With his extravagant challenges and elaborated stunts, *************************** is the most renown content creator online. The ******** YouTuber was the top-earning creator worldwide, with an estimated revenue of ** million U.S. dollars for the year. Stand-up comedian and internet personality ********* was estimated to have generated approximately ** million U.S. dollars, respectively. YouTuber and producer ********* followed, with revenues of ** million U.S. dollars in 2024.
In 2023, it was estimated that YouTube Mr. Beast (Jimmy Donaldson) ranked first as the top-earning YouTuber worldwide with earnings of approximately ** million U.S. dollars. Comedic duo Rhett & Link ranked second, with an estimate of ** million U.S. dollars earned during the last measured year. Fourth-ranked Ryan Kaji, who goes by Ryan on his YouTube channel, called Ryan's World, is 12 years old and earned ** million U.S. dollars in the past year, surpassing YouTuber Jake Paul. YouTube most popular channels and creators Created in 2005, YouTube has become one of the most popular social networks worldwide. In 2023, YouTube had approximately *** million users worldwide. As of February 2024, the most-subscribed YouTube channels did not only include popular video game commentators or comedy creators, but also children-specific channels, such as nursery rhymes channel Cocomelon, Kids Diana Show, and Like Nastya. Beauty and skincare YouTubers were also among the most beloved content creators for users on the social video platforms, with channels such as Sandra Cires Art and Jeffree Star recording over ** million subscribers as of March 2024. Content for children In 2023, U.S. children were estimated to have spent over ** minutes per day on average watching YouTube content. Despite being based on user-generated content such as make-up tutorials, toy reviews, or comedy sketches, YouTube channels for kids present professional video production values and full-time work commitment to the platform. As of March 2024, Cocomelon had over ***** billion views, while the Kids Diana Show had over *** billion views.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The average for 2021 based on 71 countries was 27.53 percent. The highest value was in Colombia: 43.7 percent and the lowest value was in Slovakia: 19.1 percent. The indicator is available from 1963 to 2023. Below is a chart for all countries where data are available.
This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.
The table only covers individuals who have some liability to Income Tax. The percentile points have been independently calculated on total income before tax and total income after tax.
These statistics are classified as accredited official statistics.
You can find more information about these statistics and collated tables for the latest and previous tax years on the Statistics about personal incomes page.
Supporting documentation on the methodology used to produce these statistics is available in the release for each tax year.
Note: comparisons over time may be affected by changes in methodology. Notably, there was a revision to the grossing factors in the 2018 to 2019 publication, which is discussed in the commentary and supporting documentation for that tax year. Further details, including a summary of significant methodological changes over time, data suitability and coverage, are included in the Background Quality Report.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in Sweden, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Sweden town median household income. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in Denmark, Maine, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Denmark town median household income. You can refer the same here
In March 2025, the top one percent of earners in the United Kingdom received an average pay of over 16,000 British pounds per month, compared with the bottom ten percent of earners who earned around 800 pounds a month.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in Peru, Massachusetts, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Peru town median household income. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in Glens Falls, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Glens Falls median household income. You can refer the same here
In the first quarter of 2024, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States follows closely behind China as the country with the most billionaires in the world. Elon Musk alone held around 219 billion U.S. dollars in 2022. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.
In 2022 the top one percent of earners in the United Kingdom accounted for around 10.2 percent of the overall national income of the UK. The share of national income earned by the top one percent increased from 6.8 percent in 1980 to a peak of 14.8 percent in 2007.
https://opensource.org/licenses/GPL-3.0https://opensource.org/licenses/GPL-3.0
This paper argues that high marginal labor income tax rates on top earners are an effective tool for social insurance even when households have high labor supply elasticity, make dynamic savings decisions, and policies have general equilibrium effects. We construct a large scale overlapping generations model with uninsurable labor productivity risk, show that it has a realistic wealth distribution and numerically characterize the optimal top marginal rate. We find that marginal tax rates for top 1% earners of 79% are optimal as long as the model earnings and wealth distributions display a degree of concentration as observed in US data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in Adams County, IA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Adams County median household income. You can refer the same here
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Taking five Anglo-Saxon countries that have relatively similar backgrounds and tax systems – Australia, Canada, New Zealand, the UK, and the US – we see that the shares of the very richest exhibit a strikingly similar pattern, falling in the three decades after World War II, before rising sharply from the mid-1970s onwards. The share of the top 1 percent is highly correlated across Anglo-Saxon countries, more so than with the share of the next 4 percent. Controlling for country and year fixed effects, we find that a reduction in the marginal tax rate on wage income is associated with an increase in the share of the top percentile group. Likewise, a fall in the marginal tax rate on investment income (based on a lagged moving average) is associated with a rise in the share of the top percentile group.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.
See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.
As of January 2025, the top earner in professional Dota 2 tournaments in Sweden was Ludwig Wåhlberg, going by the nickname Zai, who has earned a total of over 4.27 million U.S. dollars in his Dota 2 gaming career. Gustav Magnusson, with the gamer tag of s4, came in close second, earning over 2.74 million U.S. dollars in prize money. DOTA 2 (Defense of the Ancients 2) is a MOBA (Multiplayer Online Battle Arena) video game, in which a player controls a single character in a team, which competes against another team of players.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in White County, GA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for White County median household income. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in Corning, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Corning median household income. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the mean household income for each of the five quintiles in York, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Income Levels:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for York median household income. You can refer the same here
With his extravagant challenges and elaborated stunts, *************************** is the most renown content creator online. The ******** YouTuber was the top-earning creator worldwide, with an estimated revenue of ** million U.S. dollars for the year. Stand-up comedian and internet personality ********* was estimated to have generated approximately ** million U.S. dollars, respectively. YouTuber and producer ********* followed, with revenues of ** million U.S. dollars in 2024.