Graph and download economic data for Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1246) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

In the first quarter of 2025, 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 was the country with the most billionaires in the world in 2025. Elon Musk, with a net worth of 342 billion U.S. dollars, was among the richest people in the United States in 2025. 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.

This dataset contains detailed information on the Fortune Global 500 companies for the year 2022. It encompasses essential metrics such as rank, company name, revenue, percent change in revenue, profits, assets, profit percent change, and number of employees.

This rich dataset provides a foundation for exploring corporate resilience and innovation in a post-pandemic world. Analysts can utilize this information to uncover trends in corporate performance, evaluate strategic pivots, and identify leading sectors in a recovering economy.

The dataset is a valuable tool for data analysis, visualization efforts, and machine learning applications focused on corporate financial performance and market dynamics.

World Religions Across Regions

Analyzing Adherence Across Regions, States and the Global System

By Correlates of War Project [source]

About this dataset

The World Religion Project (WRP) is an ambitious endeavor to conduct a comprehensive analysis of religious adherence throughout the world from 1945 to 2010. This cutting-edge project offers unparalleled insight into the religious behavior of people in different countries, regions, and continents during this time period. Its datasets provide important information about the numbers and percentages of adherents across a multitude of different religions, religion families, and non-religious affiliations.

The WRP consists of three distinct datasets: the national religion dataset, regional religion dataset, and global religion dataset. Each is focused on understanding individually specific realms for varied analysis approaches - from individual states to global systems. The national dataset provides data on number of adherents by state as well as percentage population practicing a given faith group in five-year increments; focusing attention to how this number evolves from nation to nation over time. Similarly, regional data is provided at five year intervals highlighting individual region designations with one modification – Pacific Ocean states have been reclassified into their own Oceania category according to Country Code Number 900 or above). Finally at a global level – all states are aggregated in order that we may understand a snapshot view at any five-year interval between 1945‐2010 regarding relationships between religions or religio‐families within one location or transnationally.

This project was developed in three stages: firstly forming a religions tree (a systematic classification), secondly collecting data such as this provided by WRP according to that classification structure – lastly cleaning the data so discrepancies may be reconciled and imported where needed with gaps selected when unknown values were encountered during collection process . We would encourage anyone wishing details undergoing more detailed reading/analysis relating various use applications for these rich datasets - please contact Zeev Maoz (University California Davis) & Errol A Henderson _(Pennsylvania State University)

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How to use the dataset

The World Religions Project (WRP) dataset offers a comprehensive look at religious adherence around the world within a single dataset. With this dataset, you can track global religious trends over a period of 65 years and explore how they’ve changed during that time. By exploring the WRP data set, you’ll gain insight into cross-regional and cross-time patterns in religious affiliation around the world.

Research Ideas

• Analyzing historical patterns of religious growth and decline across different regions
• Creating visualizations to compare religious adherence in various states, countries, or globally
• Studying the impact of governmental policies on religious participation over time

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 intact - all notices that refer to this license, including copyright notices.

Columns

File: WRP regional data.csv | Column name | Description | |:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | Year | Reference year for data collection. (Integer) | | Region | World region according to Correlates Of War (COW) Regional Systemizations with one modification (Oceania category for COW country code ...

BR: Income Share Held by Second 20% data was reported at 7.700 % in 2022. This records an increase from the previous number of 7.500 % for 2021. BR: Income Share Held by Second 20% data is updated yearly, averaging 6.500 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 8.700 % in 2020 and a record low of 5.000 % in 1989. BR: Income Share Held by Second 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Norway NO: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 3.851 % in 2010. This records a decrease from the previous number of 3.862 % for 2000. Norway NO: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 3.862 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 3.883 % in 1990 and a record low of 3.851 % in 2010. Norway NO: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Land Use, Protected Areas and National Wealth. Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

United States US: Account: Income: Richest 60%: % Aged 15+ data was reported at 97.904 % in 2014. This records an increase from the previous number of 92.810 % for 2011. United States US: Account: Income: Richest 60%: % Aged 15+ data is updated yearly, averaging 95.357 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 97.904 % in 2014 and a record low of 92.810 % in 2011. United States US: Account: Income: Richest 60%: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, richest 60%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

Graph and download economic data for Net Worth Held by the Bottom 50% (1st to 50th Wealth Percentiles) (WFRBLB50107) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

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.

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

Sri Lanka LK: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 1.533 % in 2010. This records an increase from the previous number of 1.529 % for 2000. Sri Lanka LK: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 1.533 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2.222 % in 1990 and a record low of 1.529 % in 2000. Sri Lanka LK: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank: Land Use, Protected Areas and National Wealth. Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

The collection of approximately 200,000 cataloged voucher specimens includes about 160,000 skins, 22,000 complete skeletons, and 8,000 fluid-preserved specimens, as well as 2,000 spread wings, 20,000 stomach-content samples and thousands of tape-recordings of bird vocalizations. Holdings from Peru, Bolivia, the West Indies, and the Southeastern United States are the largest in the world. The collection is among the 5-10 largest in the world from Mexico, Guatemala, Belize, Honduras, Costa Rica, Panama, and Argentina. There are also substantial holdings from the western USA, Brazil, Africa, Kuwait, and Borneo. The vast majority of specimens were collected since 1950 and, therefore, relatively data-rich; a high percentage of specimens collected since 1980 have associated tissue samples.

For information about how to obtain a loan see: https://www.lsu.edu/mns/collections/ornithology.php

Graph and download economic data for Minimum Wealth Cutoff for the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1311) from Q3 1989 to Q3 2022 about wealth, percentile, and USA.

Costa Rica CR: Account: Income: Richest 60%: % Aged 15+ data was reported at 66.721 % in 2014. This records an increase from the previous number of 60.007 % for 2011. Costa Rica CR: Account: Income: Richest 60%: % Aged 15+ data is updated yearly, averaging 63.364 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 66.721 % in 2014 and a record low of 60.007 % in 2011. Costa Rica CR: Account: Income: Richest 60%: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, richest 60%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

This dataset is a comprehensive synthetic representation of data collected from a real-world urban Wireless Sensor Network (WSN) deployed for environmental monitoring, communication analysis, and anomaly detection. The dataset simulates the activity of 500 sensor nodes distributed across an urban environment, capturing over 1 million data points. Each sensor node records multiple environmental variables, communication metrics, and operational data, providing a rich source for research and analysis in various fields such as IoT-based smart cities, sensor network optimization, anomaly detection, and urban environmental studies. The dataset is available in Parquet and CSV formats, with each file containing '1,000,000 rows' and '12 columns'. The Parquet format is particularly suited for large-scale data processing, as it allows efficient data compression and columnar storage, while the CSV format ensures compatibility with a wide range of tools and platforms for analysis. Dataset Features: 1. sensor_id (Integer): - Unique identifier for each of the 500 sensor nodes in the network. - Range: 1 to 500. - Purpose: Distinguishes between different sensor nodes, allowing analysis of node-specific behavior. 2. timestamp (Datetime): - The exact time at which each sensor reading was recorded. - Range: Randomly generated timestamps spanning one year. - Purpose: Enables time-series analysis, trend discovery, and temporal anomaly detection. Useful for studying patterns over time, such as seasonal environmental changes or sensor failures. 3. temperature (Float): - The temperature reading recorded by each sensor node (in Celsius). - Range: 10°C to 40°C. - Purpose: Captures temperature variations in the urban area, which could be used for climate studies, urban heat mapping, or environmental modeling. 4. humidity (Float): - The relative humidity recorded by the sensor node (in percentage). - Range: 20% to 90%. - Purpose: Useful for studying atmospheric conditions, correlating humidity with other environmental variables, or examining anomalies related to sensor faults or weather conditions. 5. ambient_light (Float): - The level of ambient light measured by the sensor (in Lux). - Range: 100 to 1000 Lux. - Purpose: Useful for urban lighting studies, detecting lighting failures in smart cities, or assessing sunlight exposure patterns in specific locations. 6. sensor_reading (Float): - The general sensor data reading (arbitrary units). - Range: 0 to 100. - Purpose: Represents operational sensor output. It could be an aggregation of different parameters, or abstract sensor readings used for system health analysis or anomaly detection. 7. signal_strength (Float): - The strength of the signal transmitted by the sensor node, measured in decibel-milliwatts (dBm). - Range: -100 dBm to -30 dBm. - Purpose: Reflects communication performance and network reliability. Can be used to study signal attenuation in urban environments or evaluate network performance under different conditions. 8. battery_level (Float): - The remaining battery level of the sensor node (in percentage). - Range: 10% to 100%. - Purpose: Monitors sensor node power levels. It can be used to optimize sensor node maintenance, analyze power consumption patterns, or develop energy-efficient algorithms for IoT networks. 9. latitude (Float): - The geographical latitude of the sensor node. - Range: 40.7128 to 40.7484 (simulating a section of New York City). - Purpose: Useful for geospatial analysis of sensor data, identifying patterns based on location, and integrating with mapping tools for visualization. 10. longitude (Float): - The geographical longitude of the sensor node. - Range: -74.0060 to -73.9352. - Purpose: Paired with latitude, it allows spatial analysis of the network's behavior and performance. It can also be used for geolocation-based anomaly detection or optimization. 11. packet_loss_rate (Float): - The percentage of data packets lost during communication between the sensor and the central network. - Range: 0% to 5%. - Purpose: Assesses the reliability of sensor communication. Can be used to detect network issues, optimize routing protocols, or improve network robustness. 12. anomalous_event (Binary): - A binary flag indicating whether an anomalous event occurred (0 = Normal, 1 = Anomaly). - Range: 0 or 1 (5% of the data is labeled as anomalies). - Purpose: Enables research on anomaly detection, failure prediction, and system reliability improvement. The anomalies could represent sensor malfunctions, environmental events, or communication failures. Potential Applications of the Dataset: 1. Anomaly Detection: - The dataset provides rich information on anomalous events, which can be used for developing, testing, and benchmarking anomaly detection algorithms. Researchers can use the data to identify patterns that signal failures in sensor nodes, such as rapid battery...

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How are incomes and wealth distributed between people? Both within countries and across the world as a whole?

On this page, you can find all our data, visualizations, and writing relating to economic inequality.

This evidence demonstrates that inequality in many countries is substantial and, in numerous instances, has been escalating. Global economic inequality is extensive and exacerbated by intersecting disparities in health, education, and various other dimensions.

However, economic inequality is not uniformly increasing. In many countries, it has declined or remained steady. Furthermore, global inequality – following two centuries of ascent – is presently decreasing as well.

The significant variations observed across countries and over time are pivotal. They indicate that high and rising inequality is not inevitable and that the current extent of inequality is subject to change.

About this data This data explorer offers various inequality indicators measured according to two distinct definitions of income sourced from different outlets.

Data from the World Inequality Database pertains to inequality prior to taxes and benefits. Data from the World Bank pertains to either income post taxes and benefits or consumption, contingent on the country and year. For additional details regarding the definitions and methodologies underlying this data, refer to the accompanying article below, where you can also delve into and juxtapose a broader spectrum of indicators from various sources.

Australia Account: Income: Richest 60%: % Aged 15+ data was reported at 99.159 % in 2014. This records a decrease from the previous number of 99.729 % for 2011. Australia Account: Income: Richest 60%: % Aged 15+ data is updated yearly, averaging 99.444 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 99.729 % in 2011 and a record low of 99.159 % in 2014. Australia Account: Income: Richest 60%: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, richest 60%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

Germany DE: Account: Income: Richest 60%: % Aged 15+ data was reported at 99.878 % in 2014. This records an increase from the previous number of 95.367 % for 2011. Germany DE: Account: Income: Richest 60%: % Aged 15+ data is updated yearly, averaging 97.622 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 99.878 % in 2014 and a record low of 95.367 % in 2011. Germany DE: Account: Income: Richest 60%: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, richest 60%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about *** percent were worth more than *********** dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about *** billion people, it was the second most populous country in the world. Of that *** billion, about **** million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds ** percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.

Throughout the interwar period, Nazi leaders and propaganda repeatedly put forward the bogus claim that Jews owned up to 20 percent of all wealth in Germany, despite making up fewer than one percent of the population. At this time, Jews were used as a scapegoat for Germany's economic difficulties after the First World War and during the Great Depression, and the Nazis claimed that the Jews were lining their pockets at the expense of "Aryan" Germans. Unfortunately, there are no official figures for Jewish wealth in the 1930s, and emigration tax data only gives an insight into the finances of wealthier Jews. There are, however, a range of estimates from contemporary and more recent sources, which have been used to estimate the real share of German capital that was owned by Jews. Contemporary estimates At various points in the 1930s, the media, statistical office, and central bank all claimed that the combined wealth of German Jews was somewhere between two and 20 billion Reichsmarks (RM). While these three institutions were all state run under the Nazi regime, and despite their uncertainty, some of these estimates are still treated with consideration due to the credentials of the journalists, economists, and statisticians involved. Additionally, these figures were used with the purpose of identifying just how much money the state could take from the Jewish population, therefore it was of interest for the Nazi authorities to ascertain accurate figures, and not inflate estimates for propaganda purposes. Interestingly, the estimates from the Statistical office actually increased from 1933 to 1936, despite the fact that the state had already been seizing Jewish wealth and restricting Jewish business on a large scale since 1933; this has been attributed to the economic impact of the Great Depression. Modern estimates The estimates from Junz and Ritschl were published in 2002 and 2019 respectively, and used some of the contemporary estimates in their investigation, while taking many additional factors into account. These are now some of the most widely-cited estimates on this subject, with estimates of around 8-16 billion RM in 1933, five billion RM in 1936, and 4.4 billion RM in 1938. In Ritschl's 2019 paper, he then goes on to estimate the share of total German wealth owned by Jews; his results show that the Jewish share of private capital was slightly higher than the average, but was still very much in line with their population size.