we take away from our reading of the special issue, and from our own comparative analysis of the databases.

First, cross-national inequality databases are only as good as the underlying microdata sources on which they draw. One fundamental reason why the discrepancies between the summary statistics contained in LIS and IDD are considerably smaller than those between other databases lies in the decades of experience and considerable resources expended by National Statistical Offices in Europe and North America to improve both the accuracy and frequency of their primary data collection efforts. Conversely, the reason for considerable uncertainty about inequality trends and, occasionally, levels in Sub-Saharan Africa lie in the much patchier history of primary data collection in most countries in that region. Consequently, attention to cross-national compilations must not detract from a persistent focus on more and better data collection in country - and on strengthening the national institutions in charge of that collection.

Second, the world is a diverse place, and there is an inevitable trade-off between the coverage and comparability of inequality databases. When a database is focused on a narrower subset of countries that are relatively similar in terms of their economic structure and their household survey design, harmonization efforts require less draconian impositions of internal accuracy and consistency. It is no surprise, ex post, that the only two pairwise database comparisons in Table 4 that did not return country-level correlation coefficients below 0.5 were between IDD and LIS, and between CEPALSTAT and SEDLAC. One consequence for users is that, ceteris paribus, they should choose the database with the narrowest geographical coverage consistent with their research question.

Even when using data sets with the narrowest geographical coverage, the user may prefer to rely on those which make the least number of undocumented adjustments. For example, between CEPALSTAT and SEDLAC, the user may prefer to use SEDLAC until CEPALSTAT carefully documents how the adjustment for underreporting each income component is done for each country (that is, report each adjustment coefficient), which, at the moment of writing, it did not.<sup>14</sup> Third, secondary-source databases are subject to the same caveats and comparability concerns as microdata-based sources, plus some more. Examples of additional concerns affecting secondarysource databases include concerns about double-counting entries from different primary sources, as well as different decisions made by primary sources with respect to the choice of income concept, equivalization, treatment of outliers, etc. So, where there is a choice, we would recommend microdatabased compilations over secondary-source databases.

Fourth, imputation comes with both promises and pitfalls. Analysts of income distributions are no strangers to imputation practices. Missing items in consumption or income questionnaires are often imputed, rather than being treated as zeros. Occasionally total incomes for missing observations in

> 14 CEPALSTAT is in the process of modifying its methods of calculating inequality indicators and it might stop doing the adjustments for underreporting altogether.

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