Data on Income Inequality
LIS currently hosts two databases: The Luxembourg Income Study Database (LIS) and the Luxembourg Wealth Study Database (LWS). The LIS database is based on micro data collected from around the world and includes disposable household and individual income, democracy, employment, and expenditure figures. The data reaches back to 1968 and currently covers 41 countries. For a list of the countries included in the dataset see here. LIS is a non-profit data center located in Luxembourg with a satellite office at the City University of New York (CUNY) Graduate Center.
PROS - Has made data from around the globe comparable to one another. High-quality data for many of the countries in the dataset. Good coverage across time.
CONS - Number of observations and coverage of countries is limited compared to some of the other data sets.
Access: LIS data is made available to students for free through a remote-execution system. To register visit this page.
LIS Cross-National Data Center
The World Top Incomes Database is compiled based on the work of Anthony Atkinson, Facundo Alvaredo, Thomas Piketty, Emmanuel Saez, and others. The WTID collects data from tax records and is constantly evolving since it is an ongoing project. Currently, the database covers 31 countries and the figures for each country generally reach back as far back as tax collection in that country has been recorded.
PROS - Has coverage of 30+ countries over a long time frame (1870-). The series are annual and homogenous. Compared to survey-based data, which has more nonresponse and under-reporting at the top, the WTID can offer a better measure of the top of the distribution.
CONS - WTID is not fully consistent with national accounts. It deals mostly with the distribution of taxable income, not national income. Tax data misses tax-exempt income and tax evasion. The data is silent on post-tax and transfer income and has less to say about the distribution within the bottom 90%.
Access: Download data from the WTID here.
World Top Incomes Database (WTID)
The Estimated Household Income Inequality Database is compiled by the University of Texas Inequality Project. EHII combines data from the UTIP-UNIDO and the World Bank's Deininger & Squire data set and consists of a panel of estimated Ginis for a large set of countries.The data has a total of 3872 estimates of gross household income inequality for 149 countries covering the period from 1963 to 2008.
PROS - Wide and dense coverage with strong estimates (there are 3872 observations covering 148 countries in the data set). Estimates have been shown to be consistent with LIS and SILC data.
CONS - Figures are estimates derived from other measurements.This is not necessarily a drawback, but something to consider.
Access: You can download spreadsheets and presentations here.
Estimate Household Income Inequality (EHII)
The OECD collects disposable household income data and produces Ginis for most of its member countries. The OECD data covers 34 countries over the period 1983-2012.
PROS - Consistency in data collection.
CONS - Limited coverage across countries.
Access: You can access the data here.
The OECD - Income Inequality
The SILC data was launched in 2003. The data includes cross-sectional and longitudinal data on disposable household income, poverty, social exclusion, other living conditions variables. SILC data is based on surveys. The project initially covered seven EU countries and now covers 33 countries. Observations reach back to 1995.
PROS - High-quality data
CONS - Coverage restricted to European countries.
Access: Information on how to apply for access to the SILC microdata can be found here.
EU's Statistics on Incomes and Living Conditions (SILC)
CEDLAS is a research center based at the University of La Plata in Argentina. Woking with the World Bank Latin America and the Caribbean Poverty and Gender Group, CEDLAS has produced a database called the Socio-Economic Database for Latin America and the Caribbean. The database consists of microdata collected from household surveys conducted in 24 Latin American and Carribean countries over the course of the 1990s and 2000s. The data includes inequality indicators ranging from shares of deciles, income ratios, the Gini coefficient, Thiel index, and Atkinson index.
PROS - Great resource for Latin American and Caribbean countries.
CONS - Coverage restricted to specific regions.
Access: You can access the inequality data file here.
Center for Distribution, Labor and Social Studies (CEDLAS)
The World Bank offers worldwide GINI coefficients as one of their World Bank Development Indicators (WDI) and covers 149 countries from as early as 1978.
PROS - Easy access. Data can be used in conjunction with the many other WDIs.
CONS - Figures are supplied to the World Bank by third parties, hence there are significant inconsistencies in the way the data is collected and measured with little effort to standardize.
Access: You can access the data here.
World Bank Development Indicators (WDI)
The Standardized World Income Inequality Database (SWIID) is a database created by Frederick Solt who is at the Department of Political Science at the University of Iowa. SWIID draws data from LIS, WIID, EHII and other data and synthesizes them. The database provides cross-national comparative data covering 153 countries.
PROS - SWIID has figures for most of the countries around the globe. It contains 7000 Ginis for both disposable and market income. The database makes figures from various other datasets compatible with each other.
CONS - Many of SWIIDs figures are not actual measurements, but derived figures that are used to fill in the gaps across space and time.
Access: You can download the SWIID data from dataverse here.
Standardizd World Income Inequality Database (SWIID)
The Historical Household Budgets (HHB) project is an investigation of the long-run evolution of living standards around the world on the basis of household budgets. The HHB database contains hundreds of thousands of family-level records, covers two centuries (1800-today), and embraces the five continents of the globe. HHB researchers are interested in monetary indicators – income, wealth and expenditure, wages and prices – as well as in non-monetary dimensions of well-being, such as health and education outcomes, labor force participation, dwelling characteristics, and many others.
Access: Link to the HHB website here
Historical Household Budgets
OWID is an online publication that shows how living conditions around the world are changing. It communicates this empirical knowledge through interactive data visualizations (charts and maps) and by presenting the research findings on global development that explain what drives the changes that we see and what the consequences of these changes are.
Access: Visit the world in data inequality database here
Our World In Data Inequality Database
Compiles measures of wealth inequality across selected OECD countries. Indicators include mean and median net wealth, financial and non-financial assets holdings, percentile ratios, and indebtedness indicators, with possibility of breaking these down by age groups and overtime.
PROS - Follows the OECD Guidelines for micro statistics on household wealth (OECD 2013).
CONS - Some missing observations for countries and/or years.
Access: You can access the database here
OECD Wealth Distribution Database
Compiled by the European Central Banks, HFCS collects data on household finances, consumption, and financial behaviour in the euro area countries, controlling for various socio-economic characteristics. It consists of two waves, covering 2010/2011 and 2013/2015 respectively. Sample consists of around 62,000 households across 15 countries in the first wave and 84,000 households across 18 countries in the second wave.
PROS - Harmonised and comparable household survey data on wealth, income, and consumption in the euro zone. Oversampling of wealthy households. Provides detailed statistics for both core and peripheral euro area countries, as well as Hungary and Poland in the second wave.
CONS - Complex data structure (multiple imputation survey data) - potential software limitations in data analysis. Doesn’t include the present value of future pension wealth.
Access: The database is provided free of charge. You can apply for access here