The World Inequality Database (WID.world) aims to provide open and convenient access to the most extensive available database on the historical evolution of the world distribution of income and wealth, both within countries and between countries.
HISTORY OF WID.world
During the past fifteen years, the renewed interest for the long-run evolution of income and wealth inequality gave rise to a flourishing literature. In particular, a succession of studies has constructed top income share series for a large number of countries (see Thomas Piketty 2001, 2003, T. Piketty and Emmanuel Saez 2003, and the two multi-country volumes on top incomes edited by Anthony B. Atkinson and T. Piketty 2007, 2010; see also A. B. Atkinson et al. 2011 and Facundo Alvaredo et al. 2013 for surveys of this literature). These projects generated a large volume of data, intended as a research resource for further analysis, as well as a source to inform the public debate on income inequality. To a large extent, this literature follows the pioneering work of Simon Kuznets 1953, and A. B. Atkinson and Alan Harrison 1978, and extends it to many more countries and years.
THE WORLD TOP INCOMES DATABASE (2011)
The World Inequality Database was initially created as the The World Top Incomes Database (WTID) in January 2011 with the aim of providing convenient and free access to all the existing series. Thanks to the contribution of over a hundred researchers, the WTID expanded to include series on income inequality for more than thirty countries, spanning over most of the 20th and early 21st centuries, with over forty additional countries now under study.
The key novelty has been to combine fiscal, survey and national accounts data in a systematic manner. This allowed us to compute longer and more reliable top income shares series than previous inequality databases (which generally rely on self-reported survey data, with large under-reporting problems at the top, and limited time span). These series had a large impact on the global inequality debate. In particular, by making it possible to compare over long periods of time and across countries the income shares captured by top income groups (e.g. the top 1%), they contributed to reveal new facts and refocus the discussion on rising inequality.
In principle, all the top income share series respond to the same general methods: following the pioneering work of S. Kuznets (1953), they use income tax data, national accounts, and Pareto interpolation techniques to estimate the share of total income going to top income groups (typically the top decile and the top percentile). However, despite researchers’ best efforts, the units of observation, the income concepts, and also the Pareto interpolation techniques were never made fully homogeneous over time and across countries. Moreover, for the most part attention has been restricted to the top decile, rather than the entire distribution of income and wealth. These elements pointed to the need for a methodological re-examination and clarification.
FROM THE WTID TO THE WID (2015)
In December 2015, the WTID was subsumed into the WID, The World Wealth and Income Database. In addition to the WTID top income shares series, this first version of WID included an extended version of the historical database on the long-run evolution of aggregate wealth-income ratios and the changing structure of national wealth and national income first developed by T. Piketty and G. Zucman 2013, 2014 (see also T. Piketty, 2014, for an attempt to propose an interpretative historical synthesis on the basis of this new material and of the top income shares series). We changed the name of the database from WTID to WID in order to express the extension in scope and ambition of the database, as well as the new emphasis on both wealth and income.
At the same time, over the last years the distribution of personal wealth has been receiving increasing attention after having been neglected for decades. The work on top income shares was recently extended to study the long run evolution of top wealth shares (see E. Saez and G. Zucman 2016, F. Alvaredo, A. Atkinson and S. Morelli 2017, and B. Garbinti, J. Goupille and T. Piketty 2016).
FROM INCOME INEQUALITY TO WEALTH INEQUALITY
One reason is the growing recognition that, in seeking explanations for rising income inequality, we need to look not only at wages and earned income but also at income from capital. Income from interest, from dividends, and from rents represents a minority of total personal income, but it is nonetheless significant, especially at the top of the distribution. The ratio of total personal wealth to total personal income has been rising. One consequence is that the role of inherited wealth – which declined for much of the twentieth century – has, in a number of countries, begun to acquire greater significance. In addition, there is extensive evidence – e.g. from billionaire rankings – suggesting that top global wealth holders have grown much faster than average and have therefore benefited from a substantial increase in their share.
In order to produce reliable estimates of wealth inequality, it is becoming increasingly critical to combine different sources in a consistent manner, including income tax data (using the capitalization method) and inheritance tax data (using the mortality multiplier method), following the pioneering work of A. B. Atkinson and A. Harrison (1978). One also needs to introduce new sources such as global billionaire rankings, and to address novel issues such as cross-border assets and offshore wealth (G. Zucman, 2013, 2014). More generally, it is becoming more and more critical to measure the inequality of income and wealth from a global perspective, and not simply at the country level.
THE WORLD INEQUALITY DATABASE (WID.WORLD) : A NEW WEBSITE, A NEW AMBITION (2017)
In January 2017, with the objective of reaching yet a wider audience of researchers and general public, we released the first version of the more user-friendly website, WID.world, hosting the World Inequality Database.
These changes come along with a new ambition. Thanks to the continuous cooperation of the WID.world Fellows, we pursue our efforts to expand the database into three major directions.
First, we keep expanding the time coverage and the geographical coverage of the database, in particular to the countries of Asia, Africa and Latin America. We also keep updating the database with new observations, as official bodies release the necessary information each year. Additionally, we will progressively include inequality series at the sub-national level whenever possible (series of top income shares for each state in the United States are already available, as well as for urban and rural China).
Next, we plan to provide more series on wealth-income ratios and the distribution of wealth, and not only on income. Third, we aim to offer series on the entire distribution of income and wealth, from the bottom to the top, and not only for top shares.
The overall long-run objective is to be able to produce Distributional National Accounts (DINA), that is, to provide annual estimates of the distribution of income and wealth using concepts of income and wealth that are consistent with the macroeconomic national accounts. This also includes the production of synthetic income and wealth micro-files, which will also be made available online.
WHAT WE NOW HAVE AND DO NOT HAVE IN WID.WORLD (November 2022)
We should make clear that the long-run objective of WID.world – i.e. the production of annual Distributional National Accounts (DINA) describing the entire distribution of income and wealth, from bottom to top, using concepts consistent with macroeconomic national accounts – will be implemented gradually.
The history of our database started in 2011 with a focus on top income shares series, which was subsequently extended to aggregate wealth series in 2015. Between 2016 and 2019, DINA series for the full distribution of income and wealth were gradually added for more than 100 countries or regions, including the USA, Europe, China, India, Russia, Brazil, the Middle-East and other Asian and African economies. As of now (November 2022), the database includes series for the distribution of income, wealth, labor income by gender for all countries in the world. We should emphasize however that due to lack of proper data access in a large number of countries, these series should be viewed as imperfect and provisional. They are based in some cases on regional and country imputations based on regions and countries with similar characteristics (see our library for full methodological details).
PUBLICATION OF THE 2022 WORLD INEQUALITY REPORT
Recent findings from the WID.world database are presented and discussed in the World Inequality Report 2022. By developing this report, the World Inequality Lab seeks to fill a democratic gap and to equip various actors of society with the necessary facts to engage in informed public debates on inequality. For more information, please visit wir2022.wid.world.
A LONG-TERM, CUMULATIVE, COLLABORATIVE RESEARCH PROCESS
We should stress at the onset that our methods and series are and will always be imperfect, and subject to revision. We attempt to combine the different data sources available (in particular fiscal data, survey data and national accounts) in a more systematic way than what was done to date, but more progress is yet to come. We provide a detailed and explicit description of our methodology and sources, so that other users can contribute to their improvement. Our series and methods should be viewed from the perspective of a long-term, cumulative, collaborative research process.
In this spirit, we also provide a new set of research tools for scholars, journalists, or any interested user in the production of their own inequality datasets. Our programs allow for the estimation of income and wealth distributions based on raw tabulated data, such as those provided by statisical agencies and tax administrations. They can also be used to combine distributions from different countries and produce representative synthetic files. The programs are based on generalized, non-parametric Pareto interpolation techniques. They can be run directly from our website with no prior technical knowledge. Users can also download and install our open-access R-language codes on their computers.