A number of databases containing summary inequality statistics for multiple countries over many years are now publicly available. These cross-national inequality databases are being used by researchers, with increasing frequency, to document global or regional trends (e.g. Atkinson and Bourguignon, 2014; Atkinson, 2015; Bourguignon, 2015; and Piketty, 2014), as well as by scholars interested in including inequality measures in cross-country regression analyses, either as dependent or independent variables (e.g. Acemoglu et al., 2015; and Ostry et al., 2014).

Yet, these different databases are often designed for different purposes, and are constructed in very different ways. They can therefore yield conflicting pictures of inequality, both in levels and in trends. In some cases, one database will include a full annual series for a given country, while another database of purportedly similar coverage will only have two data points over two decades. For example, whereas one global database (the World Income Inequality Database, WIID) includes 350 inequality observations for Sub-Saharan Africa between 1960 and 2006, of which only _five_ are labeled as high quality, another database of similar scope (the Standardized World Income Inequality Database, SWIID) includes 934 observations for that same region, between 1960 and 2012, and treats all of them as comparable in quality terms (see Jenkins, 2015). Although inequality time-series for a given country are typically closely correlated across different databases, even this rather weak consistency criterion cannot be taken for granted. Indeed, as we will see below, the pairwise correlation for Gini coefficients across different databases was lower than 0.5 in 43 out of the 254 cases (or 17%) for which such comparisons are reported (Table 4). Examples of _negative_ correlation coefficients could be found in all of the world’s regions.

It cannot therefore be true that each and every one of these databases is perfectly accurate. If significant discrepancies occur in one out of every six pairwise country comparisons across databases, users should carefully consider which database – if any – is most suitable for their particular purposes.<sup>1</sup> In order to assist users in these considerations, the _Journal of Economic Inequality_ has prepared a special issue entitled “Appraising Cross-National Income Inequality Databases”. The issue is devoted to an assessment of the merits and shortcomings of what we believe are the most frequently used databases. The aim is to expose these potentially valuable resources to critical scrutiny that identifies perils, pitfalls, strengths and weaknesses.

With this objective in mind, we invited a group of leading income distribution scholars to review eight data sets. The reviews are included in the five main articles that follow this Introduction. In all cases, the parties responsible for the production of each of the databases were offered the opportunity to comment on the reviews. Although most producers did send specific comments to authors, only three ultimately accepted the invitation to publish their comments in this issue.

> 1 This is not novel advice: Atkinson and Brandolini (2001) discuss the issues that arise from the use of secondary sources and the need to understand the differences in the underlying data and methods, in the context of the Deininger-Squire (1996) database, an important precursor of some of the compilations reviewed below.

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