The "Per Capita GDP of All Countries 1970 to 2022" dataset provides a comprehensive overview of the economic performance of all countries in the world from 1970 to 2022 in US Dollar. This dataset includes several key columns:

Country: This column lists the names of the countries included in the dataset. Year: This column indicates the specific year for which the GDP data is recorded from 1970 to 2022. By analyzing this dataset, you can observe trends in economic growth, identify periods of significant economic change, and compare the economic performance of different countries over time. This information is valuable for economists, researchers, policymakers, and anyone interested in understanding the economic history and development of nations.

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

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

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Country Club Hills, IL, 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

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 17,156, while the mean income for the highest quintile (20% of households with the highest income) is 251,340. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 457,555, which is 182.05% higher compared to the highest quintile, and 2667.03% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Country Club Hills median household income. You can refer the same here

China Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.600 % in 2021. This records a decrease from the previous number of 11.900 % for 2020. China Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 15.100 % from Dec 1990 (Median) to 2021, with 19 observations. The data reached an all-time high of 19.500 % in 2010 and a record low of 8.900 % in 1990. China Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;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).

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

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

Monthly and long-term Indonesia GDP Per Capita data: historical series and analyst forecasts curated by FocusEconomics.

This intermediate level data set was extracted from the census bureau database. There are 48842 instances of data set, mix of continuous and discrete (train=32561, test=16281).

The data set has 15 attribute which include age, sex, education level and other relevant details of a person. The data set will help to improve your skills in Exploratory Data Analysis, Data Wrangling, Data Visualization and Classification Models.

Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following description gives more details on this data set:

• age: the age of an individual.
• workclass: The type of work or employment of an individual. It can have the following categories:
  • Private: Working in the private sector.
  • Self-emp-not-inc: Self-employed individuals who are not incorporated.
  • Self-emp-inc: Self-employed individuals who are incorporated.
  • Federal-gov: Working for the federal government.
  • Local-gov: Working for the local government.
  • State-gov: Working for the state government.
  • Without-pay: Not working and without pay.
  • Never-worked: Never worked before.
• Final Weight: The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.

These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex.

We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.

People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.

• education: The highest level of education completed.
• education-num: The number of years of education completed.
• marital-status: The marital status.
• occupation: Type of work performed by an individual.
• relationship: The relationship status.
• race: The race of an individual.
• sex: The gender of an individual.
• capital-gain: The amount of capital gain (financial profit).
• capital-loss: The amount of capital loss an individual has incurred.
• hours-per-week: The number of hours works per week.
• native-country: The country of origin or the native country.
• income: The income level of an individual and serves as the target variable. It indicates whether the income is greater than $50,000 or less than or equal to $50,000, denoted as (>50K, <=50K).

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

The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

Small changes in estimates between years should be treated with caution as they may not be statistically significant.

Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 8.100 % in 2021. This records an increase from the previous number of 7.900 % for 2020. Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 9.100 % from Dec 1985 (Median) to 2021, with 25 observations. The data reached an all-time high of 10.500 % in 2015 and a record low of 6.000 % in 1985. Belgium BE: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belgium – Table BE.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;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).

Abstract

Dataset used in World Bank Policy Research Working Paper #2876, published in World Bank Economic Review, No. 1, 2005, pp. 21-44.

The effects of globalization on income distribution in rich and poor countries are a matter of controversy. While international trade theory in its most abstract formulation implies that increased trade and foreign investment should make income distribution more equal in poor countries and less equal in rich countries, finding these effects has proved elusive. The author presents another attempt to discern the effects of globalization by using data from household budget surveys and looking at the impact of openness and foreign direct investment on relative income shares of low and high deciles. The author finds some evidence that at very low average income levels, it is the rich who benefit from openness. As income levels rise to those of countries such as Chile, Colombia, or Czech Republic, for example, the situation changes, and it is the relative income of the poor and the middle class that rises compared with the rich. It seems that openness makes income distribution worse before making it better-or differently in that the effect of openness on a country's income distribution depends on the country's initial income level.

Kind of data

Aggregate data [agg]

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Town And Country, MO, 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

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 53,176, while the mean income for the highest quintile (20% of households with the highest income) is 848,049. This indicates that the top earners earn 16 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 1,320,990, which is 155.77% higher compared to the highest quintile, and 2484.18% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Town And Country median household income. You can refer the same here

Sweden SE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.100 % in 2021. This records an increase from the previous number of 10.100 % for 2020. Sweden SE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 8.900 % from Dec 1975 (Median) to 2021, with 27 observations. The data reached an all-time high of 11.100 % in 2021 and a record low of 5.200 % in 1987. Sweden SE: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;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).

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

Monthly and long-term Brunei GDP Per Capita data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Denmark GDP Per Capita data: historical series and analyst forecasts curated by FocusEconomics.

Global Income Inequality Dataset (2000–2023)

Overview

This dataset provides a comprehensive look at global income inequality from the year 2000 to 2023. It includes key indicators such as Gini index, average income, income distribution across different population percentiles, and income group classifications for 30 countries worldwide. The dataset offers insights into how income is distributed within nations and highlights disparities across different economic groups.

Data Features

• Country: The name of the country.
• Year: The year of the data point (2000–2023).
• Population: The estimated population for the given year.
• Gini Index: A measure of income inequality, where 0 represents perfect equality and 1 represents maximum inequality.
• Average Income (USD): The average income in USD for the country in the given year.
• Top 10% Income Share (%): The percentage of total income held by the top 10% of the population.
• Bottom 10% Income Share (%): The percentage of total income held by the bottom 10% of the population.
• Income Group: Categorization of the country’s income group (Low Income, Lower Middle Income, Upper Middle Income, High Income).

Potential Uses

• Economic Analysis: Understand global income inequality trends and how they vary by country and region.
• Predictive Modeling: Use the dataset to build machine learning models predicting future income inequality based on historical data.
• Policy Research: Study the impact of income distribution on policy decisions and economic growth in different nations.
• Visualization: Create heatmaps, time series charts, and more to visualize the income inequality across various countries and years.

Source

The data has been generated to simulate realistic income inequality patterns based on publicly available data on global economic trends.

Context

This dataset was uploaded as supplemental data for the 2019 Kaggle ML & DS Survey. It allows classification of countries into income groups - low, lower-middle, upper-middle and high - by gross national income (GNI) per capita, in U.S. dollars,.

For details of this calculation see here and here.

Content

The csv file consists of 218 countries listed by name and country code and their corresponding income group and lending category.

Acknowledgements

Thanks to the World Bank for providing the data at "https://datahelpdesk.worldbank.org/knowledgebase/articles/906519">https://datahelpdesk.worldbank.org/knowledgebase/articles/906519

Inspiration

This dataset allows any other data containing country names or codes to be supplemented with income group data.