standardized _microdata_ is the priority, and the ability to construct comparable indicators is merely a consequence. In the cases of CEPALSTAT and WDI/ PovcalNet, on the other hand, the microdata is standardized only insofar as is necessary to create comparable indicators.

The databases that vary their methodology by country, do so for very different reasons. For PovcalNet, broad coverage is a necessity, arising from the World Bank’s global mandate. Indicators from PovcalNet are classified as being derived from four categories of data: grouped consumption, grouped income, unit-record income, and unit record consumption. When unit level data is available, the World Bank carries out some minimal standardization of the microdata.<sup>8</sup> However, in an effort to expand coverage, they also calculate indicators from less-standardized grouped data. As a result, users have the choice between a smaller data set derived from standardized microdata that is more comparable, and a less standardized data set with broader coverage. This trade-off between comparability and coverage is a central feature of the exercise of compiling cross-national inequality databases, and we return to it below.

The statistics in CEPALSTAT and IDD are officially endorsed by the national statistical offices. Additionally, the choice to leave more responsibility with the national statistical offices implies that more work is being done by the people who are most familiar with the underlying data. The OECD’s methodological choices prioritize these two gains at the expense of greater standardization at the microdata level.

# _Group 2: Secondary Source Data Sets_

Generally, the same set of questions (as in Panel A of Table 2) cannot be asked directly of secondarysource databases. Instead, Panel B reports on questions about the criteria and procedures used to select (and rate or rank) their _inputs,_ and whether sufficient documentation is provided for those questions to be answered with respect to the original, primary sources.

Secondary source data sets are of two types: (1) aggregation and categorization, as in WIID and (2) choice by precedence, as in _AlltheGinis_ . In both cases, the database producers need to assess the quality and comparability of their sources. After this assessment, however, WIID’s approach is to provide this information to users with a set of variables categorizing the inequality statistics by income concept, equivalence scale, and quality. The onus is then on the users to determine which of the many data points provided should be used in their research. _AlltheGinis_ , on the other hand, makes this decision for you, providing the users with the best available estimate of inequality in a given country and year. Milanovic uses the term “choice by precedence” to indicate that the GiniAll variable is chosen according to his ranking of source reliability. Fortunately, both data sets do publish the criteria through which classifications and, in the case of _AlltheGinis_ , choices of precedence, are made.

> 8 But standardization of household survey data at the World Bank is carried out separately for each macroregion of the world (e.g. Sub-Saharan Africa, South Asia, Latin America, etc.), and procedures differ substantially among them.

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