A high number of the countries with the highest income distribution levels are located in Eastern and Central Europe, with Slovakia topping the list. On the other end of the scale, South Africa was the country with the lowest income distribution.

Based on the degree of inequality in income distribution measured by the Gini coefficient, Colombia was the most unequal country in Latin America as of 2022. Colombia's Gini coefficient amounted to 54.8. The Dominican Republic recorded the lowest Gini coefficient at 37, even below Uruguay and Chile, which are some of the countries with the highest human development indexes in Latin America. The Gini coefficient explained The Gini coefficient measures the deviation of the distribution of income among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality. This measurement reflects the degree of wealth inequality at a certain moment in time, though it may fail to capture how average levels of income improve or worsen over time. What affects the Gini coefficient in Latin America? Latin America, as other developing regions in the world, generally records high rates of inequality, with a Gini coefficient ranging between 37 and 55 points according to the latest available data from the reporting period 2010-2023. According to the Human Development Report, wealth redistribution by means of tax transfers improves Latin America's Gini coefficient to a lesser degree than it does in advanced economies. Wider access to education and health services, on the other hand, have been proven to have a greater direct effect in improving Gini coefficient measurements in the region.

Comparing the 130 selected regions regarding the gini index , South Africa is leading the ranking (0.63 points) and is followed by Namibia with 0.58 points. At the other end of the spectrum is Slovakia with 0.23 points, indicating a difference of 0.4 points to South Africa. 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).

South Africa had the highest inequality in income distribution in 2023, with a Gini score of 63. Its South African neighbor, Namibia, followed in second. The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, a value of 100 represents absolute inequality. All the 20 most unequal countries in the world were either located in Africa or Latin America & The Caribbean.

Share of household income in four countries.

Income InequalityThe level of income inequality among households in a county can be measured using the Gini index. A Gini index varies between zero and one. A value of one indicates perfect inequality, where only one household in the county has any income. A value of zero indicates perfect equality, where all households in the county have equal income.The United States, as a country, has a Gini Index of 0.47 for this time period. For comparision in this map, the purple counties have greater income inequality, while orange counties have less inequality of incomes. For reference, Brazil has an index of 0.58 (relatively high inequality) and Denmark has an index of 0.24 (relatively low inequality).The 5-year Gini index for the U.S. was 0.4695 in 2007-2011 and 0.467 in 2006-2010. Appalachian Regional Commission, September 2013Data source: U.S. Census Bureau, 5-Year American Community Survey, 2006-2010 & 2007-2011

Actual and optimal income distributions in four countries.

Greece GR: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 36.000 % in 2015. This records an increase from the previous number of 35.800 % for 2014. Greece GR: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 34.600 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 36.200 % in 2012 and a record low of 32.800 % in 2003. Greece GR: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Dominican Republic DO: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 45.300 % in 2016. This records an increase from the previous number of 44.700 % for 2015. Dominican Republic DO: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 48.550 % from Dec 1986 (Median) to 2016, with 22 observations. The data reached an all-time high of 52.000 % in 2004 and a record low of 44.100 % in 2014. Dominican Republic DO: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Sweden SE: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 29.200 % in 2015. This records an increase from the previous number of 28.400 % for 2014. Sweden SE: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 27.600 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 29.200 % in 2015 and a record low of 25.300 % in 2003. Sweden SE: Gini Coefficient (GINI Index): World Bank Estimate 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: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 35.400 % in 2009. Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 35.400 % from Dec 2009 (Median) to 2009, with 1 observations. Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sudan – Table SD.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_income_equality. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

This statistic shows the inequality of income distribution in China from 2005 to 2023 based on the Gini Index. In 2023, China reached a score of ************ points. The Gini Index is a statistical measure that is used to represent unequal distributions, e.g. income distribution. It can take any value between 1 and 100 points (or 0 and 1). The closer the value is to 100 the greater is the inequality. 40 or 0.4 is the warning level set by the United Nations. The Gini Index for South Korea had ranged at about **** in 2022. Income distribution in China The Gini coefficient is used to measure the income inequality of a country. The United States, the World Bank, the US Central Intelligence Agency, and the Organization for Economic Co-operation and Development all provide their own measurement of the Gini coefficient, varying in data collection and survey methods. According to the United Nations Development Programme, countries with the largest income inequality based on the Gini index are mainly located in Africa and Latin America, with South Africa displaying the world's highest value in 2022. The world's most equal countries, on the contrary, are situated mostly in Europe. The United States' Gini for household income has increased by around ten percent since 1990, to **** in 2023. Development of inequality in China Growing inequality counts as one of the biggest social, economic, and political challenges to many countries, especially emerging markets. Over the last 20 years, China has become one of the world's largest economies. As parts of the society have become more and more affluent, the country's Gini coefficient has also grown sharply over the last decades. As shown by the graph at hand, China's Gini coefficient ranged at a level higher than the warning line for increasing risk of social unrest over the last decade. However, the situation has slightly improved since 2008, when the Gini coefficient had reached the highest value of recent times.

Laos LA: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 36.400 % in 2012. This records an increase from the previous number of 35.400 % for 2007. Laos LA: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 34.900 % from Dec 1992 (Median) to 2012, with 5 observations. The data reached an all-time high of 36.400 % in 2012 and a record low of 32.600 % in 2002. Laos LA: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Pakistan PK: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 33.500 % in 2015. This records an increase from the previous number of 30.700 % for 2013. Pakistan PK: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 32.050 % from Dec 1987 (Median) to 2015, with 12 observations. The data reached an all-time high of 33.500 % in 2015 and a record low of 28.700 % in 1996. Pakistan PK: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Introduction

This study reproduces the results of the article Relationship of gender differences in preferences to economic development and gender equality (DOI: 10.1126/science.aas9899) and partially its supplementary material.

The code for the analysis can be found at the following GitHub page: https://github.com/scerioli/Global-Preferences-Survey

Preparation of the data

Data Collection, Cleaning, and Standardization

The data used in the Falk & Hermle 2018 is not fully available because of two reasons:

1. Data paywall: Some part of the data is not available for free. It requires to pay a fee to the Gallup to access them. This is the case for the additional data set that is used in the article, for instance, the one that contains the education level and the household income quintile. Check the website of the briq - Institute on Behavior & Inequality for more information on it.

2. Data used in study is not available online: This is what happened for the LogGDP p/c calculated in 2005 US dollars (which is not directly available online). We decided to calculate the LogGDP p/c in 2010 US dollars because it was easily available, which should not change the main findings of the article.

Global Preferences Survey

This data is protected by copyright and cannot be given to third parties.

To download the GPS data set, go to the website of the Global Preferences Survey in the section "downloads". There, choose the "Dataset" form and after filling it, we can download the data set.

Hint: The organisation can be also "private".

The following two relevant papers have to be also cited in all publications that make use of or refer in any kind to GPS dataset:

• Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., & Sunde, U. (2018). Global evidence on economic preferences. Quarterly Journal of Economics, 133 (4), 1645–1692.

• Falk, A., Becker, A., Dohmen, T. J., Huffman, D., & Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Paper No. 9674.

GDP per capita

From the website of the World Bank, one can access the data about the GDP per capita on a certain set of years. We took the GDP per capita (constant 2010 US$), made an average of the data from 2003 until 2012 for all the available countries, and matched the names of the countries with the ones from the GPS data set.

Gender Equality Index

The Gender Equality Index is composed of four main data sets.

• Time since women’s suffrage: Taken from the Inter-Parliamentary Union Website. We prepared the data in the following way. For several countries more than one date where provided (for example, the right to be elected and the right to vote). We use the last date when both vote and stand for election right were granted, with no other restrictions commented. Some counties were a colony or within union of the countries (for instance, Kazakhstan in Soviet Union). For these countries, the rights to vote and be elected might be technically granted two times within union and as independent state. In this case we kept the first date. It was difficult to decide on South Africa because its history shows the racism part very entangled with women's rights. We kept the latest date when also Black women could vote. For Nigeria, considered the distinctions between North and South, we decided to keep only the North data because, again, it was showing the completeness of the country and it was the last date. Note: USA data doesn't take into account that also up to 1964 black women couldn't vote (in general, Blacks couldn't vote up to that year). We didn’t keep this date, because it was not explicitly mentioned in the original data set. This is in contrast with other choices made, but it is important to reproduce exactly the results of the publication, and the USA is often easy to spot on the plots.

• UN Gender Inequality Index: Taken from the Human Development Report 2015. We kept only the table called "Gender Inequality Index".

• WEF Global Gender Gap: WEF Global Gender Gap Index Taken from the World Economic Forum Global Gender Gap Report 2015. For countries where data were missing, data was added from the World Economic Forum Global Gender Gap Report 2006. We modified some of the country names directly in the csv file, that is why we provide it as an input file.

• Ratio of female and male labour force participation: Average International Labour Organization estimates from 2003 to 2012 taken from the World Bank database (http://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS). Values were inverted to create an index of equality. We took the average for the period between 2004 and 2013.

In our extended analysis, we also involved the following index:

• United Nations Development Programme Gender Development Index taken from Human Development Reports 2020. Note that we have downloaded the two tables of the Human Development Index for males and females, and used the ratio of the two as a GDI index, as described in the report.

The gini index in Morocco was forecast to remain on a similar level in 2029 as compared to 2024 with 0.39 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 Egypt and Algeria.

Ukraine UA: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 25.000 % in 2016. This records a decrease from the previous number of 25.500 % for 2015. Ukraine UA: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 27.000 % from Dec 1992 (Median) to 2016, with 19 observations. The data reached an all-time high of 39.300 % in 1995 and a record low of 24.000 % in 2014. Ukraine UA: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Supplementary material and data for journal article titled "The Moderating Effect of Gender Equality and Other Factors on PISA and Education Policy". ABSTRACT from the article: Globalisation and policy transfer in education make it incumbent upon decision makers to prioritise among competing policy options, select policy initiatives that are appropriate for their national contexts, and understand how system-specific factors moderate the relationship between those policies and student outcomes. This study used qualitative comparative analysis and correlational analyses to explore these relationships with publicly available data on socio-economic, cultural, and education conditions, and their association with PISA 2015 results in 49 countries. Findings show that gender and income equality, human development, and individualism were outcome-enabling conditions for PISA 2015 results, and gender equality was the most consistent of these conditions. These factors significantly moderated the relationships between education policy and PISA results. Implications for the identification of meaningful peer countries for comparative educational research, policy transfer, and the future expansion of PISA are discussed in the article.