316 country/year cells. Eastern Europe and Central Asia is a close third, with 334 observations for 301 country/year cells. The number of observations for East Asia and the Pacific, South Asia, Middle East and North Africa, and Sub-Saharan Africa is much smaller. The bias toward Latin American is driven, at least in part, by our choice of which databases to review in this issue. We hope, however, that our choices accurately reflect the use of inequality databases by practitioners.

It is interesting to note that four of these five microdata-based data sets reported in Table 1 have a purposefully limited geographical scope: two focus exclusively on Latin America and the Caribbean (CEPALSTAT and SEDLAC), and another two (mainly) on advanced countries (LIS and IDD).<sup>2</sup> Only one of the six microdata-based data sets is global in coverage: namely PovcalNet (which feeds into WDI). Conversely, of the five data sets with global coverage, the World Bank’s is the only one that generates inequality estimates directly from countries’ household (income and/or expenditure) surveys. In other words, of the eight data sets reviewed in this special issue, only the World Bank’s PovcalNet is global in scope _and_ derives its summary statistics (primarily) from microdata: In December 2014, WDI/ PovcalNet reported Gini coefficients, Theil indices, and shares of income by decile for 161 countries.

However, it is important to note that the reliance of PovcalNet on household survey microdata is sometimes indirect. Full microdata was contained in the PovcalNet database for 52 of the 126 developing countries in the 2014 release, while the distributions used for the other 74 countries were based on grouped data (at the decile, ventile and percentile levels), which was in turn obtained either from microdata available in other World Bank servers, or from national statistical offices. In the most recent update (2015) of PovcalNet, the distributions are based on full microdata for 126 of the 132 developing countries included. Grouped data is now used for only six developing countries, but these still include China. In addition, the inclusion of high-income countries in PovcalNet is both recent and incomplete. Finally, in a good example of the coverage-comparability trade-off, PovcalNet makes virtually no effort to harmonize data across countries: it contains estimates based on both income and consumption expenditure distributions and, within those categories, on income and consumption aggregates that are widely disparate.

The World Development Indicators (WDI) is a large database of country-level statistics covering a wide variety of topics, from investment rates and road density to carbon emission levels. Poverty and inequality statistics are a relatively small subset of the WDI, and it is almost entirely derived from PovcalNet, a separate online tool focusing on these topics, and featuring additional capabilities that are not present in WDI.<sup>3</sup> It is only this sub-component of the WDI which is reviewed below (Smeeding and Latner, 2015).

> 2 As noted, LIS has recently expanded into middle-income countries, through the associated database Luxembourg Middle-Income Countries Study (LMICS).

> 3 A salient feature of PovcalNet is its interactive software that allows users, for example, to generate poverty rates for any country, for any chosen poverty line.

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