The present document describes the general structure of the World Inequality Database (WID). It explains how information is organized within it, and how to retrieve and interpret its content.

Section 1 starts by explaining the different ways of accessing the database. Section 2 then explains the general structure of the variables. The other sections describe the different WID codes that exist in the database. Note that this document is mainly dedicated to describing the technical organization of the database. For a more in-depth description of concepts and estimation methods, please refer to the DINA guidelines or our library of papers.

Table of Contents

• 1. General Presentation
  • 1.1. How to Access the Data on WID.world
    • 1.1.1. Using the Graphing Tools on the Website
    • 1.1.2. Downloading Specific Series
    • 1.1.3. Bulk Download
    • 1.1.4. Packages for Statistical Software (R and Stata)
• 2. General Structure
  • 2.1. Construction of WID Code
    • 2.1.1. One-letter Code for Series Type
    • 2.1.2. Five-letter Code for Series Concept
    • 2.1.3. Three-digit Code for Age Group
    • 2.1.4. One-letter Code for Population Unit
  • 2.2. Country Codes
  • 2.3. Percentile Codes
• 3. Aggregate Income Variables
  • 3.1. Net National Income, GDP, Foreign Incomes, Foreign Wealth, and Consumption of Fixed Capital
    • 3.1.1. GDP and Net National Income
    • 3.1.2. Decomposition of Foreign Income
    • 3.1.3. Decomposition of Foreign Wealth
    • 3.1.4. Decomposition of National Income between Sectors
    • 3.1.5. Labor and Capital Share of National Income
  • 3.2. Income of Households and NPISH
    • 3.2.1. Income of the Sectors Combined
      • 3.2.1.1. Primary Incomes of Households and NPISH
      • 3.2.1.2. Secondary Incomes of Households and NPISH
      • 3.2.1.3. Consumption and Savings of Households and NPISH
      • 3.2.1.4. Relation between Net and Gross Variables
    • 3.2.2. Income of Households
      • 3.2.2.1. Primary Incomes of the Household Sector
      • 3.2.2.2. Secondary Incomes of the Household Sector
      • 3.2.2.3. Consumption and Savings of the Household Sector
      • 3.2.2.4. Relation between Net and Gross Variables
    • 3.2.3. Income of NPISH
      • 3.2.3.1. Primary Incomes of NPISH
      • 3.2.3.2. Secondary Incomes of NPISH
      • 3.2.3.3. Consumption and Savings of NPISH
      • 3.2.3.4. Relation between Net and Gross Variables
  • 3.3. Income of the Corporate Sector
    • 3.3.1. Income the Sectors Combined
      • 3.3.1.1. Primary Income of Corporations
      • 3.3.1.2. Secondary Income of Corporations
      • 3.3.1.3. Relation between Net and Gross Variables
    • 3.3.2. Income of Non-financial Corporations
      • 3.3.2.1. Primary Income of Non-financial Corporations
      • 3.3.2.2. Secondary Income of Non-financial Corporations
      • 3.3.2.3. Relation between Net and Gross Variables
    • 3.3.3. Income of Financial Corporations
      • 3.3.3.1. Primary Income of Financial Corporations
      • 3.3.3.2. Secondary Income of Financial Corporations
      • 3.3.3.3. Relation between Net and Gross Variables
  • 3.4. Income of the Government Sector
    • 3.4.1. Primary Income of the Government
    • 3.4.2. Secondary Income of the Government
    • 3.4.3. Consumption and Savings of the Government
  • 3.5. Current Account
    • 3.5.1. Trade Balance
    • 3.5.2. Capital Account
• 4. Distributed Income Variables
  • 4.1. “Fiscal” Income
  • 4.2. DINA Income
    • 4.2.1. Pretax Income
    • 4.2.2. Post-tax Income
    • 4.2.3. Factor Income
• 5. Aggregate Wealth Variables
  • 5.1. National Economy
  • 5.2. Households and NPISH
    • 5.2.1. Combined Sector
    • 5.2.1. Household Sector
    • 5.2.2. NPISH
  • 5.3. Corporate Sector
  • 5.4. General Government Sector
• 6. Distributed Wealth Variables
• 7. Price Index, Exchange Rates, Populations, etc.
  • 7.1. Price Index
  • 7.2. Exchange Rates
  • 7.3. Population
  • 7.4. Wealth/Income Ratios and Labor/Capital Share
  • 7.5. Inequality Transparency
• 8. Aggregate Carbon Variables
  • 8.1. National territorial carbon emissions
  • 8.2. National imported carbon emissions
  • 8.3. National exported carbon emissions
  • 8.4. National net imports of carbon emissions
  • 8.5. National carbon footprint
• 9. Distributed Carbon Variables

1. General Presentation

1.1. How to Access the Data on WID.world

Users can use the graphing capabilities on WID.world to make simple plots. Users can also download and import the data into their preferred statistical software for more advanced analyses. We provide packages to access the WID directly (R and Stata). Overall, we can distinguish four ways of accessing the data on WID.world.

1.1.1. Using the Graphing Tools on the Website

The WID.world website offers several basic graphing capabilities. The “WORLD” section of the website can be used to display the data for all the available countries on a world map and to plot the same series over time for different countries. The “BY COUNTRY” section can be used to display several series for the same country. In both cases, the variable selection is done via the menu (left-hand-side). By default, it suggests a handful of “key indicators” corresponding to the most common and widely available types of indicators requested.

To access other series, choose “more indicators” at the bottom of the menu. This section of the menu is divided into five categories. The two categories “average & total income” and “average & total wealth” refer to macroeconomic aggregates. To select a variable from these categories, click on them and then choose a concept from the menu that appears. Most concepts can be decomposed into more subconcepts by clicking on the arrow on their right. The two categories “income inequality” and “wealth inequality” refer to distributed variables. If you choose them, a menu will appear to let you select a concept (top-right corner) and a part of the distribution (or the Gini coefficient). The last category, “other indicators,” refers to other categories such as prices, exchange rates, and populations. Once you have selected a concept, the menu will ask you to specify other characteristics of the series requested (currency, population unit, age group, average/share/aggregate, depending on what is relevant to the context considered).

For country graphs, you can display several series simultaneously, assuming they have the same unit. Buttons on the right of the graph let you share the graph, export it as an image, or export the underlying data. The “?” lets you view the sources and the methodological notes for the data.

1.1.2. Downloading Specific Series

The “DATA” section of the website lets you download series directly. The menu in this section is best suited for downloading a limited number of specific series.

1.1.3. Bulk Download

For users who want to request a large number of series simultaneously, we recommend the bulk download options. The button at the bottom-left of the “DATA” section lets users download the entire database simultaneously. Users can also download all the data relative to a specific country on the country pages.

1.1.4. Packages for Statistical Software (R and Stata)

We have developed two packages to download data from the WID directly from Stata and R. Please refer to the documentation of these packages for more details.

2. General Structure

The precise structure of the WID is primarily of interest to users who download the database in bulk through the statistical packages in R and Stata.

Every data series in the WID has a WID code (which refers to the concept under consideration), a percentile code (which refers to a specific part of the distribution, if applicable), and a country (or region) code that refers to the area under consideration. Finally, every data point is also associated with a specific year. The WID codes are constructed by combining specific codes for series types, concepts, age groups, and population units.

For example, consider the following:

This refers to the share of pretax national income among equal-split adults (sptinc992j) for the top 1% (p99p100) in the United States (US) in 1970. The sections below detail the different options available in the WID for constructing variables such as the one above.

2.1. Construction of WID Code

2.1.1. One-letter Code for Series Type

The first letter of the WID code is a one-letter code that determines the type of series you are requesting. In many cases, several types of series exist for the same concept. For example, consider the pretax national income of the top 1%: you can request their share of income (s), their average income (a), or their threshold (t). Below is the list of all series types.

The series type determines its unit, as indicated in the table above. All monetary amounts are in local currency at constant prices for countries and subregions. Monetary amounts for world regions are in EUR PPP. Series are at last year’s prices (the database is usually updated yearly at the end of June). To check the year of reference, consider when the price index equals 1. You can access the price index using the variable inyixx999i, the PPP exchange rates using xlcusp999i (USD), xlceup999i (EUR), xlcyup999i (CNY), and the market exchange rates using xlcusx999i (USD), xlceux999i (EUR), xlcyux999i (CNY). Shares and wealth/income ratios are given as a fraction of 1. A top 1% share of 20% is given as 0.2. A wealth/income ratio of 300% is shown as 3.

2.1.2. Five-letter Code for Series Concept

The second to the sixth letter of the WID code is a five-letter concept code that determines the concept of the requested series. It is usually an income or wealth concept but can also correspond to auxiliary concepts such as price indexes, exchange rates, populations, etc.

Common five-letter codes include nninc (national income), ptinc (pretax income), diinc (post-tax income) or fiinc (fiscal income). However, there is a large variety of five-letter codes in the WID, described in the following sections.

2.1.3. Three-digit Code for Age Group

The three-digit code in WID codes refers to the population group for the requested variable. The most common codes are 999 (entire population), 992 (adult population, i.e., 20 and older), and 996 (working-age population, i.e., from 20 to 65). The complete list of population codes is given in the table below.

For aggregate variables, the age code indicates the population denominator for averages. For example, anninc999i refers to national income per capita, whereas anninc992i refers to national income per adult. For distribution variables, the age code refers to the population group receiving the income. For example, sptinc992j refers to the share of pretax income that accrues to a given percentile of the adult population. In contrast, flinc996i refers to labor income shares that accrue to a given percentile of the working-age population. For variables to which age groups do not apply (e.g., price indexes, exchange rates), we use the age code 999 (full population).

2.1.4. One-letter Code for Population Unit

The WID code’s last letter determines the variable’s population unit. The most common are “i” (for individuals), “j” (for equal-split), and “t” (for tax units, i.e. households). The complete list is given below.

Differences in population units only matter for distributed series. For aggregate series (or prices, exchange rates, and population series), we use the letter “i” (for individuals) by default. For distributed series, “i” means that the unit is the individual and that income or wealth is distributed to each specific individual; “j” means that the unit is the individual, but that income or wealth is distributed equally among all household members; “t” means that the taxing unit (i.e., the household) is taken directly as the statistical unit.

2.2. Country Codes

The two-letter country codes used in WID mostly follow the ISO 3166-1 alpha-2 standard. The list has, however, been amended to include world regions, country subregions, former countries, and countries not officially included in the standard. The complete list, including the composition of regions, is indicated below.

Regions within country XX are indicated as XX-YY, where YY is the region code. World regions are indicated as XX and XX-MER, the first using purchasing power parities (the default) and the second using market exchange rates.

The different classifications of regions on WID.world include standard, continental, sub-regions, political, and other. The choice of this classification depends on historical, political, and economic criteria and the availability of data for each country. The standard definition is currently presented for the region map on WID.world. However, different categorizations are also available to display the data produced by researchers and cover the broad interests of users. Some countries were grouped to track the evolution of inequality since 1820, as presented in Chancel & Piketty (2021).

Please refer to this file for the full cross-classification of regions and country codes by year.

Note: Middle East and North Africa will be written as MENA.

Country Codes

Country Subregions Codes

World Regions Codes (Purchasing Power Parity)

World Regions Codes (Market Exchange Rates)

2.3. Percentile Codes

Some of the data in WID.world refers to specific parts of the distribution. That is, we rank people according to their income or wealth, group them according to their rank, and report statistics on these different groups. They are coded pXpY, where X and Y are both numbers between 0 and 100. X correspond to the percentile for the lower bound of the group, and Y to the percentile for the upper bound (hence X < Y).

WID.world defines the following groups:

Key Groups

They are the most widely used. They include p0p100, p0p50, p50p90, p99p100,99p100, p99.99p100, in addition to all deciles (p0p10, p10p20, p20p30, … , p90p100). For example, p0p50 refers to the bottom 50% of the population, and p99p100 to the top 1%.

Detailed Percentile Groups

They are narrow groups that cover the whole distribution. They include all percentiles (p0p1, p1p2, …, p98p99, p99p100), but also tenths of a percentile in the top 1% ( p99.1p99.2, p99.2p99.3, …, p99.8p99.9, p99.9p100), hundreds of a percentile in the top 0.1% (p99.9p99.91, p99.92p99.93, …, p99.98p99.99, p99.99p100), and thousands of a percentile in the top 0.01% (p99.99p99.991, p99.992p99.993, … , p99.998p99.999, p99.999p100). For example p99.5p100 correspond to the top 0.5% of the population.

Detailed Top Groups

They also cover the whole distribution but refer to the entire population above a percentile. They take the form p0p100, p1p100, p2p100, …

Detailed Bottom Groups

They also cover the whole distribution but refer to the entire population below a percentile. They take the form p0p100, p0p99, p0p98, …

Some population groups can belong to several of the above categories (for example p99p100 is in all three). When that happens, the group appears only once in the data.

The population group p0p100 refers to the entire distribution and is used for aggregate series for which there is no distribution available, and for economic series with no individual meaning such as price indices and exchange rates.

For all these population groups, we provide:

Wealth or Income Share

These correspond to the income or wealth of the group, divided by the total for the whole population. For example, the income of the group p99p100 is the top 1% income share.

Wealth or Income Averages

These correspond to the average wealth or income of the people in the group. For example, the wealth average of the group p90p99 is the average income of the top 10% excluding the top 1%.

Wealth or Income Thresholds

they correspond to the minimum level of income or wealth that gets you into a group. For example, the income threshold of the group p90p100 is the income of the poorest individuals in the top 10%. By definition, it is equal to the income threshold of the groups p90p99 or p90p91.

Inverted Pareto Coefficient

For all top groups, we also provide the inverted Pareto coefficient. It is the ratio of the group average over the group threshold. They are a measure of the fatness of the tail of the distribution of income or wealth. See Thomas Blanchet, Juliette Fournier and Thomas Piketty, “Generalized Pareto Curves: Theory and Applications” (2017) for details.

Beware that people are not always ranked with respect to the quantity that the variable measures. That is true in particular for subcomponents of income or wealth. For example, a variable for “capital income” may rank individuals according to their total income (hence p99p100 would refer to the capital income of the top 1% of both labor and capital income earners), or it may rank them according to their capital income (hence p99p100 would refer to the capital income of the top 1% of capital income earners). The description of the variable specifies which ranking is used.

3. Aggregate Income Variables

The WID contains a fairly complete decomposition of national income into various subcomponents, mostly following principles of the UN SNA. These different aggregates have their specific five-letter codes, which are described below. Their cover the sequence of accounts from primary incomes (i.e. income before direct taxes and transfers) to consumption and savings.

This section presents these five-letter codes in the different decompositions of national incomes into components and subcomponents. Whenever possible, we indicate the corresponding UN SNA codes that correspond to each concept.

3.1. Net National Income, GDP, Foreign Incomes, Foreign Wealth, and Consumption of Fixed Capital

3.1.1. GDP and Net National Income

We start from the relationship between national income and GDP, presented in the table. GDP is a measure of all that is produced in a country over a given year, while national measures the income that accrues to residents. These quantities differ for two reasons. First, some of the domestic production is done by companies that are owned by foreigners, and by foreign workers. Conversely, residents can own foreign companies or work abroad. These flows are recorded under foreign incomes. Second, some of the production must be used to replace the depreciating capital stock, and therefore cannot be used to either consume or accumulate wealth, so it is not “income” in the traditional sense. This flow is recorded as the “consumption of fixed capital.”

GDP and National Income

3.1.2. Decomposition of Foreign Income

Foreign incomes can be decomposed between inflows (“received”) and outflows (“paid”), or it can be directly measured net (inflows minus outflows). Then, they can be decomposed between labor incomes (i.e. wages of employees that work in a country different from their country of residence), and capital incomes (i.e. property income generated by foreign assets). They also include a (usually small) component that correspond to taxes and subsidies on production from or to other countries.

Capital incomes may be further decomposed between foreign direct investment (i.e. ownership with direct control over the operation the company) and portfolio investment (i.e. ownership without direct control). As shown in the tables below, our recording of foreign portfolio investment differs from official statistics for two reasons. First, we estimate an amount of income that goes unrecorded in official statistics because it comes from assets hidden in offshore tax havens. Second, we estimate and include retained earnings from foreign portfolio investment in foreign income flows, which unlike retained earnings from foreign direct investment is not included in net foreign incomes in the UN SNA.

Paid/Received

Labor/Capital

3.1.3. Decomposition of Foreign Wealth

3.1.4. Decomposition of National Income between Sectors

We decompose national income between the different institutional sectors. The three main sectors are: households (and non-profits), corporations, and the government. When possible, we also distinguish between households and non-profits, and between financial and non-financial corporations.

In following tables, we consider the decomposition of “primary income” and “secondary income.” Both of them sum up to national income, but primary income refers to the income that accrues to a given sector before the operation of direct taxes and transfers (but after taxes on production), and secondary income to the income after taxes and transfers. (Our concept of “secondary income” differs from the UN SNA concept of disposable income because we do not systematically include other current transfers (D7) in it.)

Primary Income Decomposition

Secondary Income Decomposition

Several components of each sector’s income must satisfy certain identities across the entire economy. They are presented in the tables below. For example, direct taxes paid by households and corporations must correspond to the total taxes collected by the government, etc.

Decomposition of Property Income between Sectors

Decomposition of Direct Taxes on Income and Wealth between Sectors

Decomposition of Social Security Contributions between Sectors

Decomposition of Social Security Benefits between Sectors

We define the saving of a given sector as the difference between its secondary income and its consumption. When summing the savings of the different sectors we arrive at the savings of the total economy.

Decomposition of Net Savings Between Sectors

Decomposition of Gross Savings Between Sectors

Primary income, secondary income and saving may be gross or net depending on whether we account for the consumption of fixed capital of each sector. If we sum the consumption of fixed capital across all sectors we arrive at the consumption of fixed capital of the entire economy.

Net and Gross Variables across Sectors

3.1.5. Labor and Capital Share of National Income

We decompose net national income into four components: compensation of employees (pure labor income), pure capital income (net of depreciation), net mixed income (income of self-employed) and production taxes.

We use this decomposition to define a “labor share” and a “capital share” of income. To estimate these shares, we use factor price national income at the denominator (national income minus taxes on products and production) and we consider that mixed income is 70% labor and 30% capital. By construction, our shares of labor and capital income sums up to 100%.

Note that the 70-30 split of mixed income is provided as a convenience, but that the proper treatment of mixed income depends on the research question. Whenever possible, we provide the decomposition of national income into four components: production taxes, pure capital income, pure labor income and mixed income.

Labor of Capital Income

3.2. Income of Households and NPISH

The UN SNA separately defines the income of households and non-profit institutions serving households (NPISH), but in practice many countries only report both sectors jointly. The WID therefore defines variables that relate to both sectors separately, and to the sectors combined.

The sequence of accounts is very similar for both sectors, so we describe it for the combined sector only. The only difference is that there is not item for “mixed income” in the accounts of NPISH.

3.2.1. Income of the Sectors Combined

3.2.1.1. Primary Incomes of Households and NPISH

The primary income is the income that accrues to households and NPISH before the operation of (direct) taxes and transfers. It is made up of the compensation of employees (which includes social security contributions), the property income (received minus paid), the operating surplus (i.e. rental income, including imputed rents) and the mixed income (income of the self-employed). In many countries, these two items are only reported jointly. Operating surplus and mixed income can be gross or net depending on whether consumption of fixed capital is accounted for.

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.1.2. Secondary Incomes of Households and NPISH

Secondary income corresponds to primary income, to which we remove direct taxes (both income or wealth taxes and social contributions) and to which we add all cash transfers.

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.1.3. Consumption and Savings of Households and NPISH

We estimate savings by removing consumption expenditures from the secondary income.

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.1.4. Relation between Net and Gross Variables

The various income/saving concepts may be considered gross or net of depreciation. Note that for the household sector, we can distinguish between the consumption of fixed capital attributable to mixed income, and the consumption of fixed capital attributable to operating surplus.

3.2.2. Income of Households

3.2.2.1. Primary Incomes of the Household Sector

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.2.2. Secondary Incomes of the Household Sector

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.2.3. Consumption and Savings of the Household Sector

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.2.4. Relation between Net and Gross Variables

3.2.3. Income of NPISH

3.2.3.1. Primary Incomes of NPISH

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.3.2. Secondary Incomes of NPISH

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.3.3. Consumption and Savings of NPISH

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.2.3.4. Relation between Net and Gross Variables

3.3. Income of the Corporate Sector

The UN SNA distinguishes financial from non-financial corporations in its guidelines. This distinction is not always needed, so we also provide data for both sectors combined. The sequence of accounts is identical for both sectors, so we only provide detailed explanation for the combined sectors.

3.3.1. Income the Sectors Combined

3.3.1.1. Primary Income of Corporations

The primary income of corporations corresponds to the income they have left from their sales after paying their intermediary consumptions, the taxes of productions, their employees, their creditors, and their shareholders. It can be decomposed into their operating surplus (the product of their sales after paying their employees, intermediary consumptions and taxes on productions) and their net property income (usually negative because corporations pay money to their shareholders in other sectors).

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.3.1.2. Secondary Income of Corporations

To move from the primary to the secondary income of corporations, we first remove the corporate tax. Then, we also add social contributions paid directly to private employer social insurance, and remove the social benefits they pay out to their employees.

Note that for corporations, their secondary income is equal to their net saving, because corporations have no consumption expenditure.

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.3.1.3. Relation between Net and Gross Variables

3.3.2. Income of Non-financial Corporations

3.3.2.1. Primary Income of Non-financial Corporations

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.3.2.2. Secondary Income of Non-financial Corporations

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.3.2.3. Relation between Net and Gross Variables

3.3.3. Income of Financial Corporations

3.3.3.1. Primary Income of Financial Corporations

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.3.3.2. Secondary Income of Financial Corporations

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.3.3.3. Relation between Net and Gross Variables

3.4. Income of the Government Sector

3.4.1. Primary Income of the Government

The primary income of the government sector is made up of three components. The most important is taxes less subsidies on production and imports. Then we have the net property income (received minus paid) of the government, which is usually negative because it is mostly made up of interests that the government pays on its debt. The last item, “operating surplus”, is often equal to zero by convention, because the UN SNA value the production of the government at its cost, so that it has zero profit. In practice, governments have some activities that operate within market context, and this output is valued at its price like the output of other sectors, and this can lead to a small operating surplus.

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.4.2. Secondary Income of the Government

The secondary income of the government all direct taxes received by the government (income and wealth taxes, social contributions, corporate tax), and removes all social benefits in cash paid to households. Transfers in nature, however, are considered consumption expenditures and only included in consumption and savings of the government.

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

3.4.3. Consumption and Savings of the Government

The consumption expenditures of the government (i.e. transfers in nature) are first divided between individual consumption expenditures (expenditures that benefit specific individuals, mainly health and education) and collective consumption expenditures (expenditures that benefit the community as a whole).

Net of Consumption of Fixed Capital

Gross of Consumption of Fixed Capital

A second, more precise decomposition of government spending separates the different functions of government expenditures.

Government Spending by Function

Government Expenditure by Function

Government Revenue

3.5 Current Account

3.5.1 Trade Balance

3.5.2 Capital Account

4. Distributed Income Variables

Distributed income variables relate both to a specific income concept and a specific part of the distribution. They can be used to retrieve the share, the average, or the threshold for various percentile groups. Please refer to the dedicated section for how to use percentile codes in the WID,  This section describes the five-letter codes that denote the various income concepts.

Note that every five-letter code is associated with two concepts: the income that is attributed to individuals and the income used to rank individuals (the ranking variable). These concepts are sometimes identical, but this is not always true. For example, we can request that individuals be ranked according to their total income (labor and capital) to define the top 1% and then request to know the capital income of that group. The tables below give both the concept and the ranking variable associated with each WID code.

4.1. “Fiscal” Income

“Fiscal” income is a legacy concept from the World Top Income Database (WTID), the precursor of the WID. While it is still included in the WID, it will be progressively superseded by DINA income concepts, which are better harmonized.

“Fiscal” income is close to the concept of taxable income, and as such its exact definition varies between countries or over time. It differs from taxable income only because of the removal of basic deductions or taxable minimums (see country-specific papers for details). Note that in most cases, fiscal income variables are only available for the top 10% of the distribution.

4.2. DINA Income

DINA income concepts are distributed income concepts that are consistent with national accounts aggregates. The precise definitions are outlined in the DINA guidelines and country-specific papers.

4.2.1. Pretax Income

Pretax income is our benchmark distributional income concept. It includes social insurance benefits (and removes corresponding contributions) but excludes other forms of redistribution (income tax, social assistance benefits, etc.)

4.2.2. Post-tax Income

Post-tax income measures the distribution of income after redistribution. Our benchmark concept, post-tax national income, includes both in-kind and in-cash redistribution. Because the distribution of in-kind transfers requires a lot of assumptions, we also provide estimates for post-tax disposable income, which excludes in-kind transfers.

4.2.3. Factor Income

Factor income measures income inequality before any redistribution (be it through social insurance or social assistance). We prefer to avoid this concept for international comparisons because, in general, the elderly population has near-zero facto income, so that factor income can be very sensitive to the population’s age structure.

5. Aggregate Wealth Variables

The WID provides complete balance sheets (financial and non-financial) for the national economy as a whole and the different sectors of the economy. A detailed description of the concepts is available in the DINA guidelines and specific papers.

5.1. National Economy

Our benchmark measure of net national wealth is market-value national wealth. That is, corporations are valued at their market values (their equity liability). An alternative definition is book-value national wealth, which values corporations like the sum of their assets (minus their non-equity liability).

5.2. Households and NPISH

The SNA distinguishes households from non-profit institutions serving households, but in some cases the two sectors are only reported jointly. The WID contains variables for each of these two sectors, separately and jointly. The general structure of the balance sheet is identical in all cases.

5.2.1. Combined Sector

5.2.1. Household Sector

5.2.2. NPISH

5.3. Corporate Sector

Corporate sector wealth (i.e., the sum of corporations’s assets minus their debt) is what we call the book value of corporations. This value can be different from the market value of corporations (i.e. their equity liability): the ratio between these two quantities is Tobin’s Q.

5.4. General Government Sector

By default, the net wealth of the government sector is unconsolidated, like in other sectors. However, we do provide consolidated government debt as an auxiliary variable.

6. Distributed Wealth Variables

The distributed wealth variables are identical to the aggregate wealth variables of the household sector.

7. Price Index, Exchange Rates, Populations, etc.

7.1. Price Index

The WID uses a single price index to deflate all series: the national income deflator. It usually corresponds to the GDP deflator, although we resort to the CPI when the deflator is unavailable. The price index is always anchored to the data’s base year, so it is equal to 1 for this base year.

7.2. Exchange Rates

The WID contains two types of currency conversion factors (PPPs and market exchange rates) for three currencies (US dollar, euro and yuan). We provide long-term series for both of these indicators, but note that to convert between currencies in our benchmark approach, you only need to use the last value of each series: see Thomas Blanchet, Prices and currency conversions in WID.world, WID.world Technical Note Series n°2017/3 for details.

PPP Conversion Factors

Market Exchange Rates

7.3. Population

The WID contains several variables that relate to population sizes. They are all associated with the one-letter code. Combined with the various five-letter codes for concepts, they form the six-letter codes described below. The main concept is npopul, which refers to the total individual population within a sex and age group, but we also provide variables related to tax units or employed population in some cases where this information is helpful to understand other (inequality) series.

7.4. Wealth/Income Ratios and Labor/Capital Shares

The WID provides the following variables for wealth/income ratios and the capital share. They are provided for convenience, and are easy to recalculate based on macro variables. For the labor and the capital share, also see section 3.1.5.

7.5. Inequality Transparency

WID.world produces a single inequality transparency index for each country. The index ranges from 0 to 20 and seeks to emphasize wealth and income equally. It is constructed as follows:

• Income survey tabulations (or consumption surveys) available to researchers for at least one year over the past 10 years add 1 point.
• Income survey microdata add up to 3 points.
• Wealth survey tabulations add up to 1 point.
• Wealth survey microdata add up to 3 points.
• Income tax tabulations covering labour income add up to 1 point.
• Income tax tabulations covering capital income add up to 1 point.
• Income tax microdata covering labour income add up to 2 point.
• Income tax microdata covering capital income add up to 2 point.
• Wealth tax or estate tax tabulations add up to 2 point.
• Wealth tax or estate tax microdata add up to 4 point.

8. Aggregate Carbon Variables

WID.world provides several variables related to carbon aggregates. It is constructed as follows:

8.1. National territorial carbon emissions

8.2. National imported carbon emissions

8.3. National exported carbon emissions

8.4. National net imports of carbon emissions

8.4. National carbon footprint

9. Distributed Carbon Variables

WID.world provides distributed carbon variables. It is constructed as follows: