North America, Europe and Latin America in more recent decades. Conversely, it is likely to be least reliable where actual data is most scarce, and hence constancy assumptions have to be made for countries, periods and concepts where stability is least defensible.

Both secondary-source and imputation-based data sets embody not only their own assumptions but also those of the primary sources they use as inputs. Interestingly, while the methodological frameworks for WIID, All the Ginis, and SWIID are different from one another, they rely on much of the same primary source data. Table 3 shows the overlapping nature of the sources of the secondary data sets. Inequality indicators from LIS Key Figures, SEDLAC, and the World Development Indicators are inputs to all three databases. WIID and All the Ginis additionally include data from Deininger and Squire (1996), and a number of other inequality studies. ATG then absorbs all WIID data. Finally, SWIID uses both WIID and ATG.

How a secondary-source data set accounts for, prevents, or documents double-counting is a concern. From the publicly provided documentation alone, we were unable to determine how either ATG or SWIID address this issue. If one were to look only at ATG’s choice by precedence series, Gini All, this would not be an issue. But for a researcher looking at all of the data in ATG, it is unclear whether the observations attributed to WIID are based on statistics already previously included in the database. Similarly, a researcher would need to compare SWIID’s replication files to those obtained from the original source (or, as we did, contact the author directly) to determine that observations are removed to prevent double counting.

# _An alternative taxonomy_

Our three-group classification, based on the source of the inequality statistics, is clearly not the only way in which these eight databases could be classified. Atkinson et al. (2010) discuss a hierarchy of methodologies employed in the standardization of income inequality data sets. “In short,” they write, “we have a ‘hierarchy’ of degrees of standardization: 1. Common survey instrument (European Community Household Panel, ECHP); 2. Ex-ante harmonized framework (EU-SILC); 3. Ex-post standardized microdata (LIS); 4. Ex-post customized results (OECD); 5. Meta-analyses of results (Kuznets)” (p. 103).

While this hierarchy places higher levels of standardization at the top, the authors note that a common survey instrument does not necessarily imply the best results. Rather, different sources of data may be better suited for some purposes, and worse for others – as in the aforementioned discussion of consumption data collected in a poor developing country, vis-à-vis income data in a rich country. It is unlikely that a questionnaire aimed at collecting the best possible information to accurately assess living standards in Malawi would look much like one designed with the same objective, but for Switzerland. One can also think of Atkinson et al.’s hierarchy as a spectrum ranging from a methodology that is internally consistent by construction, but for which broad coverage is difficult, to an approach that is designed for broad coverage but for which internal inconsistencies are, in practice, inevitable. The tension reflects the trade-off between coverage and comparability to which we have already alluded.

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