|About this dataset||This file has multiple sheets with data for one or more indicators used by Gapminder. In this sheet below you'll first find an overview of the indicators (measures) and the list of underlaying sources. The actual data we use, is found in the sheet(s) labeled "data-...".|
This file is also a documentaiotn of the data process. To follow how the data was transformed from the original sources, start in the sheet to the far right, which holds the input data. You can then follow the process step by step, by looking at the formulas in the sheets from right to left, until you reach the output in the "data-..." sheets.
|Updated:||2018 December 5|
|Download:||Excel file »|
|Link to latest version:||http://www.gapm.io/dgini|
Contributor(s) to this version:
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|1||Gini||Gini shows income inequality in a society. A higher number means more inequality.||Gini coefficient||Coefficient||gini||measure||3|
|Dataset description:||Gapminder has combined gini data from multiple sources into long trends for all countries (and some other territories) betwee, for the period 1800 to 2040. The uncertainty is large for most countries. Comparing income across countries, with different tax levels, social welfare and subsidies systems, is not trivial. We only think these numbers are useful to draw big pictures of income inequalities, and we use them primaraley for our income mountains: www.gapm.io/incm. The numbers should not be used for numerica analysis. We are sharing these numbers for transparency purpose, to enable others to replicate our visualizations. For our long Gini-series, we have aligned all trends to the earliest year in PovcalNet, which is the source with the most comparable and broad coverage. Some Ginis from WDI were used, when WDI had more data than PovcalNet18dec, E.g. USA. For different coutneis the trends in PovcalNet starts in different years, and we have linked from those years back to 1800, based on whatever source had data, in the priority order : LIS, CBEI, Chartbook Of Economic Inequality, CLIO Infra and then vanZanden et. al. 2014. When data was missing, we used the closest year with data and just extended the time-series back to 1800. A few small countries had no data historically form any of these sources, in case they were assigned the Gini value 40, to not miss out in out charts.|
For years beyond 2015 we have reused the latest available gini (often a number for between 2010 and 2017 from PovcalNet) and continued applying it into the future. We have no way of predicting how income distributios in countries will evolve in the future. We use these static forecasts, only to draw the "if-scenario" described at the bottom of this page: gapm.io/incm
We strongly recommend against using our ginis for numeric analysis. Gapminder uses them only to create global Income Mountains, to confront the false worldview of a divided world. For this purpose, the uncertainty of specific countries has minor effect on the big picture. In most cases, even a huge difference of gini estimates between 30 or 40 for a single country, will not change even a single pixel in the global picture on your screen.
|Link to documentation:||http://gapm.io/ddgini|
|Short source summary:||Gapminder, PovcalNet, LIS, CLIO, and more|
|1||WDI||World Development Indicators, December 1, 2018, by the World Bank.|
PovcalNet, December 1, 2018, 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.
PovcalNet, March 28, 2018, 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.
|6||CLIO||CLIO Infra: (including a few estimates from Bas van Leuven et. al.)|
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.
We believe in free knowledge and therefor we produce free data. Most sheets in this file are provided under the open license, called Creative Commmon Attribution License CC BY 4.0., except those mentioned in the exceptions section below. This means you can freely use, copy, and spread the data in those sheets, as long as you mention the following: 'Free data from Gapminder.org'.
You should also mention the underlaying data sources listed above and include this link: http://gapm.io/ddgini
|License link:||Creative Common License CC BY 4.0|
|Exceptions:||The sheets starting with the word "data" are covered by this license. Other sheets are included for documentation purpose, and may include data that is governed by other licenses. Check the underlaying sources for the specific licenses in these cases.|
|Versions||Link||Changes compared to previous||Date||Contributors|
|v1||—||Combining World Bank Ginis with Van Zanden data provided by Bas v Lauven. For the many countries missing historical estimates. A rough assumption was made a standard gini of 37.||2014 September||Ola Rosling|
|v2||—||Anchored the series to PovCalNet for many additional countries that didn't have data for recent years i the preivous verison.|
Improved the rough estimates of historic data by including data from LIS, and estimates based on the Chartbook of Economic Inequality.
|2018 March||Ola Rosling|
Updated previous version with the latest data from PovCalNet
|2018 December 5||Ola Rosling|
|The Google id of this spreadsheet||1g3x_nUa9lFyu1SlKSmq8YB2ZknGco-jTQU80Jz3Reok|
|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.|
|For developers||If you like to integrate Gapmidner's data into your product, it's better if you integrate|
These spreadsheets are part of Gapminder's data production and publishing, but there's more to it. Please follow this link to get the bigger picture of our data processes.
|DDF mapping:||schema for indicator table|