touch on the roles of public research organizations in preserving and making available materials that are deemed out of commerce and circulation.

Computer vision can predominantly be focused to design the strategies for the conservation of the plants. Previous decade’s trends and the current prevailing incidents with respect to global warming, forest fires, and famines act as potential indicators of how much nature is destroyed by human activities. Plants are vitally used in foodstuff, medicine, industry and as well for environmental protection. However, due to lack of resources and knowledge, it is difficult to recognize different plant species, plant diseases, etc. Nowadays modern equipment’s are being designed to address these issues. So considering the challenges, demands, we have constructed a database of different plants. The plants taken for study are the native plants of the Kashmir region of India. The climate of Kashmir remains chilling for a few months and pleasant for the rest of the year. Eight different plants namely Apple, Apricot, Cherry, Cranberry, Grapes, Peach, Pear, and Walnut are selected for the study based on their commercial and medicinal usage. The leaf is the primary object of reference taken for making the database, as they grow much earlier than fruits as well as the other plant parts. For each plant two types of leaves are selected, one healthy and the other diseased. Considering the natural conditions under which the farmers or the agriculturists have to work, the images are captured in broad daylight under the auto mode with the Nikon D-SLR digital camera with an ISO Speed = 100, Aperture = F/5.6, Flash = Not Fired, Shutter Speed = 1/640. All the images are captured by an 18-55 mm lens and are in .JPG format. The leaves are divided into two major classes A and B respectively. The two major classes were then divided into 16 sub classes i.e., eight healthy and eight diseased. The symbol “h” e.g., plant-name_h001 in the images represent healthy images and “d” i.e., plant-name_d001 represents the diseased images. The images are labeled, resized and classified into different classes. The class of healthy images comprises of a total of 1201 images and the diseased images constitute of a total of 935 images. Thus a total of 2136 images were selected from the captured images to sew up this database. Every little step towards a positive perspective marks the beginning of the era of growth with kindness.

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 Apple Valley. 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 Apple Valley, the median income for all workers aged 15 years and older, regardless of work hours, was $43,087 for males and $27,002 for females.

These income figures highlight a substantial gender-based income gap in Apple Valley. 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 town of Apple Valley.

- Full-time workers, aged 15 years and older: In Apple Valley, among full-time, year-round workers aged 15 years and older, males earned a median income of $67,181, while females earned $53,938, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

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

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 Apple Valley 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 Apple Creek. 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 Apple Creek, the median income for all workers aged 15 years and older, regardless of work hours, was $52,500 for males and $27,461 for females.

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

- Full-time workers, aged 15 years and older: In Apple Creek, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,250, while females earned $44,375, 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 Apple Creek.

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

About this dataset

Context

The dataset tabulates the Apple Valley household income by gender. The dataset can be utilized to understand the gender-based income distribution of Apple Valley income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Apple Valley, CA annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Apple Valley, CA annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2022)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Apple Valley income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Apple River town household income by gender. The dataset can be utilized to understand the gender-based income distribution of Apple River town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Apple River, Wisconsin annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Apple River, Wisconsin annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Apple River town income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Apple Valley household income by age. The dataset can be utilized to understand the age-based income distribution of Apple Valley income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Apple Valley, UT annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Apple Valley, UT household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Apple Valley income distribution by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Apple River town median household income by race. The dataset can be utilized to understand the racial distribution of Apple River town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Apple River, Wisconsin median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Apple River, Wisconsin (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Apple River town median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the Apple Valley median household income by race. The dataset can be utilized to understand the racial distribution of Apple Valley income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Apple Valley, CA median household income breakdown by race betwen 2012 and 2022
• Median Household Income by Racial Categories in Apple Valley, CA (2022)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Apple Valley median household income by race. You can refer the same here