Second is the _World Top Incomes Database_ (WTID) produced by Facundo Alvaredo, Anthony Atkinson, Thomas Piketty and Emmanuel Saez. Unlike the other data sets, WTID uses information from tax returns (mainly) to estimate the share of income earned by certain groups at the top of the distribution, such as the richest 1% of the population (Alvaredo et al., 2013). It does not always include holistic inequality metrics such as the Gini coefficient. However, a number of such measures, including the Gini coefficient, were produced by researchers working on the individual country studies that fed into WTID. Jäntti, Riihelä, Sullström, and Tuomala, for example, produce a Gini series for Finland, along with a series of top income shares (Atkinson and Piketty, 2010).

Third, while all of the data sets reviewed in this issue compile measures of inequality for household income or consumption (whether per capita or equivalized), the University of Texas Inequality Project (UTIP) is focused directly on individual earnings inequality. The project computes inequality statistics, chiefly Theil’s T statistic, from industrial, regional, and sectoral data. It is a rich, interesting and frequently used data set, but its focus on earnings also makes it substantially different from the other databases reviewed here.

We also note the recent development of two new sources of cross-national inequality indicators. The GINI project produced 94 discussion papers and 26 country reports on the drivers and impacts of inequality, along with a new 30-country data set (see _GINI Growing Inequalities' Impacts_ , 2015). This secondary-source data set focuses on disposable income inequality (and relative poverty) for use in within-country analysis and analysis that compares trends, but not levels, between countries. The primary sources were chosen by country experts to maximize coverage and comparability within countries and include the European Union’s Survey of Incomes and Living Conditions (EU-SILC), LIS, the output of National Statistical Offices, and selected publications by Thompson and Smeeding, Atkinson and Micklewright, Milanovic, and Transmonee. The data set was released in 2014 and has not yet been widely used beyond GINI Project discussion papers.

The Global Consumption and Income Project (GCIP) was introduced in 2014, although its data set is not yet publicly available.<sup>7</sup> GCIP uses secondary sources to estimate monthly real consumption and income levels and shares by decile. Like the SWIID, GCIP imputes indicators for years in which survey data is unavailable (although the methodologies are different). However, unlike the secondary source and imputation-based data sets reviewed in this issue, GCIP is not concerned only with the distribution of income (and consumption), but also with levels (Lahouti et al., 2014).

# **2. Differences in Metrics and Methodology**

To compare the metrics and methodologies of the databases reviewed in this special issue, we summarize some of their main features in Table 2. Panel A of Table 2 refers to the Group 1 data sets,

> 7As of August, 2015 GCIP data was unavailable, but presentation and summary statistics were available online. (www.globalconsumptionandincomeproject.org )

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