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  The two main data sources used in the DINA series continue to be income tax data and national accounts (just like in the WTID series), but we use these two core data sources in a more systematic and consistent manner, with fully harmonized definitions and methods, and together with other sources such as household income and wealth surveys, and inheritance, estate, and wealth tax data, as well as wealth rankings provided by “rich lists” compiled by the press. In most cases, the general trends in inequality depicted in the WTID series will not be very different in DINA series.4 However, the latter will allow for more precise comparisons over time and across countries, more systematic world coverage, and more consistent analysis of the underlying mechanisms.5

  In the DINA Guidelines (Alvaredo et al., 2016) the following key elements used in WID.world are discussed at length:

  • The units of observation.

  • The income concepts (pre-tax national income, pre-tax factor income, post-tax disposable income, post-tax national income, and fiscal income) and the wealth concepts (personal wealth, private wealth, public wealth, corporate wealth, and national wealth, as well as the corresponding notions of capital income flows and rates of return).

  • The methods (e.g., imputation) employed to reconcile income tax returns and household survey micro-files with national accounts totals, as well as with wealth inequality sources.

  • The methods employed to produce synthetic microfiles on income and wealth.

  • The methods that can be used in the case of countries and time periods with more limited data sources.

  In this section, we briefly refer to the units of observation and the income and wealth concepts used in WID.world, but the interested reader should consult the DINA Guidelines for the full documentation and a thorough investigation of details, problems, limitations, and challenges.

  As was the case with the development of national accounts, the methodological discussion starts from the perspective of the developed countries, given the higher (though not perfect) quality and availability of data from all sources. A number of additional and important problems arise when we consider developing countries. In many cases—e.g., in China, India, or Mexico today—we only have income tax data for the top of the distribution, and the questions involve how to combine them with the household survey data that exist for the lower part of the distribution, and even the representativeness of tax data. Piketty, Yang, and Zucman (2017) provide an illustration for the case of China. In this respect, it should also be noted that in developing countries, the underground and informal economy may play a more significant role than in developed countries, possibly requiring different imputation techniques and different means of bridging gaps between micro-data and national accounts totals. Additionally, the discrepancies of both levels and trends from the existing data sources can be very large (Bourguignon, 2015) and deserve special and case-by-case attention.

  The Units of Observation

  One of the major limitations of the WTID series was the lack of homogeneity of the micro-level observation unit. Most WTID series were constructed by using the “tax unit” (as defined by the tax law of the country at any given point in time) as the observation unit. In joint-taxation countries like France and the United States, the tax unit has always been defined as the married couple (for married individuals) or the single adult (for unmarried individuals), and the top income shares series that were produced for these two countries did not include any correction for the changing structure of tax units (i.e., the combined income of married couples is not divided by two, so couples appear artificially richer than nonmarried individuals). This is problematic, since variations in the share of single individuals in the population, or in the extent of assortative mating in couples (being in a couple with a person similar to you socio-economically), could potentially bias the evolution of income inequality in various and contradictory ways. In some other countries, the tax system switched to individual taxation over the course of the history of the income tax (e.g., in 1990 in the United Kingdom), which creates other comparability problems in the WTID series (see Atkinson, 2005, 2007).

  In order to correct for these biases, our DINA series try to use homogenous observation units. Generally speaking, our benchmark unit of observation is the adult individual. That is, our primary objective is to provide estimates of the distribution of income and wealth between all individuals aged 20 years and over (such as the shares of income and wealth going to the different percentiles of the distributions of income and wealth). Whenever possible, we also aim to construct estimates of individual income and wealth distribution that can be decomposed by age, gender, and number of dependent children. Ideally, we aim at producing synthetic micro-files providing the best possible estimates of the joint distribution—by age, gender, and number of dependent children—of income and wealth between adult individuals. But at the very least we want to be able to describe the distribution of income and wealth between all adult individuals.

  One key question is how to split income and wealth between adults who belong to a couple (married or not) and/or to the same household (i.e., adults who live in the same housing unit). To the extent possible, we want to produce for each country two sets of inequality series: “equal-split-adults series” and “individualistic-adults series.” In the equal-split series, we split income and wealth equally between adults who belong to the same couple. In the individualistic series, we attribute income and wealth to each individual income recipient and wealth owner (to the extent possible).

  We should make clear that both series are equally valuable in our view. They offer two complementary perspectives on different dimensions of inequality. The equal-split perspective assumes that couples redistribute income and wealth equally between the partners. This is arguably a very optimistic perspective on what couples actually do: bargaining power is typically very unequal within couples, partly because the two members come with unequal income flows or wealth stocks. But the opposite perspective (zero sharing of resources) is not realistic either, and tends to underestimate the resources available to nonworking spouses (and therefore to overestimate inequality in societies with low female participation in the labor market). By offering the two sets of series, we give the possibility to compare the levels and evolutions of inequality over time and between countries under these two different perspectives. Ideally, the best solution would be to organize synthetic microfiles in such a manner that the data users can compute their own inequality series based upon some alternative sharing rules (e.g., assuming that a given fraction of the combined income of couples is equally split) and/or some alternative equivalence scales (e.g., dividing the income of couples by a factor less than two). This is our long-run objective.

  Regarding the equal-split series, an important question is whether we should split income and wealth within the couple (narrow equal-split) or within the household (broad equal-split). In countries with significant multi-generational cohabitation (e.g., grandparents living with their adult children), this can make a significant difference (typically, broad equal-split series assume more private redistribution and display less inequality). In countries where nuclear families are prevalent, this makes relatively little difference. Ideally, both series should be offered. We tend to favor the narrow equal-split series as the benchmark series, both for data availability reasons (fiscal data are usually available at the tax unit level, which in a number of countries means the married couple or the nonmarried adult) and because there is possibly more splitting of resources at the narrow level (which is also arguably the reason why fiscal legislation usually offers the possibility of joint filling and taxation at the level of the married couple rather than at the level of the broader household, whose exact composition can vary and is not regulated by a legal relationship). However, in countries where fiscal sources are limited and where we mostly rely on household survey data (e.g., in China), it is sometime easier to compute the broad equal-split series. This should be kept in mind when mak
ing comparisons between countries (see the discussion in Piketty, Yang, and Zucman, 2017; and with the comparison between DINA series for China, France, and the United States).

  Finally, when we look at inequality of post-tax disposable income, we introduce dependent children into the analysis, in order to be able to compute the relevant cash and in-kind transfers to parents (family benefits and tax credits, education allowances, etc.).

  In the individualistic series, observed labor income and pension income is attributed to each individual recipient. This is easy to do in individual-taxation countries like the United Kingdom today, where by definition we observe incomes at the individual level. In general, labor income and pension income are also reported separately for each spouse in the tax returns and income declarations used in joint-taxation countries like France. In some cases, however (e.g., in US public-use tax files), we only observe the total labor or pension income reported by both spouses, in which case we need to use other sources and imputation techniques in order to split income appropriately between spouses (see Piketty, Saez, and Zucman, 2016).

  The issues are more complicated for capital income flows. In individual-taxation countries, we usually observe capital income at the individual level. However, in joint-taxation countries, capital income is usually not reported separately for both spouses, and we generally do not have enough information about the marriage contract or property arrangements within married couples to be able to split capital income and assets into common assets and own assets. So in joint-taxation countries we simply assume in our benchmark series that each spouse owns 50% of the wealth of a married couple and receives 50% of the corresponding capital income flow. If and when adequate data sources become available, we might be able to offer a more sophisticated treatment of this important issue.

  The Income and Wealth Concepts

  One of the other major limitations of the WTID time series was the lack of homogeneity of the income concept and its dependence on the tax laws of each country. In contrast, the income concepts used in DINA series are defined in the same manner in all countries and time periods, and aim to be independent of the tax legislation of the given country/year. We use four basic pre-tax and post-tax income concepts to measure income inequality: (1) pre-tax national income; (2) pre-tax factor income; (3) post-tax disposable income; and (4) post-tax national income (see Alvaredo et al., 2016, for a detailed discussion of definitions and challenges).6 All of them are anchored on the notion of national income (i.e., gross domestic product, minus consumption of fixed capital, plus net foreign income, for the economy as a whole) defined by using the same concepts as those proposed in the latest international guidelines on national accounts, as set forth by the 2008 UN System of National Accounts (SNA). However, in attributing income to the household sector, we apply a broader definition than is used in the 2008 SNA, as we also distribute the income of the other sectors in the economy (i.e., corporations, general government, and nonprofit institutions) rather than focusing on the household sector as defined in the national accounts. In the same way as for the income concepts, our wealth concepts refer to the latest international national accounts guidelines, based on which we define personal wealth, private wealth, public wealth, corporate wealth, and national wealth.7

  We should make clear at the outset that our choice of using national accounts income and wealth concepts for distributional analysis certainly does not mean that we believe that these concepts are perfectly satisfactory or appropriate. Quite the contrary: our view is that official national accounts statistics are insufficient and need to be greatly improved. In particular, one of the central limitations of official GDP accounting is that it does not provide any information about the extent to which the different social groups benefit from GDP growth. By using national accounts concepts and producing distributional series based upon these concepts, we hope to contribute to addressing one important shortcoming of existing national accounts, to reduce the gap between inequality measurement and national accounts and also maybe between the popular individual-level perception of economic growth and its macro-economic measurement. The other reason for using national accounts concepts is simply that these concepts at this stage represent the only existing systematic attempt to define notions such as income and wealth in a common way, which (at least in principle) can be applied to all countries independently from country-specific and time-specific legislation and data sources.

  One important limitation of existing official national accounts is the fact that consumption of fixed capital does not usually include the consumption of natural resources. In other words, official statistics tend to overestimate both the levels and the growth rates of national income, which in some cases could be much lower than those obtained for GDP. In the future, we plan to gradually introduce such adjustments to the aggregate national income series provided in the WID.world database. This is likely to introduce significant changes both at the aggregate and distributional level. We should also make clear that official national accounts are fairly rudimentary in a number of developing countries (and also sometimes in developed countries). Often they do not include the level of detail that we need to use the income and wealth definitions proposed below. In particular, proper series on consumption of fixed capital and net foreign income are missing in a number of countries, so that official series do not always allow national income to be computed.8

  Countries/Years with Limited Income and Wealth Data

  The construction of DINA series is very demanding in terms of data needs. Countries do not usually have all the data sources required, the limitations being very pronounced in many countries/years. This problem was also at the center of the development of national accounts: designing the SNA meant accepting that the standards could not be set at the level of the best, i.e., their implementation had to be feasible in less well-advanced countries. Methods need to be developed in the case of countries and periods with more limited data sources, typically on the basis of income tax tabulations rather than income tax micro-files, and/or with income tax data covering only a subset of the population rather than the entire population, and/or inadequacy of income tax data due to, for example, large or complete exemptions for capital incomes. The DINA Guidelines refer to each of these problems and illustrate the methods that can be applied with the cases of China (a country with limited access to income tax data; see Piketty, Yang, and Zucman, 2017) and France (a country with detailed tax data but where only income tax tabulations—rather than micro-files—are available prior to 1970; see Garbinti, Goupille-Lebret, and Piketty, 2017).9

  What Can We Say Based on Available Evidence? First Results from WID.world and DINA

  Income Inequality Dynamics: The United States, China, France

  We first present some selected results on income inequality for the United States, China, and France (a country that is broadly representative of the West European pattern) in Figure 6.2. All series shown follow the same general DINA Guidelines (Alvaredo et al., 2016). National accounts, surveys, and fiscal data are combined in a systematic manner in order to estimate the full distribution of pre-tax national income (including tax-exempt capital income and undistributed profits). For more detailed results and discussions, we refer to the country-specific papers (Piketty, Saez, and Zucman [2016] for the United States; Piketty, Yang, and Zucman [2017] for China; and Garbinti, Goupille-Lebret, and Piketty [2017] for France).10

  The combination of tax and survey data leads to a markedly upward revision of the official inequality estimates of China. The corrected top 1% income share is around 13% of total income in 2015, as compared to 6.5% in survey data. We stress that these estimates should be viewed as lower bounds, due to tax evasion and other limitations of tax and national accounts data, but we regard them as more realistic and plausible than survey-based estimates. The estimates illustrate the need for more systematic use of administrative records, even for countries where the tax administration is far from perfect. China had very low-income inequal
ity levels in the late 1970s, but it is now approaching the United States, where income concentration is the highest among the countries shown. In particular, we observe a complete collapse of the bottom 50% income share in the United States between 1978 and 2015, from 20% to 12% of total income, while the income share of the top 1% rose from 11% to 20%. In contrast, and in spite of a similar qualitative trend, the share of the bottom 50% remains higher than the top 1% share in 2015 in China and, even more so, in France.11

  Figure 6.2. Distribution of Income in China, the United States, and France, 1978–2015

  Note: Distribution of pre-tax national income (before taxes and transfers, except for pensions and unemployment insurance benefits) among adults. Corrected estimates combine survey, fiscal, wealth, and national accounts data. Equal-split-adult series (the income of married couples is divided by two).

  Sources: US: Piketty, T., E. Saez, and G. Zucman (2016), “Distributional national accounts: Methods and estimates for the United States,” NBER Working Paper, No. 22945; France: Garbinti, B., J. Goupille-Lebret, and T. Piketty (2017), “Income inequality in France, 1900–2014: Evidence from Distributional National Accounts (DINA),” WID.world Working Paper, No. 2017/4; China: Piketty, T., L. Yang, and G. Zucman (2017), “Capital accumulation, private property and rising inequality in China 1978–2015,” WID.world Working Paper, No. 2017/6. StatLink 2 http://dx.doi.org/10.1787/888933839677.