# **1. A Brief Description of the Databases**

This special issue includes reviews of eight databases of summary inequality statistics. All contain summary statistics that describe national-level estimates of inequality in incomes or consumption expenditures in multiple countries over multiple years. As we have already suggested, however, the databases are far from identical. They vary greatly in methodology – to which we will return in greater detail in the next section – as well as in geographical coverage, purpose, and ambition.

Two of the databases focus primarily on high-income countries (LIS and IDD), although both have been expanding to include some middle-income nations. Another two cover Latin America and the Caribbean exclusively (CEPALSTAT and SEDLAC), while four are global in scope (ATG, PovcalNet, SWIID, and WIID). ATG, and SWIID are produced privately by individual researchers, while all the other data sets are institutional projects. Among the latter, LIS and SEDLAC are produced by institutions whose primary purpose is the harmonization, storage and analysis of microdata originally generated by others, whereas the IDD, WDI/PovcalNet, CEPALSTAT, and WIID are produced by large organizations with broader mandates, which also happen to produce and host these databases (the OECD, the World Bank, the UN’s Economic Commission for Latin America and the Caribbean – ECLAC – and the United Nations University’s World Institute for Development Economics Research, UNU-WIDER, respectively).

For our purposes, however, the most important distinction among the eight databases concerns the _source_ of the summary statistics they report. Some of the databases calculate inequality directly from microdata (i.e. the underlying household surveys or other unit-record data sets), while others are secondary compilations that amalgamate indicators from a variety of other sources. In one case, the inequality statistics reported in the database are actually imputed, on the basis of an algorithm that draws on underlying secondary data. In this Introduction, we therefore classify the data sets into three main groups: Group 1 includes data sets whose producers estimate the income inequality indicators they report directly from microdata. There are five such databases, namely CEPALSTAT, IDD, LIS, PovcalNet, and SEDLAC. Group 2 includes those that are secondary sources of inequality indicators: All the Ginis and WIID. Finally, group 3 includes the database generated by imputation methods (based on secondary source indicators of inequality) and is comprised of just one data set: SWIID. Let us briefly consider each of the three groups in turn.

# _Group 1: Microdata-based Data Sets_

Five of the eight databases reviewed in this special issue belong to this group: CEPALSTAT, IDD, LIS, PovcalNet, and SEDLAC. Table 1 displays their aggregated spatial coverage. The first two columns report the net and gross coverage by region of the world: net coverage indicates the number of country-year cells with at least one observation, whereas gross coverage is a simple count of observations from primary sources across all databases, _without_ netting out country-year repetitions. Columns 3 and 4 indicate the earliest and most recent observations for each region. There is a clear quantitative bias towards Latin America and the Caribbean, with 832 (gross) observations spanning 378 country/year cells. North America and Western Europe comes next, with 403 observations for 4