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.

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

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=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 *** 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).

Abstract The issue of social inequalities is a subject of recurrent studies and remains relevant due to the growing trend of these inequalities over the years. This study proposes the creation of the Health Inequality Index (HII) composed of health indicators – Mean life span and Mean Potential Years of Life Lost (PYLL) – and socioeconomic indicators of income, schooling, and population living in poverty in the city of Natal – the State Capital of Rio Grande do Norte, Brazil. Therefore, a probabilistic linkage was made between mortality and socioeconomic databases in order to capture the census tracts of households with death records from 2007 to 2013. The authors used the Principal Component Factor Analysis to calculate the index. The Health Inequality Index showed areas with worse socioeconomic and health conditions located in the suburban areas of the city, with differences between and within the districts. The difference in the mean life span between the districts of Natal arrives at 25 years, and the worst district has mortality rates comparable to poor African countries. Public policymakers can use the index to prioritize actions aimed at reducing or eliminating health inequalities.

Factors that shape super emitter household footprints (GHG intensity and income) (2019) and a comparison of super emitter employment by sector to that of the overall U.S. economy.

The share of pre-tax and post-tax national income and emissions (2019) captured by each income group, for both the supplier and producer frameworks.