Analysis of ‘GapMinder - Income Inequality’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/psterk/income-inequality on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

This analysis focuses on income inequailty as measured by the Gini Index* and its association with economic metrics such as GDP per capita, investments as a % of GDP, and tax revenue as a % of GDP. One polical metric, EIU democracy index, is also included.

The data is for years 2006 - 2016

This investigation can be considered a starting point for complex questions such as:

1. Is a higher tax revenue as a % of GDP associated with less income inequality?
2. Is a higher EIU democracy index associated with less income inequality?
3. Is higher GDP per capita associated with less income inequality?
4. Is higher investments as a % of GDP associated with less income inequality?

This analysis uses the gapminder dataset from the Gapminder Foundation. The Gapminder Foundation is a non-profit venture registered in Stockholm, Sweden, that promotes sustainable global development and achievement of the United Nations Millennium Development Goals by increased use and understanding of statistics and other information about social, economic and environmental development at local, national and global levels.

*The Gini Index is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measurement of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper Variability and Mutability.

The dataset contains data from the following GapMinder datasets:

EIU Democracy Index:

"This democracy index is using the data from the Economist Inteligence Unit to express the quality of democracies as a number between 0 and 100. It's based on 60 different aspects of societies that are relevant to democracy universal suffrage for all adults, voter participation, perception of human rights protection and freedom to form organizations and parties. The democracy index is calculated from the 60 indicators, divided into five ""sub indexes"", which are:

1. Electoral pluralism index;
2. Government index;
3. Political participation indexm;
4. Political culture index;
5. Civil liberty index.

The sub-indexes are based on the sum of scores on roughly 12 indicators per sub-index, converted into a score between 0 and 100. (The Economist publishes the index with a scale from 0 to 10, but Gapminder has converted it to 0 to 100 to make it easier to communicate as a percentage.)" https://docs.google.com/spreadsheets/d/1d0noZrwAWxNBTDSfDgG06_aLGWUz4R6fgDhRaUZbDzE/edit#gid=935776888

Income: GDP per capita, constant PPP dollars

GDP per capita measures the value of everything produced in a country during a year, divided by the number of people. The unit is in international dollars, fixed 2011 prices. The data is adjusted for inflation and differences in the cost of living between countries, so-called PPP dollars. The end of the time series, between 1990 and 2016, uses the latest GDP per capita data from the World Bank, from their World Development Indicators. To go back in time before the World Bank series starts in 1990, we have used several sources, such as Angus Maddison. https://www.gapminder.org/data/documentation/gd001/

Investments (% of GDP)

Capital formation is a term used to describe the net capital accumulation during an accounting period for a particular country. The term refers to additions of capital goods, such as equipment, tools, transportation assets, and electricity. Countries need capital goods to replace the older ones that are used to produce goods and services. If a country cannot replace capital goods as they reach the end of their useful lives, production declines. Generally, the higher the capital formation of an economy, the faster an economy can grow its aggregate income.

Tax revenue (% of GDP)

refers to compulsory transfers to the central governement for public purposes. Does not include social security. https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS

Context

Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand. Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found here.

Acknowledgements

Thanks to gapminder.org for organizing the above datasets.

Inspiration

Below are some research questions associated with the data and some initial conclusions:

Research Question 1 - Is Income Inequality Getting Worse or Better in the Last 10 Years?

Answer:

Yes, it is getting better, improving from 38.7 to 37.3

On a continent basis, all were either declining or mostly flat, except for Africa.

Research Question 2 - What Top 10 Countries Have the Lowest and Highest Income Inequality?

Answer:

Lowest: Slovenia, Ukraine, Czech Republic, Norway, Slovak Republic, Denmark, Kazakhstan, Finland, Belarus,Kyrgyz Republic

Highest: Colombia, Lesotho, Honduras, Bolivia, Central African Republic, Zambia, Suriname, Namibia, Botswana, South Africa

Research Question 3 Is a higher tax revenue as a % of GDP associated with less income inequality?

Answer: No

Research Question 4 - Is Higher Income Per Person - GDP Per Capita associated with less income inequality?

Answer: No, but weak negative correlation.

Research Question 5 - Is Higher Investment as % GDP associated with less income inequality?

Answer: No

Research Question 6 - Is Higher EIU Democracy Index associated with less income inequality?

Answer: No, but weak negative correlation.

The above results suggest that there are other drivers for the overall reduction in income inequality. Futher analysis of additional factors should be undertaken.

--- Original source retains full ownership of the source dataset ---

The gini index in Slovakia was forecast to continuously decrease between 2024 and 2029 by in total 0.01 points. The gini is estimated to amount to 0.22 points in 2029. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Slovenia and Hungary.

This dataset contains annual data from 130 developing countries between 1990 and 2021. It was used to study the impact of Information, Communication, and Technology (ICT) on income inequality. The variables include Gini coefficients, GDP per capita, internet access rates, mobile cellular subscriptions, and other socio-economic indicators such as education levels and female labor force participation. Data were compiled from multiple sources, including the World Development Indicators (WDI) and the Standardized World Income Inequality Database (SWIID). The dataset was cleaned and harmonized for consistency and accuracy.

This is the data used in the research "Complexity, Inequality, and Internet". Inequality is quantified using the Gini coefficient after taxes and transfers, sourced from the Standardized World Income Inequality Database, incorporating data from the OECD, World Bank, and ECLAC (Solt, 2020). Explanatory variables include the Economic Complexity Index (ECI) from the Atlas of Economic Complexity (The Growth Lab at Harvard University, 2019), normalized within a range of -3 to +3; real GDP per capita in 2017 US dollars from the Penn World Table (Feenstra, et al., 2015); and internet access, measured as the percentage of the population using this service (The World Bank, 2023). The dataset comprises 126 countries with data spanning from 1995 to 2020.

This data is presented in CSV and XLSX formats for further review.

The gini index in Czechia was forecast to continuously decrease between 2024 and 2029 by in total 0.01 points. The gini is estimated to amount to 0.24 points in 2029. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Poland and Slovakia.

Information about GDP per capita; GDP growth; total public expenditure as percent of GDP; percent public consumption; gini coefficient for equivalent disposable income; percent imports from developing countries; inflation; percent social security transfers; percent unionized; percent unemployed; female labor force participation rate; percent employed in industry; percent employed in agriculture; percent employed in service sector; percent of population aged 0-14 years; percent of population aged 15-64 years; percent of population aged 65 years or more for 16 industrialized countries during the period 1966-1994.

Purpose:

Find the reason to why income distribution change in industrialized countries.

Dataset for tables and figures

The gini index in Poland was forecast to remain on a similar level in 2029 as compared to 2024 with 0.29 points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Slovakia and Slovenia.

Research Hypothesis

The research investigates the relationship between economic growth and income inequality, drawing on Kuznets' theory of an inverted U-shaped relationship. The central hypotheses are:

H0: Income inequality is not affected by GDP growth, indicating no relationship between economic growth and income inequality.
H1: GDP growth influences income inequality, which may increase or decrease depending on societal and economic contexts.
H2: GDP growth positively affects income inequality, widening income disparities.
H3: GDP growth negatively affects income inequality, reducing disparities and promoting equitable distribution.
H4: In lower-middle-income countries, GDP growth reduces income inequality.

Description of Data

The study utilizes data from the World Bank for 39 countries spanning the years 2004 to 2019. The dataset includes:

Gross Domestic Product (GDP): Measured in constant local currency units (LOGGDP), used as a proxy for economic growth.
Gini Index: A standardized measure of income inequality, ranging from 0 (perfect equality) to 100 (maximum inequality).
Income Categories: Countries are grouped into high, upper-middle, and lower-middle income categories based on the World Bank’s GNI per capita classification.

Methodology and Data Gathering

Selection Criteria: Countries were selected to represent diverse income groups, ensuring a balanced and comprehensive analysis of varying economic contexts.
Data Source: All data were sourced from the World Bank’s publicly available databases.
Data Analysis:
  Correlation analysis to explore the general relationship between GDP and inequality.
  Linear regression models to identify causal relationships across income categories.
  Group-specific analysis to investigate how GDP impacts inequality within high-, upper-middle-, and lower-middle-income countries.

Notable Findings

Overall Trends:
  Across all countries, a positive correlation was observed between GDP and the Gini index, indicating that GDP growth is generally associated with increasing income inequality.
  The regression model (GINI = 23.931 + 0.937 × LOGGDP) confirmed a statistically significant relationship, with an F-value (p < 0.05) supporting the model’s validity.

Income Group Analysis:
  High-Income Countries: No statistically significant relationship between GDP growth and inequality.
  Upper-Middle-Income Countries: A weak relationship was observed, but it lacked statistical significance.
  Lower-Middle-Income Countries: A significant negative relationship was identified (β = -22.291, p < 0.001), suggesting that in these countries, GDP growth reduces income inequality.

Interpretation and Use of Data: The findings can be interpreted in light of Kuznets' hypothesis, which posits that inequality first rises and then falls as economies develop.

Iran is a country locating in Middle east. Iran is located in a strategic region at the crossroads of Europe, Asia, and Africa. This has made it a major center of trade and commerce for centuries. Iran is also a member of the United Nations, the Non-Aligned Movement, and the Organization of Islamic Cooperation.

Despite its rich history, large population, and abundant economic potential, Iran is a lower-middle-income country (according to the World Bank). It has large reserves of raw materials, including oil, gas, and minerals, but unfortunately, it does not fully utilize these resources.

This dataset is all the data about Iran in the world bank website. Here is a summary:

Economic data(2022/23) - GDP (current US$): 463billion - GDPpercapita(currentUS): $5,211 - Inflation, GDP deflator (annual %): 31.5% - Oil rents (% of GDP): 25.6% - Gini index: 38.8 (2019)

Social data - Population, total: 88.5 million (2022) - Population growth (annual %): 1.1% (2022) - Net migration: 28,080 (2021) - Life expectancy at birth, total (years): 77 (2021) - Human Capital Index (HCI) (scale 0-1): 0.63 (2020)

Environmental data - CO2 emissions (metric tons per capita): 7.2 (2021) - Renewable energy consumption (% of total final energy consumption): 3.6% (2021) - Forest area (% of land area): 7.8% (2020)

You can access the data in this link. There is also lots of plots and other fun tools which you should try.

[World Bank notes] The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In Iran, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in Iran. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For Iran, this applies to 1972-2022. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.

It is noted that the reporting period for national accounts data is designated as either calendar year basis (CY) or fiscal year basis (FY). For Iran, it is fiscal year based (fiscal year-end: March 20).

This project aims to explore the relationship between the social conditions of different countries and the prevalence of mental disorders. By integrating multiple datasets, including mental health statistics, economic indicators, and demographic variables, we analyzed patterns and potential correlations to gain valuable insights.

To facilitate this analysis, we developed an interactive dashboard featuring various visualizations that compare different variables. The final dataset includes:

• Demographic & Geographic Data: Country ISO code, country name, age range, sex, rural population percentage.
• Health Indicators: AVD for mental disorders, suicide mortality rate (2019), life expectancy at birth.
• Economic & Social Indicators: HDI value, rank and classification, GDP, GDP per capita (PPP, constant 2021 dollars) and GDP classification, Gini Index (classified as high/not high), unemployment rate (ILO estimate, 2021).

By combining these diverse data sources, the project provides a comprehensive understanding of how social and economic conditions influence mental health outcomes globally.

This work was developed as part of a course in our degree program.

A cross-national data set of 21 variables was assembled for 212 countries from three sources (Barro and Lee 1994; Gordon 2005; CIA World Fact Book 2005). Our data set includes several proxy measures for national wealth, cultural diversity, social instability (both at national and international levels), and demography. Separate diversity measures were calculated for three different cultural domains, namely language, religion and ethnic groups . In addition, wealth variables (per capita GDP, and GINI, the coefficient of income inequality) were assembled, along with indicators of societal functioning drawn from the literature (especially Barro and Lee 1994), including indices of political rights (PRIGHTSB), revolutions and coups d'états (REVCOUP), and political instability (PINSTAB). Measures of international conflict were extracted from the social science literature, and the following were used: the proportion of the time between 1960-85 the country was involved in an external war (WARTIME), the number of international disputes in which the country was involved (TOTINTDISP), and an index of total military expenditure (TOTMILITEXP). Possible confounding variables such as population size (POPSIZE) and the number of international borders (NBINTBORDERS) were also included.

New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of 0.52 in 2023. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year.

The datasets are collated from reputable international organization to analyse the links between inequality (in consumption and income) and terrorism in Africa. The data are for 46 African countries. The details of the datasets consist domter - Domestic terrorism; transter - Transnational terrorism; unter - Unclear terrorism; totter - Total terrorism; gini - Gini coefficient; theil - Theil coefficient; atkin - Atkinson coefficient; palma - Palma ratio; totnat - total natural resources; surface - Surface area(log); polreg - political regime; conflicts - Dummy (1 conflicts, 0 no conflict); trade - Trade openness; and gdppc - GDP per capita.