For Good Measure Read online

  Facundo Alvaredo is Professor and Co-Director of WID.world at the Paris School of Economics and IIEP-UBA-Conicet, Lucas Chancel is Co-Director of WID.world at the Paris School of Economics and IDDRI, Thomas Piketty is Professor and Co-Director of WID.world at the Paris School of Economics, Emmanuel Saez is Professor and Co-Director of WID.world at University of California, Berkeley, and Gabriel Zucman is Professor and Co-Director of WID.world at the University of California, Berkeley. This chapter summarizes the recent work behind the WID.world project. In particular, we refer the reader to the following papers: Alvaredo et al., 2016; Piketty, Saez, and Zucman, 2016; Saez and Zucman, 2016; Garbinti, Goupille-Lebret, and Piketty, 2016, 2017; Piketty, Yang, and Zucman, 2017; Alvaredo, Atkinson, and Morelli, 2017, 2018; and Alvaredo et al., 2017. For the helpful discussions at the various meetings and seminars that took place since January 2014, we would also like to thank the members of the High-Level Expert Group on the Measurement of Economic Performance and Social Progress, as well as Marco Mira D’Ercole, Martine Durand, Jorrit Zwijnenburg, Peter Van de Ven (all from the OECD Statistics and Data Directorate), and participants at the HLEG Workshop on Measuring Inequalities of Income and Wealth (Berlin, September 2015). The opinions expressed and arguments employed in the contributions below are those of the authors and do not necessarily reflect the official views of the OECD or of the governments of its member countries.

  Introduction

  Renewed interest in the long-run evolution of the distribution of income and wealth has given rise to a flourishing literature over the past 15 years. In particular, by combining historical tax and national accounts data, a series of studies has constructed time series of the top income share for a large number of countries (see Piketty, 2001, 2003; Piketty and Saez, 2003; and the two multi-country volumes on top incomes edited by Atkinson and Piketty, 2007, 2010; see also Atkinson, Piketty, and Saez, 2011; and 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 has followed the pioneering work and methodology of Kuznets (1953) and Atkinson and Harrison (1978) on the long-run evolution of income and wealth distribution, extending it to many more countries and years.

  The World Top Incomes Database, WTID (Alvaredo et al., 2011–2015), was created in January 2011 to provide convenient and free access to all the existing time series generated by this stream of work. Thanks to the contributions of over a hundred researchers, the WTID expanded to include time series on income concentration for more than 30 countries, spanning most of the 20th and the early 21st centuries, and, in some cases, going back to the 19th century. The key innovation of this research has been to exploit tax, survey, and national accounts data in a systematic manner. This has permitted the estimation of longer and more reliable time series on the top income shares than previous inequality databases (which generally rely on self-reported survey data, with usually large under-coverage and under-reporting problems at the top, and limited time span). These new series had a large impact on the discussion on global inequality. In particular, by making it possible to compare the shares captured by top income groups (e.g., the top 1%) over long periods of time and across countries, they contributed to reveal new facts and refocus the discussion on rising inequality.

  In December 2015 the WTID was subsumed into WID.world (the World Wealth and Income Database, renamed in 2017 the World Inequality Database). In addition to the WTID top income shares series, the first version of WID.world included an updated historical database on the long-run evolution of aggregate wealth-to-income ratios and on the changing structure of national wealth and national income first developed in Piketty and Zucman (2014).1 The name changed from WTID to WID.world in order to reflect the extension in scope and ambition of the database, and the new emphasis on both wealth and income.

  In January 2017 a new website was also launched (www.wid.world), with better data visualization tools and more extensive data coverage. The database is currently being extended into three main directions. First, the project aims to go beyond only covering developed countries to focus more on developing countries; in recent years, tax information has been released in a number of emerging economies, including China, Brazil, India, Mexico, and South Africa. Second, WID.world plans to provide more and updated series on wealth-to-income ratios and the distribution of wealth, and not only on income. Third, it aims to cover the entire distribution of income and wealth, and not only of top groups. The overall long-run objective is to produce Distributional National Accounts (DINA).

  The development of economic statistics is a historical lengthy process that involves economic theory, the limits of available data, the construction of a body of conventions, and the agreement of the community of scholars. Macro-economic aggregates (GDP, national income) from the System of National Accounts (SNA) are the most widely used measures of economic activity. In the beginning, national accountants were also experts in distributional issues, as the inter-linkages between the estimation of national income and its distribution were clearly recognized. However, the focus of the SNA has so far always been on the main sectors in the economy, only distinguishing results for the household sector as a whole, and not providing insights into disparities within the household sector. Partly as a result of these developments, the discrepancies between income levels and growth rates displayed in national accounts and the ones displayed in micro-statistics and underlying distributional data have been growing in all dimensions: income, consumption, and wealth (see, for example, Deaton, 2005; Bourguignon, 2015; and Nolan, Roser, and Thewissen, 2016). Scholars have been clearly aware of the discrepancies, and also have some ideas to explain the reasons behind them, but systematic and coordinated action to put them in a consistent framework has started only recently.2 In 2011, the OECD and Eurostat launched a joint Expert Group to carry out a feasibility study on compiling distributional measures of household income, consumption, and saving within the framework of national accounts, on the basis of micro-data. This group, which was followed up by the creation of an OECD Expert Group on Disparities in National Accounts (EG DNA) in 2014, aimed to systematically combine micro- and macro-results to arrive at more granular breakdowns of the household sector available from the national accounts (see sidebar, “The Work of the OECD Expert Group on Disparities in a National Accounts Framework” for more information). One reason why this work has only started recently is quite clear: it is not a simple task.

  A renovated approach to the measurement of economic inequality consistent with macro-aggregates should rebuild the bridges between distributional data available from micro sources and national accounts aggregates in a systematic way. This is the main goal of the WID.world project pursued through DINA. The aim is to provide annual estimates of the distribution of income and wealth using concepts that are consistent with the macro-economic national accounts. In this way, the analysis of growth and inequality can be carried over in a coherent framework. In addition, the WID.world project aims to also include the production of synthetic micro-files (i.e., individual-level data that are not necessarily the result of direct observation but rather through estimations that reproduce the observed distribution of the underlying data, including the joint distribution of age, gender, numbers of dependent children, income, and wealth between adult individuals), providing information on income and wealth, which will also be made available online. The long-run aim is to release income and wealth synthetic DINA micro-files for all countries on an annual basis. Such data could play a critical role in the public debate, and be used as a resource for further analysis by various actors in civil society and in the academic, business, and political communities.

  THE WORK OF THE OECD EXPERT GROUP ON DISPARITIES IN A NATIONAL ACCOUNTS FRAMEWORK

  In response to the increased interest in household material well-being and its distribution, the OECD and Eurostat
launched an expert group in 2011 to carry out a feasibility study of compiling distributional measures of household income, consumption, and saving across household groups within the framework of the national accounts. A methodology was developed according to a step-by-step approach that assists countries in building the best conceptual link between the micro- and macro-data; closing any gaps between the micro-data and the national accounts totals; imputing for any items that may be lacking in micro-data sources; and linking data across sources to arrive at consistent sets of accounts for various household groups. This work was continued in 2014 by EG DNA to improve the methodology and to look into possibilities to improve the timeliness of the distributional results. OECD Member countries have engaged in two exercises to compile experimental distributional results, and some countries have already started to publish their estimates (Australia, the Netherlands, and the United Kingdom).

  The EG DNA project has a lot of similarities with the DINA project, as both aim to compile distributional results in line with national accounts totals, and overcome any discrepancies between the micro- and the macro-totals. Where DINA is focusing on income and wealth, the OECD project initially focuses on income, consumption, and saving, planning to include wealth in the second phase, probably in cooperation with the European Central Bank and Eurostat. While there are similarities, the projects also differ in some respects. First of all, the aim of the EG DNA project is to arrive at breakdowns of the household sector from the national accounts at an aggregated level, focusing on specific household groups, e.g., classified by income quintile, main source of income, or household composition, whereas DINA also aims to produce synthetic micro-data files for income and wealth. Second, the two projects apply different income definitions in deriving distributional results: whereas DINA aims to align the results to national income, i.e., for the economy as a whole (distinguishing five income concepts), the EG DNA project specifically targets the income of the household sector, with primary income, disposable income, and adjusted disposable income as main aggregates. A third difference relates to the unit of observation: while the DINA project focuses on individuals aged 20 years and older, the EG DNA considers the income of households (under the assumption that income is fully shared and that consumption decisions are made within the household), using equivalence scales to adjust for differences in household size and composition. These two methodological differences may lead to differences in distributional results derived from both projects.

  Since the start of the OECD project, member countries have engaged in two exercises compiling first sets of experimental distributional results. Figure 6.1 presents an example of results derived from the exercise conducted in 2015: it presents estimates of the S80/S20 ratio, comparing the income of households in the highest income quintile with that of households in the lowest quintile. On the basis of these results, income inequality turns out to be very high in Mexico, followed by the United States and Switzerland, whereas it is smallest in Slovenia, followed by the Netherlands, France, and Sweden. In addition to distributional results by income quintile, the experimental results also contain breakdowns into main source of income and household composition for a selection of countries, as well as information on the socio-demographic composition of the income quintiles.

  Figure 6.1. Ratio of Household-Adjusted Disposable Income of Households in the Top and Bottom Income Quintiles

  Note: Data refers to 2012 and 2011 for Australia, France, Netherlands, Portugal and Switzerland.

  Source: Zwijnenburg, J., S. Bournot, and F. Giovannelli (2017), “Expert group on disparities in a national accounts framework: Results from the 2015 exercise,” OECD Statistics Working Papers, No. 2016/10, OECD Publishing, Paris, http://dx.doi.org/10.1787/daa921e-en. StatLink 2 http://dx.doi.org/10.1787/888933839696.

  While some countries have already started to publish distributional results according to EG DNA methodology, the OECD Expert Group is pursuing its work to improve the methodology to arrive at more robust and comparable results across a broader range of countries. In that perspective, the project faces similar challenges as the DINA, particularly in obtaining a better understanding of the reasons for gaps between micro-data and national accounts totals, gaps which for some items are very substantial; and in improving the methodology to impute for items for which micro-data are lacking. This should lead to a more robust methodology and to the publication of distributional results for a broader range of countries within the next couple of years.

  Source: Text provided by Jorrit Zwijnenburg, OECD Statistics and Data Directorate.

  It is worth stressing that the WID.world database has both a macro- and a micro-dimension. The objective is to release homogenous time series both on the macro-level structure of national income and national wealth, and on the microlevel distribution of income and wealth, using consistent concepts and methods. By doing so, we hope to contribute to reconciling inequality measurement and national accounting, i.e., the micro-level measurement of economic and social welfare and the macro-level measurement. In some cases, this may require revising central aspects of key national accounts concepts and estimates. By combining the macro- and micro-dimensions of economic measurement, we are following a very long tradition. In particular, it is worth recalling that Simon Kuznets was both one of the founders of US national accounts (and author of the first national income series), and the first scholar to combine national income series and income tax data in order to estimate the evolution of the share of total income going to top fractiles in the United States over 1913–48 (Kuznets, 1953).3 This line of research continued with Atkinson and Harrison (1978), who made use of historical inheritance tax data and capital income data to study the long-run evolution of the distribution of personal wealth in Britain over 1922–72. We are simply pushing this effort further by trying to cover more countries and years, and by studying wealth and its distribution rather than only income.

  Such an ambitious long-term objective—annual distributional national accounts for both income and wealth and for all countries in the world—will require a broad international and institutional partnership. The first set of methodological principles and recommendations are being set by ongoing work on the first version of the DINA Guidelines (Alvaredo et al., 2016). There are still many methodological decisions to be taken and agreed upon. It took from the 1910s to the 1950s before scholars (such as Kuznets, Kendrick, Dugé, Stone, Meade, and Frankel) could hand over the estimation of national income to official statistics bodies. It also took a long time (from the 1950s to the 2000s) before official national accounts were able to include standardized stock accounts. In fact, the first consistent guidelines for balance sheets—covering stocks of assets and liabilities—appear in the SNA manuals of 1995 and 2008 (in some key countries, such as Germany, the first official balance sheets were released only in 2010). Along the same lines, the development of a system of DINA is expected to take a long time before consensus among scholars and the statistical community is reached. In that regard, it is very encouraging that the OECD Expert Group on Disparities in National Accounts, which is working on compiling distributional results, has already engaged in two exercises, and that the first countries have already started to publish distributional results on the basis of the Expert Group’s methodology (see sidebar).

  We should stress at the outset that our methods and time series are imperfect, fragile, and subject to revision. The WID.world DINA project attempts to combine the different data sources that are available (in particular tax data, survey data, and national accounts) in a systematic way. We also try to provide a very detailed and explicit description of our methodology and sources, so that other users can contribute to improving them. But our time series and methods should be viewed in the perspective of a long, cumulative, collective process of data construction and diffusion, rather than as a finished product.

  What Are the Concepts and Methods Being Discussed?

  The concepts and methods used in the WTID series were
initially exposed in the two collective volumes edited by Atkinson and Piketty (2007 and 2010), and in the corresponding country chapters and research articles. All country-level time series follow the same general principles: building on the pioneering work of Kuznets (1953), they combine income tax data, national accounts, and Pareto interpolation techniques in order to estimate the share of total income going to top income groups (typically the top decile and the top percentile). However, despite our best efforts, the units of observation, the income concepts, and the Pareto interpolation techniques were never made fully homogenous over time and across countries. Moreover, for the most part, attention was restricted to the top income decile rather than the entire distribution of income and wealth.

  In contrast, the DINA time series and associated synthetic micro-files aim to be fully homogenous across all of these dimensions (or at least to make much more explicit the remaining heterogeneity in data construction) and, most importantly, to provide more detailed and comprehensive measures of inequality. In the DINA series, inequality is always measured using homogenous observation units, and taxable income reported on fiscal returns is systematically corrected and upgraded in order to match national accounts totals separately for each income category (wages, dividends, etc.) using various sources, imputation methods, and techniques to align the micro- and macro-data. Currently, WID.world aims to provide series on wealth (and not only on income) and on the entire distribution (and not only on top shares).