Data: Gini — v2
|Free data from www.gapminder.org||id||version|
Updated: April 9, 2018
|CC BY 4.0 LICENCE||gini||v2|
|Indicator:||Gini||Are you seeing this offline? Please make sure you use the latest version. Here's the permalink:|
|Description:||Estimates of income inequality used for the width of distribution shapes in our income mountains.||gapm.io/d_gini|
|Download:||Excel file »|
|About this file|
|Gapminder has combined gini data from multiple sources , into long trends for all countries between 1800 and 2040. We only think these numbers are useful to draw income mountains as described here: gapm.io/incm. The uncertainty is large for historical gini data, and also for many countries today. Comparing income across countries, with different tax levels, social welfare and subsidies systems, adds uncertainty everywhere. Therefor, these numbers are not useful for numeric analysis. We are sharing these numebrs to allow others to replicate our visualizaiotns if they like. Please only use these ginis for visual eyeballing to see the general global development. |
In the following sheets you'll find the data with years in the top row. Three countries also have rough esimttaes of ginis for splitted into rural and urban, which you find on the six first rows of data: China, India & Indonesia.
|Data source summary|
|For our long series, we have aligned everything to PovcalNet data in 2013, which has the most comparable and broad coverage of gini data for almost all countries, during the last 2 decades. For each country the trends in PovcalNet starts in different years, and we have linked them back to 1800 based on whatever is available for that country from the three sources LIS, Chartbook Of Economic Inequality, CLIO Infra and van Zanden et. al. 2014, listed below. When data was missing, we used the closest year with data and just extended the time-series back to 1800.|
We strongly recommend against using our ginis for numeric analysis. Gapminder uses them only to create global Income Mountain, to confront the false worldview of a divided world. For this purpose, the the uncertainty of specific countries has minor effect on the big picture. In most cases, a huge differenc of gini between 30 or 40 will not change a pixel in the global picture on your screen.
For years beyond 2015 we have copied the latest available gini (often a number for 2013 from PovcalNet), as we have no way of predicting how income distributios in countries will evolve in the future. We use these numebrs only to draw the "if-scenario" describe at the bottom of this page here: gapm.io/incm
|Detailed data source documentation|
|Related data and info|
|Author of this version: Ola Rosling|
|Permalink to this version: v2|
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|Gapminder's Data License|
|Creative Common License CC BY 4.0|
|We produce free data, and most (not all!) sheets here are provided under the open license. You can use, copy, and spread this data, as long as you mention the following:|
|Free data from Gapminder.org: gapm.io/d_gini_v2|
— PovcalNet, by the World Bank has Ginis for average monthly per capita income, based on on 1500 household surveys spanning 1979-2015 and 163 countries. Almost all countries have data for several years between 2000 and 2013. Half the countries have data back to 1985. This is what we use as benchmark for our longer time-series.
— LIS: Luxembourg Income Study Database provide Gini’s for disposable household income (DHI) for 47 countries, where some of them reach back to 1970’s.
— Chartbook: The Chartbook of Economic Inequality has gathered trends for different kinds of Ginis and other inequality measures but they are not standardized. Those trends guide our guesstimations in a few cases.
— CLIO Infra: (including a few estimates from Bas van Leuven et. al.) represening
— van Zanden et. al. (2014) In our data we also have included a few estimates we got sent to us, directly from Bas van Leuven , which were part of his calculations published by OECD here: Income inequality since 1820. In Jan Luiten van Zanden, Joerg Baten, Marco Mira d'Ercole, Auke Rijpma, Conal Smith & Marcel Timmer (Eds.), How Was Life? Global Well-being since 1820 (pp. 199–215). OECD Publishing.
|These spreadsheets are part of Gapminder's data production and publishing, but there' more to it. Please follow this link to get the bigger picture of our data processes.|
|Formulas||The formulas in this workbook may be referring to other spreadsheets online, by their named ranges, and not by sheet names. If a spreadsheet is broken in the tree of formulas, we avoid other formulas to get broken, by always linking to the output data sheets, which are manually copied and pasted form the formula outputs in the sheets to the right of DATA_PREP sheet.|
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